Firms’ Response to Macroeconomic Estimation Errors

Oliver Binz Duke University

oliver.binz@duke.edu

William J. Mayew Duke University

william.mayew@duke.edu

Suresh Nallareddy Duke University

suresh.nallareddy@duke.edu

March 2019

We would like to thank Manuel Adelino, Ravi Bansal, Jeremy Bertomeu, Francesco Bianchi, Qi Chen, Anna Cieslak, Scott Dyreng, Cam Harvey, David Hsieh, Xu Jiang, Cosmin Ilut, Maria Ogneva, Adriano Rampini, Katherine Schipper, Matias Sokolowski, Rahul Vashishtha, Mohan Venkatachalam, Vish Viswanathan, Aytekin Ertan and seminar participants at Duke University Accounting Department, Duke University Economics Department, Duke University Finance Department, Southern Methodist University, 2018 Southeast Summer Accounting Research Conference at University of Georgia, and Transatlantic Doctoral Conference at London Business School for helpful comments.

Firms’ Response to Macroeconomic Estimation Errors

Abstract Initial Gross Domestic Product (GDP) announcements are important economic signals that convey information on the state of the economy but contain substantial estimation error. We investigate how GDP estimation errors affect firms’ real decisions and profitability. Consistent with theoretical predictions from the literature on macroeconomic signal errors, we find that GDP estimation errors are positively associated with one-quarter-ahead changes in firms’ capital investments, production, inventory and corporate profitability. Stronger sensitivities to GDP signal errors are observed for more cyclical firms relative to less-cyclical firms. We observe some reversal in future quarters’ corporate profits for cyclical firms as a result of GDP estimation errors, consistent with over (under) production being met with declines (increases) in future profitability.

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Firms’ Response to Macroeconomic Estimation Errors

1. Introduction

We investigate how firms respond to estimation errors in initial GDP announcements. Our

investigation is motivated by both the importance of GDP announcements as a signal of the state

of the economy and the status of the literature regarding the macroeconomic response to estimation

errors. GDP announcements are highly scrutinized by policy makers, investors, capital market

participants, and the business press. However, initial GDP estimates are known to contain

estimation error, and the amount of misestimation can be calculated by observing subsequent

restatements of the initial GDP estimate by the Bureau of Economic Analysis (BEA).

The macroeconomics literature has exploited the BEA restatement process to decompose

the intial macroeconomic announcement into a component that proxies for the ‘true’ state of the

economy and the remainder which captures estimation error. The seminal work of Oh and

Waldman (1990) provides empirical evidence that the economy overall responds to the error

component of the signal, where higher estimation error is associated with higher subsequent

overall economic activity. This finding is consistent with a large theoretical literature suggesting

that higher positive measurement error in public signals can lead to higher subsequent economic

activity and vice versa (Lorenzoni 2009; Jaimovich and Rebelo 2009; Oh and Waldman 1990).

The central notion underpinning these theories is that agents cannot sufficiently filter out the error

component of the signal. In turn, signal errors serve as expectation shocks that can in some cases

be exacerbated by strategic complementarities (Beaudry and Portier 2007; Morris and Shin 2002;

Angeletos and Pavan 2004).

Assessing how firms respond to macroeconomic signal errors is important for two reasons.

First, it is unclear whether firms would respond to macroeconomic signal errors in the same way

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that is observed for the economy overall. Public corporations represent a specific class of economic

agents with rich information environments, resulting from internal collection of information or

from external sources (e.g., financial analysts, stock market, other firm disclosures), which can

facilitate macroeconomic signal error filtering. For instance, investors who trade in the shares of

public companies appear to at least partially filter out the error component of the signal (Gilbert,

2011). Therefore, a key theoretical assumption that agents cannot filter out the true and error

component of the initial macro announcement may not apply to public firms. Second, the

macroeconomic literature shows that estimation errors are positively related to subsequent

aggregate economic and investment growth but are not significantly related to subsequent

consumption growth (Mora and Schulstad, 2007). This finding suggests the possibility of a supply

and demand imbalance at the firm level. That is, if a favorable estimation error increases the supply

of goods, but such goods are not consumed, the economy may be oversupplied, and negatively

impact firm profitability. Despite this possiblity, we know of no existing firm-level analysis on

firms’ response to macroeconomic estimation errors.

We begin our investigation by confirming the insights from the macroeconomic literature

in our extended sample spanning 1972 – 2016. We decompose initial GDP announcements into

their true and error components and show that both components are positively associated with

subsequent quarter real GDP. This confirms the insights from Oh and Waldman (1990). We then

examine how GDP estimation errors relate to both aggregate investment and consumption, and

confirm the inference in Mora and Schulstad (2007) that GDP estimation errors are positively

associated with investment but not associated with consumption.1

1 We find that GDP errors are unrelated to other components of the GDP such as growth in government spending and net exports.

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We then turn to the firm level analysis to investigate whether firms respond to GDP

estimation errors. Given the investment response in the economy overall, we begin by assessing

the impact of GDP estimation errors on quarterly capital expenditures. We find that positive

(negative) errors in initial GDP estimates relate to higher (lower) subsequent firm investment.

Specifically, multivariate regression analysis including controls and the true GDP estimate reveals

a positive association between GDP estimation error and quarter-ahead seasonally adjusted capital

expenditures. Because budgets for capital expenditures are set annually, the degree to which firms

can respond on a quarterly basis to macroeconomic signals is potentially limited. Firms can

arguably respond by adjusting production levels from assets currently in place. To test this

conjecture, we also investigate firms’ production response to GDP error and find that production

levels are increasing in GDP estimation errors. Increased production not matched with

corresponding increased consumption should result in inventory buildup, and we also find

inventory is increasing in GDP esimtation errors. In terms of firm profitability, we find that one-

qauarter-ahead profitability is also increasing in GDP estimation errors, suggesting at least part of

the inceased production is passed onto consumers.

We conduct cross-sectional tests to strengthen our inference that GDP estimation errors are

positively related to subsequent firm profitability. Cyclical firms are more exposed to

macroeconomic fluctuations and their decisions are more dependent on expectations about the

state of the economy. As a result, the profitability of such firms should be more strongly related to

GDP estimation errors. To test this prediction, we employ three distinct measures of firm

cyclicality. First, partial irreversibility paired with uncertainty causes investment to be pro-cyclical

and volatile (Bernanke, 1983; Bloom, 2009; Bloom et al. 2018). This results in cyclical

fluctuations in the demand for investment goods producing firms, so called “make firms,” which

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sell predominantly to other firms, relative to the demand of firms selling predominantly to

households, so called “use firms.” Therefore, make firms should be more sensitive to GDP errors.

Second, firms in central industries – industries which are strongly connected with the overall

economy – are more sensitive to macroeconomic fluctuations (Ahern and Harford 2014; Aobdia

et al. 2014; Ogneva and Xia 2017). Hence, these firms should respond more to GDP estimation

errors. Lastly, larger firms are more exposed to macroeconomic fluctuations as they have broader

supplier and customer networks. Thus, larger firms should respond more to GDP estimation errors.

We find evidence consistent each of these predictions, suggesting cyclical firms’ profitability

reacts more positively to GDP estimation errors relative to less cyclical firms.

Collectively, our evidence suggests that firms respond to the error component of initial

GDP announcements, with investment, production and earnings increasing in GDP estimation

errors. That earnings ultimately increase (decrease) when GDP estimation errors are positive

(negative) suggests that firms are investing, producing, and selling in response to latent increases

(decreases) in consumer demand. This finding is puzzling given that, at the macro level, investment

is increasing in GDP estimation errors, but consumption is not. To solve this puzzle, we revisit

profitability responses to GDP estimation errors by make firms, who sell predominantly business-

to-business, and non-make firms that sell predominantly to householders. Sales by the make firms

are classified as investments at the aggregate level (BEA, 2009).2 Therefore, even if the household

consumption is not significantly related to GDP estimation errors, profitability of make firms could

nonetheless increase if they sell to other firms in the economy who are themselves responding to

positive GDP estimation errors when investing. We find that make firm responses to GDP

estimation errors are more sensitive and longer lasting relative to the non-make firms.

2 At the economy level, households spending, i.e., consumption, comprises two-thirds and business spending, i.e., investment, comprises one-third of aggregate spending (Bureau of Economic Analysis 2017).

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Make firms respond to GDP estimation errors by investing, producing, and selling more

than non-make firms. However, if non-make firms are not able to sell to the households then the

profitability effects we document in the short-run should be reversed at least partially in the long-

run. To test this conjecture, we investigate the long-run responses to the GDP estimation errors.

We find that in the future quarters, profitability of the make firms reverses partially. In contrast,

we do not observe such reversals in the profitability for non-make firms. We evaluate whether

these micro-level results hold at the macro level by estimating long-run responses of GDP and

investment to the GDP estimation errors. As in the firm-level analysis, we find that at the economy

level, GDP and investment also partly reverse in the future quarters.

Our paper makes several contributions. We are, to the best of our knowledge, the first to

examine firm level effects of errors in macroeconomic announcements. In doing so, we contribute

to the literature on the role of GDP estimation errors in the economy by documenting effects by

public firms, as one particular class of economic agents. We further extend the literature by

documenting that the initial responses to the GDP estimation errors reverse partly in future quarters

even at the economy-level. We also contribute to the literature interested in understanding how

firm investment and profitability is influenced by macroeconomic factors. Further, the current

literature predominatly investigates whether firm-level disclosures convey macroeconomic

information (e.g., Bonsall et al. 2013). We extend this literature by documenting how

macroeconomic information affects firm-level decisions. Finally, our paper adds to the literature

interested in understanding when macro and micro level phenomenon are congruent or incongruent

(Ball and Sadka, 2015; Kothari et al. 2006).

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2. Related Literature and Hypothesis Development

2.1. GDP Announcements, GDP Restatements, and GDP Errors

GDP announcements help describe the state of the economy and are heavily scrutinized by

a wide audience including policy makers, regulators, investment professionals, the media, and

politicians. The Bureau of Economic Analysis (BEA) estimates and releases initial GDP estimates

for a given calendar quarter three weeks after quarter end. However, the initial GDP estimate is

based on imprecise and preliminary information with only 45 percent of the information included

in this initial estimate coming from full 3-month survey data and the remaining 55 percent coming

from extrapolated information and trend data derived using various indicators (Grimm and

Weadock, 2006). As a result, initial GDP estimates contain substantial measurement error.

Subsequent to the initial GDP announcement, the BEA collects additional data to improve

the initial GDP estimate. Specifically, BEA accumulates more source data from additional large

sample annual surveys, late responders to the initial surveys, corporate tax filings from the Internal

Revenue Service, and other relevant data. Based on this subsequent information, BEA restates the

GDP estimates (Grimm and Weadock, 2006; Landefeld, Seskin, and Fraumeni 2008). Further,

BEA continues the revision process of the initial GDP estimate by restating all GDP figures every

five years to reflect any new information collected from mandatory census surveys covering more

than seven million business units (Landefeld et al., 2008).3

The revision process ultimately allows researchers to approximate a period’s ‘true’ GDP,

in turn allowing for the identification of error in the initial GDP forecast as follows:

Intial_GDPt = GDPt + Errort (1)

3 These five-year benchmark restatements may also include changes to BEA’s methodology (Croushore, 2011).

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In equation (1), Intial_GDPt is the initial real GDP estimate for calendar quarter t, which is publicly

released 3-weeks after the end of quarter t. GDPt is the most recently updated real GDP estimate

for quarter t available to the reseacher and serves as the proxy the true GDP in quarter t. Errort is

the estimation error in the initial real GDP figure for quarter t as illustrated in Figure 1.

2.2 Implications of Macroeconomic Estimation Errors

Empirical research has used the decomposition in equation (1) to study whether future

economic activity is related to both components of initial macroeconomic signals. The seminal

work of Oh and Waldman (1990) shows that future economic activity in the United States is

positively associated with both the true and, more importantly, the error component of an initial

macroeconomic signal. This latter finding is consistent with the large theoretical literature arguing

that errors in public macroeconomic signals can affect future macroeconomic activity (Lorenzoni

2009; Beaudry and Portier, 2007; Jaimovich and Rebelo, 2009; Cooper and John, 1988; Morris

and Shin 2002; Angeletos and Pavan, 2004). This literature forwards two non-mutually-exclusive

theoretical mechanisms, both based upon how signal errors can influence expectations about future

economic activity. The first is the notion that macroeconmic estimation errors serve as

“expectation shocks,” which operate like demand shocks. Lorenzoni (2009) forwards a model in

which economic agents know their own condition while having only imperfect information about

the overall economy’s state. Subsequently, agents receive a noisy public signal about the overall

state of the economy, but cannot sufficiently filter out the error component. The model reveals that

positive (negative) signal errors can lead to increases (decreases) in output and employment, as the

agents increase (decrease) their beliefs about the economy’s productive capacity and consequently

increase (decrease) their production.

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The second notion is based on strategic complementarities, where agents form expectations

based upon their beliefs about the expectations of other agents in the economy (Cooper and John,

1988; Morris and Shin 2002). Agents again cannot sufficiently filter out the error in a public signal,

and in the case of strategic complementarities in production, all agents produce more when public

signals indicate future growth and vice versa. The intuition is that agents care about what other

agents will produce, and if all agents revise their expectations upward (downward) at the same

time, production increases (decreases) despite the absence of any real change in economic

fundamentals. In such a setting, positive (negative) errors in public signals increase (decrease)

incentives to produce more, driving a positive empirical association between macroeconomic

signal errors and future economic activity.

Changes in expectations, under either explanation, underpin the empirical association

between macroeconomic signal errors and subsequent economic activity documented in Oh and

Waldman (1990). Oh and Waldman (2005) confirm this underpinning by showing that initial

forecast errors in the National Bureau of Economic Research’s (NBER) index of leading indicators

affects macroeconomists’ expectations of future economic activity. Other researchers have also

examined the Oh and Waldman (1990) findings in various ways. Orphanides (2001) extends the

findings to an alternative outcome by documenting that monetary policy prescriptions are sensitive

to GDP estimation errors. Kaukoranta (2010) extends the findings to other countries outside of the

United States, showing that errors in initial aggregate production and investment signals influence

future production and investment in Finland, the United Kingdom, and Sweden.4

4A recent empirical literature in economics employs structural VAR models to identify the effect of news and shocks on subsequent economic activity (e.g., Beaudry and Portier, 2006; Barsky and Sims, 2011; Barsky and Sims, 2012; Blanchard, L’Huillier, and Lorenzoni, 2013). The findings in this literature are mixed, with conclusions being sensitive to the specific structural model being estimated. To overcome these limitations, we employ an identification strategy based on the GDP restatement process.

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Mora and Schulstad (2007) replicate the Oh and Waldman (1990) result using Gross

National Product (GNP) and then investigate which major component of GNP is most responsive

to the GNP signal error by individually examining consumption, investment and government

spending. They find that GNP estimation errors are associated with future investment, but are not

related to next period’s consumption and government spending.

In sum, these empirical studies suggest that economic agents, in aggregate, respond to

errors in macroeconomic signals. What is lacking in the literature is how specific agents in the

economy respond to macroeconomic estimation errors. In this paper, we examine how public firms

respond to errors in macroeconomic signals. We focus specifically on public corporations as

economic agents of interest for three reasons. First, as a practical matter, public companies in the

United States must produce and file financial statements on a quarterly basis in compliance with

securities laws. Financial statements enable the researcher to directly measure firm responses such

as investment and production, which is not possible for other economic agents like individual

consumers.

Second, applying the insights from the macroeconomic empirical literature to public firms

suggests the potential for production errors. If firms, as producers, respond to positive

macroeconomic estimation errors but consumers do not (Mora and Schulstand, 2007), such

investment may result in excess production capacity and/or overproduction relative to demand. In

such a case, future firm profitability could be affected by macroeconomic estimation errors.5

5 This implies that macroeconomic signals may help improve the understanding of firm level profitability. Such an argument has been forwarded in the literature (e.g., Konchitchki, 2011; Li, Richardson, and Tuna, 2014; Carabias, 2017), however the consideration of the error component of economic signals has not been addressed. Our analysis also complements the voluminous literature in accounting that essentially does the reverse by showing how firm-level earnings information can be used to improve the accuracy of macroeconomic forecasts or understand the state of the economy better (e.g. Kothari, Lewellen, and Warner (2006); Anilowski, Feng, and Skinner (2007); Shivakumar (2007, 2010); Sadka and Sadka (2009); Cready and Gurun (2010); Jorgensen, Li, and Sadka (2012); Konchitchki and Patatoukas (2014a); Konchitchki and Patatoukas (2014b); Nallareddy and Ogneva (2016); Hann, Ogneva, and Sapriza (2012); Choi, Kalay, and Sadka (2012); Gallo, Hann, and Li (2013); Ogneva (2013); Shivakumar and Urcan (2017),

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Third, it is unclear whether it is appropriate to extrapolate inferences from the

macroeconomic signal error literature to the firm level, given the numerous settings whereby

macro level phenomena are not mimicked at the micro level (Ball and Sadka, 2015). In the case of

macroeconomic signal errors, a key theoretical assumption driving the error response is that agents

cannot sufficiently filter out the error component of the macroeconomic signal. Public companies

may, in fact, be well suited to filter errors from macroeconomic signals, given the rich information

environment they operate in. This richness stems from both the internal collection of information

that pertains to the macroeconomy within a given firm and the availability of other sources of

macroeconomic information from sources outside the firm.

Regarding the former, research suggests that a firm’s own earnings forecasts can provide

information about the macroeconomy (Bonsall, Bozanic, and Fischer, 2013). Regarding the latter,

financial sell-side analysts who follow public companies and forecast firm fundamentals also

provide insights on the macro economy (Hugon et al., 2016). These sell-side analysts provide

reports to inform investors who trade in the shares of public firms (Huang, Zang, and Zheng 2014),

and research suggests that investors appear to understand, at least partially, the difference between

the true and error components of initial macroeconomic signals (Gilbert 2011). Managers even

apepar to use stock price itself as an economic signal to guide investment decisions (Luo, 2005;

Chen, Goldstein, and Jiang 2006).

The combination of such outside sources of information with any internally produced

information may enable managers to filter out the error component of macroeconomic signals. In

such a case, we may not observe the macro level association between macroeconomic signal errors

Kalay, Nallareddy, and Sadka (2017), Kim, Hand, Schonberger, and Wasley (2017), Khan and Ozel (2016), and Abdalla and Carabias, (2017)).

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and investment at the public firm level. We, therefore, test the following hypothesis, stated in null

form:

H1: Firm investment is not associated with initial GDP estimation errors.

3. Data and Empirical Analysis

3.1. Data

Our sample combines quarterly macroeconomic and firm-level data from 1972– 2016. We

obtain quarterly macroeconomic data from the Real-Time Data Set for Macroeconomists provided

by the Federal Reserve Bank of Philadelphia. All variables are defined in the Appendix. Panel A

of Table 1 presents descriptive statistics for all macroeconomic variables that are used in our

analysis. Our main variable of interest is the quarterly GDP estimation error (Errort) – measured

as initial GDP estimate minus the final GDP estimate as of the vintage date December 15th, 2016.

The mean (median) estimation error is -0.41 (-0.34), implying that on average the initial GDP

estimate is lower than the true GDP that will be subsequently revealed through upwards revisions

by the BEA. These findings are consistent with the prior literature that documents that GDP

estimation error is not mean zero (Aruoba, 2008; Nallareddy and Ogneva, 2016). This prior

literature also documents that GDP estimation errors can be both positive and negative and are

highly variable over time. These findings hold in our sample as well, as shown in Figure 2 where

we plot GDP estimation errors over time. Figure 2 reveals little autocorrelation in GDP estimation

errors, with an autocorrelation coefficient of -0.03, which is statistically insignificant.

For our firm-level analysis, we investigate investment responses to GDP estimation errors

and ultimately consider the potential impact on firm profitability. Standard determinants of firm

investment require a measure of cash flows and standard determinants of profitability require a

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measure of accruals. We, therefore, begin our sample in 1988 to coincide with the mandatory

adoption of the statement of cash flows, which facilitates imputation of cash flows and accruals

while avoiding measurement errors that plague the balance sheet approach (Hribar and Collins,

2001). 6 All firm-level variables are defined in the Appendix. All continuous variables are

winsorized at the 1st and 99th percentiles. Further, we require a firm to have a minimum of 30

quarterly observations to be included in the sample. Panel B of the Table 1 presents the firm-level

descriptive statistics. We observe positive mean values for capital expenditures, production,

inventory and earnings in our sample.

3.2. GDP Error and Macroeconomic Conditions

In this section, we investigate how GDP estimation errors are related to different

macroeconomic conditions given as could affect the GDP estimation process. For example,

business cycle transitions or periods with high macroeconomic and policy uncertainty could make

the estimating true GDP more difficult, which would result in larger errors. Difficulty in estimation

conceptually implies the potential for GDP errors that result from both overestimation and

underestimation of true GDP. However it is unclear empirically whether certain macroeconomic

conditions tend to result in systematically positive or negative estimation errors.

3.2.1 Univariate Evidence

We begin by studying business cycles. Figure 2 plots GDP estimation errors over business

cycles, with grey shaded area representing recessionary periods. The plot does not suggest any

obvious relationship between recessions and GDP estimation errors, with both over and under-

estimation occurring during recessions. To more formally examine this issue, in Panel A of Table

6 As a robustness check, we redo all our firm-level analysis using the balance sheet approach to estimate the accruals and cash flows when cash flow statement data is unavailable (periods 1972-1987) and measures estimated from cash flow statement from 1988-2016. Our inferences are robust to including the sample using the balance sheet approach.

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2 we compare the average GDP estimation error in periods of recession and expansion. We find

the average GDP estimation error is -0.38 in recession and -0.57 during expansion, but the

difference is not statistically significant (t = -0.44). To further investigate the relation between

GDP estimation errors in the quarters with business cycle transitions, we also investigate GDP

estimation errors in quarters where the economy is and is not transitioning from recession to

expansions. The average GDP estimation error in the transitionary periods is both statistically and

economically indistinguishable from the overall sample mean of -0.41. However, since only six

observations comprise the transition condition, the power of the statistical test is unreasonably low.

To overcome this limitation, we consider additional partitions. First, we investigate GDP

estimation errors in periods with low and high consumer sentiment periods as measured by the

University of Michigan Consumer Sentiment Index.7 We classify quarters with above (below)

median consumer sentiment as high (low) sentiment periods. We do not find that GDP errors are

statistically different across sentiments periods (t = -0.27).

Second, we estimate the GDP estimation errors in periods with high and low economic

uncertainty periods. We employ Baker et al.’s (2016) Economic Policy Uncertainty Index to proxy

for the economic uncertainty. 8 This data is available starting 1985. We classify quarters with

above (below) median Economic Policy Uncertainty Index as high (low) economic uncertainty

periods. We continue to observe no difference in GDP estimation errors across conditions (t =

0.17).

Lastly, as discussed in Section 2, BEA’s initial GDP estimates draw heavily from survey

data. As such, the quality of the initial GDP estimate could be related to response rates as higher

response rates are likely to be representative of the sample population and also less likely to have

7 We download University of Michigan Consumer Sentiment Index data from http://www.sca.isr.umich.edu. 8 We obtain Economic Policy Uncertainty Index data from http://www.policyuncertainty.com.

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nonresponse bias (Groves and Peytcheva, 2008). We test this conjecture by examining mean Errort

during periods of high and low response rates to two major surveys conducted by the Bureau of

Labor Statistics’ (BLS), the Current Employment Statistics and the Employment Cost Index

surveys. Both surveys are used in the GDP estimation process (Landefeld, Seskin, Fraumeni

2008).9 Survey response rate data is available from Q1:2008—Q3:2016. Interestingly, survey

response rates vary little over time. In particular, the average response rate for the Current

Employment Statistics survey is 59%, the median 60%, the minimum 53%, and the maximum 64%.

Similarly, average, median, minimum and maximum response rates for the Employment Cost Index

survey are 71%, 72%, 67%, and 75%, respectively.

To investigate the relation between response rates and GDP estimation errors, we classify

the quarters with above (below) median response rates as high (low) partition. We find that the

low response rate quarters have higher errors relative to the high response rate quarters. However,

the differences are not statistically significant for both the Current Employment Statistics survey

(t=-0.90) and the Employment Cost Index survey (t=-0.31).

Overall, the collective univariate evidence in Panel A of Table 2 does not suggest that GDP

estimation errors vary with the macroeconomic environment. Put differently, estimation errors do

not appear to be meaningfully identifiable using simple data partitions based on various proxies

for the state of the macroeconomic environment. These results arguably suggest variation in the

macroeconomic environment does not systematically impact macroeconomic nowcasting.

However, the analysis is limited in that there may be other factors that can better capture variation

in nowcasting difficulty, and in turn driving variation in GDP errors. We consider such a possibly

via multivariate determinant model.

9 We downloaded BLS survey response rate data from https://www.bls.gov/osmr/response-rates/home.htm.

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3.2.2 Multivariate Evidence

In our multivariate determinant model, we consider a wide range of state variables

considered in the literature that are measurable for our entire sample period. First, Nallareddy and

Ogneva (2017) document that earnings dispersion captures labor and resource reallocation in the

economy and that initial macroeconomic estimates do not fully capture this information. Second,

Gilbert (2011) documents that stock market returns on the day of macroeconomic indicator

announcements predict subsequent BEA restatements. To the extent that the market can identify

errors in the initial estimate produced by the BEA, market returns on the GDP announcement day

would correlate with GDP estimation errors. Third, prior literature documents that estimation

errors in the initial GDP figures could affect monetary policy (e.g., Orphanides, 2001). Therefore,

GDP errors could be related to the FED’s policy decisions, which, in turn, could affect managers’

real decisions. Fourth, macroeconomic estimation errors could be related to professional

macroeconomic forecast errors. Specifically, the factors that contribute to misestimation by

professional forecasters may be the same as those faced by government agencies like the BEA.

To consider these possibilities, we estimate the following GDP error determinant model:

Errort+1 = α + β1 Ear_Dispt + β2 Ann_Rett + β3 T-billt + β4 Forecast_Errort + β5 Conumer_Sentit

+β6 Ret_Dispt + β7 Recessiont + εt+1, (2.1) where Errort+1 is the initially announced GDP estimate minus the final vintage GDP estimate for

the quarter t+1, Ear_Disp is earnings dispersion, Ann_Ret the S&P 500 index returns on the GDP

announcement day, t-bill the average 1-year maturity t-bill yield, and Forecast_Error is the

professional forecasters’ GDP forecast error. For completeness, we also include two

macroeconomic variables considered in our univariate analysis pertaining to consumer sentiment

captured by the University of Michigan Consumer Sentiment Index (Conumer_Senti) and business

cycle captured by a recession indicator variable (Recession). Finally, Ret_Disp is stock return

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dispersion. Other macroeconomic state variables we previously considered, including employment

surveys and economic uncertainty, are not available for our entire sample period. Detailed variable

definitions are in the appendix.

Estimation results are provided in Panel B of Table 2. The findings suggest that 14% of the

variation in GDP estimation errors can be explained overall, with the explanatory power coming

primarily from two factors. First, consistent with Nallareddy and Ogneva (2016), Ear_Dispt is

significantly related to BEA’s GDP estimation error for the next period (β3 = 0.27, t = 2.72).10

These findings suggest that earnings dispersion contains information about labor and resource

reallocation in the economy. Initial GDP estimates do not take this information content fully into

account, in turn driving a positive relation between earnings dispersion and GDP estimation errors.

Second, we find that macroeconomic forecast error by professional forecasters is related to the

GDP estimation error (β4 = 0.35, t = 3.64). No other determinant is statistically significant and as

we observed in our univariate tests, business cycles and consumer sentiment continue to be

unrelated to GDP estimation error.

We use the results from estimating equation (2.1) to decompose Errort+1 into two

components, one that is correlated with macroeconomic state variables (Error_Corrt) and the other

that is not (Error_UnCorrt).11 The correlated (uncorrelated) component is captured by the fitted

value (residual) of estimation equation (2.1). Descriptive statistics for Error_UnCorrt and

Error_Corrt are presented in Table 1, and by construction, the mean of Error_UnCorr is zero.

10 Nallareddy and Ogneva (2016) define GDP restatement as final minus the initial estimate. Whereas, to be consistent with the theoretical predictions, we define GDP restatements as initial minus the final estimate. Therefore, earnings dispersion is positively related to GDP estimation errors in Table 2, Panel B, whereas Nallareddy and Ogneva (2017) document a negative relation between earnings dispersion and GDP restatements. 11 Note that we calculate true GDP for each quarter as the most recently available BEA estimate as of December 15th, 2016. As a result, these data are not available in real time and our GDP error decomposition should not be interpreted as separating predictable from unpredictable components, but rather as separating components correlated with aggregate variables from those that are not.

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3.3. Economy-level Evidence of the Response to GDP Errors

In this section, we investigate whether the insights from the macroeconomic literature

showing macroeconomic signal errors are increasing in subsequent quarter’s macroeconomic

activity (e.g., Oh and Waldman, 1990; Mora and Schulstad, 2007) hold true in our extended sample

period. To do so, we estimate the following regression specification:

GDPt+1 = α + β1GDPt + β2Errort + β3Ann_Rett + β4T-billt + β5Forecast_Errort + β6Conumer_Sentit + εt+1, (2.2)

All variables are defined as previously.12 We use the Newey and West (1987) correction with a

lag order of four to address potential autocorrelation in standard errors. Results are displayed in

Column 1 of Table 3. Consistent with Oh and Waldman (1990), we find that GDP estimation errors

are positively related to subsequent economic growth (β2 = 0.552, t = 3.45). That is, periods with

higher estimation errors have higher subsequent economic growth and vice versa. To assess

whether the economic agents are able to unravel the estimation error from true GDP, we compare

the coefficient on Error with the coefficient on GDP. True GDP is increasing in future economic

growth (β1 = 0.607, t = 5.72), and the coefficient on Errort and GDPt do not statistically differ (p

= 0.659). Both findings are consistent with Oh and Waldman (1990). That agents are unable to

filter the noise component of the initial GDP forecast at all is somewhat puzzling given we

observed previously that a portion of the Error is correlated with proxies for the macroeconomic

12 We note that equation (2.2) excludes some of the variables that are part of the GDP error decomposition model equation (2.1). We exclude Recessiont because NBER classifies the economy into different business cycles using ex-post data. Instead, we include ex-ante consumer sentiment measure to proxy for the state of the economy. We also exclude Ear_Dispt and Ret_Dispt for two reasons. First, these variables are related to the GDP estimation error but the theoretical underpinnings for future GDP are unclear. Second, we will subsequently in equation (2.3) be decomposing Error into two components, where the fitted value will include the effects of earnings dispersion from Panel B of Table 1. Including Ear_Dispt and Ret_Dispt will result in perfect collinearity preventing estimation.

18

state. As a result, we partition Errort into Error_UnCorrt and Error_Corrt as discussed in Section

3.2 and re-estimate (2.2). This yields the following model:

GDPt+1 = α + β1GDPt + β2Error_UnCorrt + β3Error_Corrt + β4Ann_Rett + β5T-billt + β6Forecast_Errort + β7Conumer_Sentit + εt+1 (2.3)

where Error_UnCorr is the residual and Error_Corr is fitted value obtained from estimating (2.1).

Table 3 Column 2 presents the estimation results and reveals that after partitioning the errors into

portions correlated and uncorrelated with macroeconomic variables, only the uncorrelated portion

of GDP estimation errors is positively related to subsequent GDP (β2 = 0.555, t = 3.95). The portion

of the GDP estimation error that is correlated with macroeconomic variables is discounted by

market participants as evidenced by an statistically insignificant point estimate (β3 = -1.070, t = -

1.49). Put differently, the overall effect of Error in Column 1 of table 3 is driven entirely by the

portion of the error uncorrelated with macroeconomic state variables.

Given the robust positive relationship between GDP estimation errors and subsequent

quarter GDP, we proceed to replicate Mora and Schulstad (2007), who show that macroeconomic

estimation errors matter specifically for next quarter’s investment but not next quarter’s

consumption. To do so, we re-estimate equations (2.2) and (2.3) but replace the dependent variable

GDPt+1 with the GDP components investment (Investt+1) and consumption (Const+1), respectively.

Results are presented in Table 3 Columns 3 and 4 for aggregate investment and Columns 5 and 6

for aggregate consumption.

The results for one quarter ahead investments mimic those for GDP. Column 3 reveals that

both the true (β1 = 2.583, t = 4.40) and error components (β2 = 2.462, t = 3.31) of the initial GDP

estimate are positively associated with future investment, with effect sizes are not statistically

different from each other (p=0.854). Decomposing the estimation error in Column 4 shows that

the overall estimation error effect in Column 3 is driven by Error_UnCorr (β2 = 2.475, t = 3.47),

19

with no statistically significant relation between Error_Corr and future investment (β3 = -4.355, t

= 1.34).

Turning to consumption, in contrast to the GDP and investment results, we do not find that

GDP errors are related to subsequent consumption. In particular, as shown in Column 5, while the

true component of GDP is associated with future consumption (β1 = 0.096, t = 4.02) the point

estimate on Errort is not statistically different from zero (β2 = 0.052, t = 1.26). Decomposing Error

into two components reveals that neither the portion correlated nor the portion uncorrelated with

macroeconomic state variables exhibit a statistically significant association with future

consumption.

The evidence so far suggest a strong positive association between GDP signal errors and

investment, but a lack of a robust association between GDP signal errors and consumption. While

these results replicate Mora and Schulstad (2007), it provides no insights on why this occurs. One

conjecture is that consumers observing a macroeconomic signal respond by investing instead of

consuming (Bianchi, 2013). However, such an interpretation would require that we observe no

association between consumption and either portion of the initial GDP signal. However, we do

observe a positive and significant coefficient on the true portion of the GDP signal with respect to

consumption, as we observed with overall GDP and investment.

Another conjecture is that consumers do not consider macroeconomic forecasts in their

decision making, but rather consume based on their own observed disposable income. If such

disposable income is correlated with the true state of the economy, we may plausibly observe an

association between true GDP and consumption but not GDP errors and consumption. We have

no means of testing this conjecture. However, it nonetheless confirms the possibility that

production stemming from investment is not met with demand by households. As such a situation

20

may have important implications for firm profitability, we now turn to the assessment of how

macroeconomic signal errors influence public firm outcomes.

3.4. Real effects of macroeconomic estimation error: Firm-level evidence

We begin our investigation by studying how firms’ future capital expenditure, production,

and inventory balances relate to GDP estimation errors after controlling for macroeconomic state

variables. Our empirical specifications at the firm level follow the progression at the macro level

where equation (3.2) allows for a decomposition of the estimation error considered in equation

(3.1):

CAPEXi,t+1 = α0 + γ1GDPt + γ2Errort + MACRO CONTROLSt + FIRM CONTROLSi,t

+ FIRM FEi + υi,t+1 (3.1)

CAPEXi,t+1 = α0 + γ1GDPt + γ2.1 Error_UnCorrt + γ2.2 Error_Corrt

+ MACRO CONTROLSt + FIRM CONTROLSi,t + FIRM FEi + υi,t+1 (3.2)

The dependent variable captures firm level investment instead of economy wide aggregegate

investement. Specfically, our firm level investment proxy is capital expenditure, CAPEXt, defined

as the seasonally adjusted capital expenditure for firm i in quarter t scaled by lagged total assets

for quarter t. MACRO CONTROLS are the same as we examined previously and include the effects

of macroeconomic forecasters, (Forecast_Error) and overall market (Ann_Ret) expectations,

monetary policy (T-bill), as well as consumer sentiment (Consumer_Senti). In addition to the

macroeconomic controls, we add firm-specific investment determinants (FIRM CONTORLS)

identified by previous literature (Fazzari et al. 1988). FIRM CONTROLS are Tobin’s Q (TobinsQ)

at the beginning of the quarter, quarterly cash flow (Cash Flow), and change in cash flow (Ch.

Cash Flow). Finally, we include firm fixed effects (FIRM FE) to capture stable but unobservable

firm characteristics that may influence capital expenditures.

21

Table 4 Columns 1 to 3 present a progression to the full specification. We begin by

examining the effect of estimation errors with macro controls and firm fixed effects in column (1).

We observe a positive and statistically significant coefficient on both the true GDP signal

(coefficient = 0.064, p<0.01) and estimation error (coefficient = 0.062, p<0.01). As we observed

at the macro level, these coefficients do not statistically differ from each other (p=0.769). In

column (2) we replace the estimation error with its decomposed components and find that the

overall effect of error is driven by the portion of the error uncorrelated with macroeconomic

factors. That is, the coefficient on Error_UnCorr is positive and statistically significant

(coefficient = 0.062; p < 0.01) and of equal magnitude as the effect of Error overall, while the

coefficient on Error_Corr (coefficient = 0.034) is not statistically different from zero. In column

(3) we replicate column (2) but include time varying firm controls to assess whether they represent

correlated but omitted factors pertaining to firm level investment decisions. Including these

controls does little to impact the point estimates on true GDP or the uncorrelated estimation error,

which both decrease only slightly from 0.062 in column (2) to 0.056 in column (3). In the full

specification on column (3) the effect of true GDP does not statistically differ from the effect of

the uncorrelated estimation error (p=0.969), while both individually remain positive and

statistically significant. The correlated portion of the estimation error remains statistically

insignificant. Firm specific control variables are significant in their own right and consistent with

Fazzari et al. (1988).

Collectively, our findings to this point suggest that firms’ future capital investments mimic

investment patterns at the macroeconomic level. Firm’s capital expenditures are positively related

to errors in initial GDP estimates, specifically the uncorrelated portion of the error, and the

magnitude is statistically indistinguishable to the capital expenditure response to true GDP.

22

However, at the macroeconomic level investments do not appear to be fully backed by

contemporaneous increases in aggregate consumption, as previously discussed. Perhaps firms are

aware of this and do not produce more. Indeed, while capital investment relates to expected future

production, it does not necessarily imply that the increase in production is immediate. We,

therefore, investigate the effect of GDP estimation errors on firms’ production decisions. To do

so, we re-estimate equation (3.1) and (3.2) but replace capital expenditure growth with production

growth (Prod), defined as seasonally adjusted production scaled by lagged total assets. Table 4

Columns 4 to 6 present the results. Similar to the investment results, firm production exhibits a

significantly positive association with both true GDP and the overall error in GDP. The difference

in coefficient between true GDP and GDP estimation error is statistically indistinguishable. The

production response to the overall error is driven entirely by the residual component of the

estimation error.

Because consumers appear to exhibit little sensitivity to initial GDP errors and firm

production sensitivity is greater than zero, inventory holdings at the end of the quarter may vary

with initial GDP errors. For example, a positive initial GDP error would increase production,

which, if not met with consumption, would suggest a build-up of unsold inventory. To see if this

is the case, we re-estimate equations (3.1) and (3.2) but replace the dependent variable with

inventory (Inventory) as the dependent variable, where Inventoryt is equal to seasonally adjusted

changes in inventory scaled by lagged total assets. Columns 7 to 9 of Table 4 present the inventory

results, which display the same pattern as capital expenditure and production. Inventory is

increasing in true GDP and the estimation error, to the same extent, with the estimation error effect

being driven solely by the residual portion of the error.

23

3.5. Real effects of macroeconomic estimation error: Firm-level profitability

The evidence thus far suggests that at the firm level, capital expenditures, production and

inventory are increasing in GDP errors. Ultimately, whether GDP errors also systematically

influence firm profitability depends on both customer demand and how costly inventory holding

is for the firm. Prior research has documented that large positive (negative) inventory changes are

predictably associated with decreases (increases) in future firm profitability over the subsequent

four fiscal quarters (Thomas and Zhang, 2002). This relation between inventory changes and firm

profitability is consistent with firms overproducing relative to demand initially, and then having

to markdown the purchase price to ultimately sell the product or having to write down inventory

values for products that cannot be sold. Either situation would imply a negative impact on firm

profitability as a result of overproduction. The Thomas and Zhang (2002) result also implies that

firms which underproduce relative to demand initially experience improved future profitability,

either through the ability to increase selling prices to expand margins and/or through larger sales

volume to meet heightened demand.

To assess the impact of GDP errors on firm profitability we estimate the following

specifications:

Earni,t+1 = α0 + γ1GDPt + γ2Errort + MACRO CONTROLSt + FIRM CONTROLSi,t

+ FIRM FEi + υi,t+1 (3.3)

Earni,t+1 = α0 + γ1GDPt + γ2.1 Error_UnCorrt + γ2.2 Error_Corrt

+ MACRO CONTROLSt + FIRM CONTROLSi,t + FIRM FEi + υi,t+1 (3.4)

Equation (3.3) and (3.4) are identical to equations (3.1) and (3.2) except that we replace the

dependent variable with profitability (Earn) and change the firm controls (FIRM CONTROLS) to

include determinents of firm profitability from the extant literature. Earn is defined as seasonally

24

adjusted changes in earnings before extraordinary items scaled by lagged total assets. FIRM

CONTROLS are firm-year variables identified in the extant literature (Hou et al. 2012; Fama and

French 2000) and include Total Assets, defined as the natural logarithm of total assets, Dividends

defined as dividends scaled by lagged total assets, Dividend Dum defined as an indicator that the

firm pays a dividends, Neg Earn Dum defined as an indicator that the company is making a loss,

and Accruals defined as working capital accruals scaled by lagged total assets.

Table 5 Columns 1 to 3 provide the progression of estimation as in Table 4. Column 1

reveals that true GDP signals (γ1 = 0.121, t = 5.49) and GDP errors (γ1 = 0.112, t = 3.40) are

positively related to one-quarter ahead firm profitability and the effects are statistically of the same

magnitude. This implies that increases in production and inventory levels in response to positive

GDP signals are met with increased profits in the subsequent quarter.

In Column 2, we once more split Error into Error_UnCorr and Error_Corr and again

observe that the estimation error effect overall is driven by the residual component of the GDP

estimation error. The fitted component of the GDP estimation error continues to not statistically

differ from zero. Column (3) includes firm profitability determinants, which again are statistically

significant in their own right but do not alter the conclusions from column (2) where firm-year

controls were not considered.

3.6. GDP Estimation Errors and Firm Profitability: Cross-sectional Partitions

To better understand how GDP estimation errors impact firm profitability, we examine

whether the effects vary predictably in the cross-section. Prior literature documents that cyclical

firms are more sensitive to macroeconomic signals (e.g., Gomes, Kogan, and Yogo 2009; Gilbert,

25

Palacios, and Wang 2011). 13 If our results reflect firm responses to macroeconomic signals, we

should expect that GDP estimation errors have a stronger effect on cyclical firms’ profits. To test

this implication, we analyze the interactive effects between GDP estimation errors and cyclicality

on firm profitability. We employ three alternative measures of cyclicality. First, we measure firm

cyclicality as the cyclicality of demand for goods produced by the firm based on whether the firm

is a “make firm” or “use firm” (Gomes et al. 2009). Second, we measure firm cyclicality by its

degree of connectedness to other firms measured as eigenvector centrality (Ahern and Harford’s

2014).14 Third, we measure firm cyclicality via firm size (Anilowski et al. 2007).

Centrality Measure Based on Demand for Goods Produced: Make Vs. Use Firms

The profitability of firms that produce goods whose demand is more sensitive to

macroeconomic fluctuations should be more affected by shocks to expectations about state of the

economy. Bernanke (1983) argues that partial irreversibility paired with uncertainty causes

investment to be pro-cyclical and volatile. A large body of empirical research finds evidence

consistent with Bernanke’s theoretical predictions (e.g., Bloom, 2014). Indeed, as shown in Figure

3, quarterly growth in aggregate investment is pro-cyclical and volatile relative to GDP and

aggregate consumption. As a result, the demand for investment goods and the profitability of

investment goods produced by so-called “make firms” – who primarily sell to other businesses

rather than to households - should be more affected by shocks to expectations about the

macroeconomic state.

13 Anilowski et al. (2007) label such cyclical firms “bellwether firms” and examine how their management forecast behavior influences market returns. Bonsall et al. (2013) provide evidence that management forecast of cyclical firms contain timely and relevant information for the macro economy. Gilbert, Palacios, and Wang (2011) find that firms exposed to a systematic risk factor react more strongly to macroeconomic signals than other firms do, thereby revealing news about that risk factor. Hugon et al. (2016) show that sell side analyst access to a macroeconomist is particularly useful for cyclical firms. 14 We download BEA’s make-use tables from https://www.bea.gov/industry/input-output-accounts-data.

26

To identify make firms, we use the industry classification developed in Gomes et al.

(2009).15 Make firms fall into the investment good manufacturers category, and so we define Make

as an indicator variable that is equal to one for the firms that belong to a make industry and zero

otherwise. To investigate the incremental profitability response of make firms to the GDP

estimation errors relative to other firms, we modify our previous models by interacting the true

GDP and error component with Make indicator variable. Columns 1 and 2 of Table 6 display the

results. We find that make firm profitability is more sensitive to aggregate shocks. Specifically,

GDP interacted with Make is positive, suggesting that make firms’ profitability is more sensitive

to aggregate output shocks. Importantly, the interaction term between Make and GDP estimation

error is significantly positive (coefficient = 0.077, t = 3.38) suggesting that make firm profitability

is incrementally related to GDP errors relative to other firms. Furthermore, the interaction between

residual GDP error and Make is positive and statistically significant (coefficient = 0.091, t = 2.76),

whereas, the interaction with the fitted component of the estimation error is statistically

insignificant (coefficient = -0.007, t = -0.12).

Centrality Measure Based on Network Centrality: Eigenvector Centrality Measure

Signals about macroeconomic growth should have more pronounced effect on firms that

are more connected to the overall economy. Prior literature employs the industry level input-output

tables to measure firms’ connectedness and finds that increased connectedness explains merger

waves (Ahern and Harford 2014), leads to increased information spillovers (Aobdia et al. 2014),

and can be used to identify bellwether firms (Ogneva and Xia 2017). We build on these papers and

calculate industry-level eigenvector centrality (EVC) to proxy for industry’s connectedness with

15 We download industry classification data from Motohiro Yogo’s website https://sites.google.com/site/motohiroyogo.

27

the overall economy (Bonacich 1972; Wasserman and Faust 1994). An industry’s EVC increases

in its transaction volume with other well-connected industries. We measure firm level

connectedness with the overall economy as the EVC of its industry. Further, as argued above, firms

in “make” industries are more exposed to macroeconomic fluctuations than other firms. Hence,

we use annual summary Make tables to compute EVC.

Following Ahern and Harford (2014), we divide each cell in the summary make table by

the overall industry output for the year. EVC’s calculation requires a symmetric matrix, so we take

the maximum between each cell and its corresponding cell on the other side of the diagonal and

set both cells equal to that maximum. We find the set of eigenvectors of matrix (A) by solving:

Ac = λc (4)

where c is one of A’s eigenvectors and λ a constant. Eigenvector centrality for industry i is

measured as the ith element of A’s principal eigenvector, that is, the eigenvector with the largest

magnitude in c, the set of A’s eigenvectors. The resulting EVC distribution exhibits large outliers

and is heavily skewed. To mitigate the influence of outliers, we transform the initial EVC measure

by adding one and taking the natural logarithm of the resulting sum.

We modify our previous models by interacting the true GDP and error component with the

EVC measure to investigate the profitability response of central firms to the GDP estimation errors.

Columns 3 and 4 of Table 6 display the findings. In column 3, we find that more central industries

respond more to the true (0.009, t= 1.74) and error component (0.017, t=2.33) of GDP. In column

4 we find residual component of the GDP estimation error is responsible for the overall estimation

error effect in column 3 as it is positively related to subsequent profits (coefficient = 0.020, t=2.82)

while the fitted component exhibits a statistically insignificant association (coefficient = 0.004;

t=0.25). Overall, the findings suggest that more connected firms respond more to the GDP

28

estimation errors, consistent with theoretical predictions that expectation shocks and strategic

complementarities drive subsequent economic activity (e.g., Lorenzoni, 2009; Oh and Waldman,

1990).

Centrality Measure Based on Firm Size

The scope of larger firms’ activities is broader and their supplier and customer networks

are wider, resulting in increased connectedness and exposure to macroeconomic news. The

granular origins of the macroeconomic fluctuations literature (e.g., Gabaix 2011; Carvalho and

Grassi 2015) suggests that in economies with power-law shaped firm size distributions,

idiosyncratic performance fluctuations of large firms are responsible for a substantial fraction of

aggregate fluctuations, leading to a strong co-movement between such firms and the overall

economy. Therefore, large firms should also be more exposed to macroeconomic shocks.

Empirically, Anilowski et al. (2007) and Ogneva and Xia (2017) find that guidance and earnings

surprises of a small set of the largest firms within the US economy predict aggregate earnings,

aggregate returns, and GDP growth. Following this literature, we use firm size, measured as log

market capitalization, as a proxy for the entity’s sensitivity to macroeconomic fluctuations and

predict that their profitability is relatively more strongly affected by expectations shocks through

GDP signals.

Columns 5 and 6 of Table 6 document the findings using firm size (Size) as centrality

measure. As before, the variable of interest is the interaction term between centrality measure

(Size) and GDP error. We find that larger firms’ profitability is more strongly affected by GDP

estimation errors (coefficient = 0.009, t = 2.22). Moreover, the interaction of Size with residual

GDP estimation error is positively related to subsequent profitability (coefficient = 0.011, t = 2.77).

29

In contrast, the interaction term of Size and fitted GDP estimation error are not statistically related

to the subsequent profitability (coefficient = -0.002, t = -0.25).

As a collection, using three different proxies for cyclicality, we find consistent evidence

that more cyclical firms profitability exhibits heightened sensitivity to true GDP signals and GDP

forecast errors relative to less cyclical firms.

3.7. Reconciliation of Macro-level and Firm-level Findings and Long-Run Effects

The analysis thus far suggests that, at the firm level, investment, production, inventory

holdings, and profitability are increasing in the residual portion of GDP estimation errors. Further,

the impact of firm profitability is pronounced for cyclical firms. That firms are able to increase

profitability in response to positive GDP estimation errors implies consumption is increasing.

Indeed, in untabulated analysis, we find that one period ahead sales are also increasing in residual

GDP estimation errors. 16 However, at the macro level, while overall economic growth and

aggregate investment are increasing in residual GDP estimation errors, we found no association

between GDP estimation errors and consumption in Table 3.

How do we reconcile these firm-level and macro-level findings with respect to

consumption? One possiblity pertains to considering the type of customer that firms ultimatley

sell to. Make firms sell predominently to other firms while use firms sell predominently to

households. If we decompose overall aggregate spending in the economy, household spending,

which is caputred largely by consumption data at the macro level, comprises two-thirds of

aggregate spending. Business spending, which is captured by investement data at the macro level,

16 We repeat our tests after replacing profits with revenues. All our inferences are robust to this change. GDP estimation errors are positively related to subsequent firm revenues. Further, more cyclical firm revenues respond more to the GDP errors than less cyclical firm revenues.

30

comprises the remaining one-third of aggregate spending (Bureau of Economic Analysis

2017).17,18 This implies business-to-business sales will increase the profits of the firms that sell

predominently to other firms in the economy. Therefore, examining the differential effect of

residual GDP estimation errors on make firm versus non-make (i.e. use) firm profitability may

help to reconcile the conflicting findings at the firm and aggregate levels.

Furthermore, if increased firm investment and production is not ultimatley backed by

consumer demand, then profitability effects documented in the short-run should reverse over the

long-run. Such reversals should be stronger for the make firms. In this section, we conduct long-

run tests to (i) investigate the long-run effects of the GDP estimation errors on firm-level decisions

and profitability, (ii) how such long-run responses differ for make vs. use firms.

We start by analyzing long-run effects for the overall sample. Table 7 Panel A presents the

firm-level results for each of our variables of interest, capital expenditures (Panel A.1), production

(Panel A.2), inventory (Panel A.3), and profitability (Panel A.4), for one to eight quarters ahead.

We use the full specification but suppress the slope coefficients of control variables for parsimony.

The first column in each sub-panel replicates the findings in Tables 4 and 5. We find that residual

GDP estimation errors are positively related to capital expenditures up to four quarters in the

future. Similarly, these estimation errors are associated with production for the subsequent three

quarters. Inventory increases in residual GDP estimation errors for four quarters before the reaction

to the signal ceases. The timing of investment, production and inventory build-up aligns with

profitability reversals. In particular, the positive association with Error_UnCorr ceases in the

second quarter and turns negative in each quarter following the fifth quarter ahead, when inventory

17 Consumption also includes the goods and services purchased by the nonprofit institutions serving households. 18 Spending on fixed assets by the households are classified as investments rather than consumption.

31

ceases to associate with residual GDP estimation errors. In terms of statistical significance,

profitability six quarters ahead is significant (coefficient = -0.069, t = -1.75).

These patterns give rise to the following interpretation. In reaction to favorable GDP

signals, firms increase investment and production, which is partially met with demand initially,

but not in the intermediate term, leading to inventory build-up followed by reversals in

profitability. The question then becomes one of identifying where the initial demand that matches

production is coming from. If household consumers are not increasing their purchases but other

firms are, at the macroeconomic level we would observe aggregate investment increasing in GDP

estimation errors rather than consumption. If this is what is occurring, then at the firm level the

overall effects documented in Panel A of Table 7 should be especially pronounced (attenuated) for

firms that sell to other firms (end consumers). To assess this possibility, in Panel B and C of Table

7 we partition firms into make firms and non-make using the industry classification utilized in

Table 6.

Table 7 Panels B (C) present the results for the make (non-make) firms. The investment

response lasts for four quarters for make but only three quarters for non-make firms. Further, the

production response lasts for three quarters for make and non-make firms. Inventory builds up for

five quarters for make but only for four quarters for non-make firms. The impact of estimation

errors is positive for make firm profitability one (coefficient = 0.259, t = 5.91) and two (coefficient

= 0.155, t = 2.60) quarters ahead, but only last one quarter for non-make firms (coefficient = 0.104,

t = 3.28). In contrast, profitability reversals occur in quarters six (coefficient = -0.133, t = -2.32),

seven (coefficient = -0.128, t = -3.13), and eight (coefficient = -0.104, t = -2.10) for make firms

only. We interpret our findings as evidence that the effect of residual GDP estimation errors on

firm-level decision making is pronounced for firms whose customers comprise mainly other firms,

32

which are themselves investing in response to positive GDP signals. Further, the impact and

subsequent reversals are also concentrated in these make firms.

Do firm-level results extend back to the macroeconomy? If firm-level profitability is

reversed five and six quarters after the initial response to the residual GDP estimation error, then

we should also observe such reversals for economic growth and aggregate investments. To test

this conjecture, we explore the long-run dynamics between GDP estimation errors and subsequent

GDP and investment growth. Table 8 presents the results. We re-estimate (2.3) after changing the

dependent variable to economic/investment growth 1 to 8 quarters ahead.

The first column replicates the findings of Table 3 Column 2. One-quarter-ahead GDP is

increasing in estimation error (coefficient = 0.555, t = 3.95). Consistent with the findings from the

firm-level analysis, five quarters ahead GDP growth is negatively related to GDP estimation errors

(coefficient = -0.260, t = -1.66). Investment growth exhibits a similar pattern with the reversals in

investment five and six quarters ahead, with statistically significant results occurring six quarters

ahead.

Collectively, the long-run results suggest that short-run profitability effects of residual

GDP estimation errors partially reverse in future quarters. Initially pronounced investment and

production responses are followed by profitability reversals. These effects are more pronounced

for make firms, which helps to reconcile the puzzle that profitability at the firm level increases

while consumption at the aggregate level does not. At the aggregate level, make firm sales, which

are to other firms, are classified as investment and thus are included in macroeconomic

consumption figures. This allows for make firm sales and profitability to increase while aggregate

consumption does not. Finally, consistent with firm-level results, we do observe reversals in long-

term GDP and investments in the future quarters.

33

4. Robustness Checks

4.1. Alternative Measure of Macroeconomic Estimation Error

In our main analysis, we use the most recent GDP estimate (vintage date: December 15th,

2016) as a measure of true GDP. An advantage is that this is the most accurate proxy for true GDP

as it incorporates all revisions that occur as of December 2016. However, using this method to

identify true GDP, and by construction, the error in the GDP signal, has two potential limitations.

First, data points later in the sample period will not include the same amount of potential

information about true GDP as earlier data points. Second, allowing for long revision periods, such

as up to 5 years after the initial estimate, allows the potential for the measurement of true GDP to

change if the BEA updates their methodology. To overcome these limitations, we repeat all our

main tests using alternative measures for GDP and the GDP estimation error that utilizes a fixed

horizon. Specifically, we compute the alternative true GDP measure as BEA’s GDP estimate for

quarter t two years after the quarter has passed. Accordingly, we define the alternative GDP

estimation error measure as the initial GDP forecast made three weeks after the quarter ended

minus this alternative true GDP measure. We conduct the re-estimations using the full

specifications that include all control variables. We continue to find that sensitivities to both the

true and error components are positive and statistically significant for cyclical firms, and that the

point estimates are larger than for less cyclical firms. We conclude from this analysis that our

overall inferences regarding firm level responses to macroeconomic signals are not sensitive to the

definition of true GDP.

34

4.2. Alternative Deflators

In untabulated analysis, we redo all our test with alternative deflators such as market and

book values of equity. Our inferences remain unchanged.

5. Conclusions

We document that public firms respond to estimation errors inherent in initial GDP

estimates. We find that positive (negative) errors in initial GDP estimates relate to higher (lower)

subsequent quarter firm investment, production, inventory levels and profitability. We find the

effects to be stronger for cyclical firms, which are more sensitive to macroeconomic fluctuations.

We also observe that cyclical firm responses to favorable GDP errors result in inventory build-up,

which decreases profitability in subsequent quarters.

Our examination provides, to the best of our knowledge, the first evidence of how public

firms respond to estimation errors in initial macroeconomic announcements. We extend the

literature in finance and accounting interested in the determinants of corporate investment and

profitability by introducing a specific macroeconomic factor important to macroeconomists.

Investigation of the role of other economic agents outside of the public company domain represents

a fruitful area of research for deepening our understanding of the role of macroeconomic signals

in the economy.

35

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Appendix: Variable Definitions

Macroeconomic Variables

Variable Definition Ann_Rett S&P 500 index return on the day of initial GDP announcement for

quarter t.

Const Real personal consumption expenditure for quarter t. Seasonally adjusted annual rate.

Consumer_Sentit Change in consumer sentiment according to the University Michigan Consumer Sentiment Index for quarter t.

Ear_Dispt Cross-sectional standard deviation of seasonal random walk earnings deflated by total assets for quarter t.

Errort Real GDP estimation error (initially announced value minus final restated value) for quarter t.

Error_Corrt Component of Errort that is correlated with macroeconomic state variables calculated as the fitted value of estimating (2.1).

Error_UnCorrt Component of Errort that is uncorrelated with macroeconomic state variables calculated as the residual of estimating (2.1).

Forecast_Errort Initial real GDP estimate minus median real GDP macroeconomist forecast for quarter t.

GDPt Final vintage (as of March 2017) value of real GDP growth for quarter t. Seasonally adjusted annual rate.

Invt Real gross private domestic investment for quarter t. Seasonally adjusted annual rate.

Recessiont Recession indicator according to NBER’s US Business Cycle Expansions and Contractions.

Ret_Dispt Cross-sectional standard deviation of stock returns for quarter t.

T-billt Annualized average daily 1-year maturity t-bill yield during quarter t.

40

Firm Variables Variable Definition

Accrualsi,t Accruals (Compustat: IBQt – OANCFYt – OANCFYt-1) for firm i in quarter t scaled by lagged total assets (Compustat: ATQt-1).

Cash Flowi,t Seasonally adjusted cash flow for firm i in quarter t (Compustat: OANCFYt – OANCFYt-4) scaled by lagged total assets (Compustat: ATQt-1).

CAPEXt Seasonally adjusted capital expenditures (Capital expenditurest – Capital expenditurest-4) scaled by lagged total assets for quarter t (Compustat: ATQt-1). Capital expenditures are defined as the Compustat variable CAPXY in the year’s first fiscal quarter, and CAPXY minus lagged CAPXY for all subsequent quarter as Compustat reports cumulative capital expenditures in the variable CAPXY.

Ch. Cash Flowi,t Seasonally adjusted change in cash flow for firm i in quarter t (Compustat: (OANCFYt – OANCFYt-4) - (OANCFYt-1 – OANCFYt-5)) scaled by lagged total assets (Compustat: ATQt-1).

Dividendsi,t Dividends flow for firm i in quarter t (Compustat: DVYt) scaled by lagged total assets (Compustat: ATQt-1).

Dividend Dumi,t Indicator that firm i is paying a dividend in quarter t.

Earnt Seasonally adjusted earnings before extraordinary items (Compustat: IBQt - IBQt-4) scaled by lagged total assets (Compustat: ATQt-1) for quarter t.

EVC Natural logarithm of 1 plus the firm’s industry eigenvector centrality, where industry j’s eigenvector centrality is calculated as the jth element of the principal eigenvector calculated annually from BEA’s IO summary Make table matrix. We divide each cell in the summary Make table by the overall industry output for the year and make each of matrix symmetric by taking the maximum between each cell and its corresponding cell on the other side of the diagonal and set both cells equal to that maximum.

Inventoryt Seasonally adjusted inventory (Compustat: INVTQt - INVTQt-4) scaled by lagged total assets for quarter t (Compustat: ATQt-1).

Make Indicator that the firm belongs to an investment goods producing industry according to Gomes et al.’s (2009) industry classification.

41

Neg Earn Dumi,t Indicator that firm i’s earnings are negative in quarter t.

Prodt Seasonally adjusted production scaled by lagged total assets for quarter t. Production is defined as cost of goods sold for quarter t (Compustat: COGSQt) plus inventory for quarter t (Compustat: INVTQt) minus inventory for quarter t-1 (Compustat: INVTQt-1).

Size Natural logarithm of firm i’s market capitalization Compustat: (PRCCQt × CSHOQt) in quarter t.

TobinsQi,t Tobin’s Q for firm i at the beginning of quarter t (Compustat: (PRCCQt-1 × CSHOQt-1 + 2 × ATQt-1 – LTQt-1)/ ATQt-1).

Total Assetsi,t Natural logarithm of total assets (Compustat: ATQt) for firm i in quarter t.

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Figure 1. Timeline of GDP Initial Estimate, Final Estimate, and Error

Figure 1 illustrates the decomposition of the initial GDP estimate into true GDP, as proxied by the final restated GDP estimate, and GDP estimation error.

Figure 2. GDP Errors Over Time

Figure 2 plots the time series of the GDP estimation error over time (Errort = Intial_GDPt - GDPt). Grey shaded regions represent the recessions.

-0.1

-0.05

0

0.05

0.1

1972 1977 1982 1987 1992 1997 2002 2007 2012

GD

P Er

ror

Year

Mean: -0.41Auto-correlation: -0.03

End of Quarter t-1

Initial GDP Announcement for Quarter t (Initial_GDPt)

Initial_GDPt = GDPt + Errort

End of Quarter t

Final Restated GDP Estimate for Quarter t (GDPt)

End of Last Quarter in the Sample

End of Quarter t+1

43

Figure 3. Investment, GDP and Consumption Over Time

Figure 3 plots the time series of quarterly Investment growth, GDP growth and Consumption growth over time. Grey shaded regions represent recessions.

Investment

-50

-40

-30

-20

-10

0

10

20

30

40

50

1972 1977 1982 1987 1992 1997 2002 2007 2012

% C

hang

e

Year

Consumption GDP Investment

44

Table 1. Descriptive Statistics Panel A. Descriptive Statistics: Macro Variables (*100)

N Mean Std P1 P25 Median P75 P99

GDP 178 2.82 3.30 -7.87 1.30 2.99 4.48 10.22 Initial_GDP 178 2.42 3.09 -9.10 1.22 2.50 3.95 8.95 Error 178 -0.41 2.00 -7.51 -1.58 -0.34 0.90 4.38 Error_UnCorr 178 0.00 1.82 -5.26 -1.04 0.01 0.83 5.05 Error_Corr 178 -0.41 0.83 -2.69 -0.90 -0.41 0.09 1.67 Invest 178 4.59 15.76 -38.70 -3.60 3.15 13.60 44.60 Cons 178 0.75 0.66 -1.47 0.41 0.78 1.10 2.13 Ann_Ret 178 0.04 1.01 -2.33 -0.55 0.06 0.54 3.33 T-bill 178 1.31 0.87 0.03 0.56 1.34 1.86 3.55 Forecast_Error 178 0.10 1.85 -4.81 -0.96 -0.07 1.19 5.34 Conumer_Senti 178 0.06 5.31 -13.90 -3.00 -0.15 2.90 15.20 Ret_Disp 178 20.72 6.87 11.89 16.45 19.42 22.66 49.06 Ear_Disp 178 3.12 1.94 0.67 1.39 3.09 4.04 8.49 Recession 178 14.05 34.84 0.00 0.00 0.00 0.00 100.00

Panel B. Descriptive Statistics: Firm Variables (*100)

N Mean Std P1 P25 Median P75 P99 Ear 288,719 0.06 4.18 -16.17 -0.53 0.09 0.76 16.80

CAPEX 288,719 0.12 1.65 -5.41 -0.21 0.00 0.35 6.92

Prod 288,719 1.30 6.32 -18.24 -0.44 0.45 2.62 27.18

Inventory 288,719 0.67 4.05 -12.96 -0.08 0.00 1.14 17.04 Table 1 presents descriptive statistics for macroeconomic and firm level variables. For panel A (macroeconomic data), the sample period spans from Q1:1972 to Q3:2016. For panel B (firm-level data), the sample period spans from Q1:1988 to Q3:2016. Variable definitions are in the Appendix.

45

Table 2. GDP Error and Macroeconomic Environment Panel A. Macroeconomic Estimation Errors over Alternative Periods (*100)

Business Cycle Expansion Recession Diff t-value Error -0.38 -0.57 0.19 -0.44 Obs. 153 25

Transition from Recession to Expansion Yes No Diff t-value Error -0.41 -0.41 0.00 -0.01 Obs. 6 172

Consumer Sentiment High Low Diff t-value Error -0.45 -0.37 -0.08 -0.27 Obs. 88 90

Economic Uncertainty High Low Diff t-value Error -0.19 -0.24 0.05 0.17 Obs. 63 63

Current Employment Statistics: Survey Response Rate High Low Diff t-value Error 0.07 0.54 -0.47 -0.90 Obs. 17 17

Employment Cost Index: Survey Response Rate High Low Diff t-value Error 0.22 0.39 -0.17 -0.31 Obs. 17 17

Panel B. GDP Estimation Errors and Aggregate Variables Intercept Ear_

Dispt Ann_ Rett

T-billt Forecast_ Errort

Conumer_ Sentit

Ret_ Dispt

Recessiont

Adj. R2

#Obs

0.00 0.27*** 0.12 -0.19 0.35*** -0.03 -0.05 0.01 0.14 178 (0.08) (2.72) (1.10) (-1.57) (3.64) (-1.33) (-1.52) (1.03)

Table 2 documents the macroeconomic estimation errors over alternative periods and the relation between the estimation errors and aggregate variables. Expansion are the quarters with GDP expansion periods (NBER recession indicator equals 0), Recession are the quarters with GDP recession periods (NBER recession indicator equals 1), Transition from Recession to Expansion are the quarters where economy is transitioning from recession to expansion, High (Low) consumer sentiment are the quarters with above (below) median consumer sentiment, High (Low) economic uncertainty are the quarters with above (below)

46

median economic uncertainty, High (Low) response rate are the quarters with above (below) median survey response rates. Economic uncertainty data is available from Q1:1985-Q3:2016. Survey response rate data is available from Q3:2007-Q3:2016. Panel B presents the relation between macroeconomic estimation error and aggregate variables. The sample period spans from Q1:1972 to Q3:2016. Variable definitions are in the Appendix. Standard errors are calculated following Newey and West (1987) using a lag order of 4 quarters. Robust t-statistics are reported in parentheses. ***, **, and * denote statistical significance at the 1, 5, and 10% level, respectively.

47

Table 3. Macro-level Evidence: GDP Announcements and Subsequent GDP, Investment, and Consumption Growth

Dep Var GDPt+1 Investt+1 Const+1 (1) (2) (3) (4) (5) (6) Intercept 0.011** 0.016*** -0.013 0.006 0.005*** 0.006*** (2.39) (2.86)

(-0.55) (0.26)

(3.73) (3.67)

GDPt 0.607*** 0.545*** 2.583*** 2.324*** 0.096*** 0.086*** (5.72) (5.19)

(4.40) (3.81)

(4.02) (3.52)

Errort 0.552*** 2.462*** 0.052 (3.45)

(3.31)

(1.26)

Error_UnCorrt 0.555*** 2.475*** 0.053 (3.95)

(3.47)

(1.40)

Error_Corrt -1.070 -4.355 -0.199 (-1.49)

(-1.34)

(-1.05)

Ann_Rett 0.039 0.215 0.750 1.489 -0.049 -0.021 (0.16) (0.82)

(0.69) (1.31)

(-1.05) (-0.40)

T-billt 0.172 -0.610 -0.376 -3.662** 0.015 -0.107 (0.67) (-1.28)

(-0.32) (-2.19)

(0.20) (-0.80)

Forecast_Errort -0.528** 0.077 -0.989 1.553 -0.120*** -0.027 (2.49) (-0.24)

(1.04) (-0.97)

(3.71) (0.37)

Conumer_Sentit -0.128** -0.067 -0.679** -0.423 -0.022** -0.013 (-2.54) (-1.41)

(-2.46) (-1.60)

(-2.49) (-1.20)

Obs. 178 178 178 178 178 178

Adj. R2 0.210 0.240 0.260 0.280 0.140 0.160 Pr[GDPt = Errort] 0.659 0.854 0.177 Pr[GDPt = Error_UnCorr] 0.928 0.809 0.271

Table 3 documents the real effects of macroeconomic estimation errors on the aggregate economy. The table presents the results of regressing future macroeconomic variables (GDP/ investment growth (Invest) /real consumption growth (Cons)), on lagged GDP, GDP estimation error, and controls. Error_Corr (Error_UnCorr) is the part of the GDP estimation error that is correlated (uncorrelated) with macroeconomic state variables. The sample period spans from Q1:1972 to Q3:2016. Variable definitions are in the Appendix. Standard errors are calculated following Newey and West (1987) using a lag order of 4 quarters. Robust t-statistics are reported in parentheses. ***, **, and * denote statistical significance at the 1, 5, and 10% level, respectively. The last two rows of the panel report the p-values of F-tests testing whether the coefficients of GDP and Error or Error_UnCorr are equal.

48

Table 4. GDP Announcements and Subsequent Investments, Production, and Inventory Dep Var Capext+1 Prodt+1 Invt+1 (1) (2) (3) (4) (5) (6) (7) (8) (9)

GDPt 0.064*** 0.062*** 0.056*** 0.436*** 0.434*** 0.408*** 0.164*** 0.152*** 0.134*** (9.68) (8.33) (7.82) (10.32) (9.48) (9.08) (7.47) (5.93) (5.49)

Errort 0.062*** 0.394*** 0.156*** (6.31) (6.50) (4.55)

Error_UnCorrt 0.062*** 0.056*** 0.394*** 0.367*** 0.155*** 0.136*** (6.03) (5.59) (6.42) (6.15) (4.23) (3.77)

Error_Corrt 0.034 0.024 0.352 0.323 -0.039 -0.041 (0.81) (0.56) (1.57) (1.42) (-0.35) (-0.37)

TobinsQi,t 0.001*** 0.005*** 0.003*** (20.99) (15.11) (15.27)

Cash Flowi,t 0.011*** 0.013*** -0.046*** (10.71) (3.12) (-12.76)

Ch. Cash Flowi,t -0.003** -0.039*** -0.047*** (-2.05) (-6.32) (-9.85) Macro Controls YES YES YES YES YES YES YES YES YES Firm FE YES YES YES YES YES YES YES YES YES Obs. 288,719 288,719 288,719 288,719 288,719 288,719 288,719 288,719 288,719 Adj. R2 0.014 0.014 0.024 0.100 0.100 0.109 0.108 0.108 0.131 Pr[GDP = Error] 0.769 0.337 0.715 Pr[GDP = Error_UnCorr] 0.964 0.969 0.364 0.355 0.893 0.947

Table 4 documents the real effects of macroeconomic estimation errors on firms’ subsequent real decisions. The Table presents the results of regressing future firm decisions (investment growth (Capex)/ Production (Prod)/ Inventory (Inv)) on lagged GDP, GDP estimation error, controls, and firm fixed effects. Error_Corr (Error_UnCorr) is the part of the GDP estimation error that is correlated (uncorrelated) with macroeconomic state variables. The sample period spans from Q1:1988 to Q3:2016. Variable definitions are in the Appendix. Standard errors are clustered at the firm and quarter levels. Robust t-statistics are reported in parentheses. ***, **, and * denote statistical significance at the 1, 5, and 10% level, respectively. The last two rows of the panel report the p-values of F-tests testing whether the coefficients of GDP and Error or Error_UnCorr are equal.

49

Table 5. GDP Announcements and Subsequent Profits Dep Var Earnt+1 (1) (2) (3)

GDPt 0.121*** 0.123*** 0.117*** (5.49) (5.85) (5.82)

Errort 0.112*** (3.40)

Error_UnCorrt 0.112*** 0.129*** (3.45) (4.01)

Error_Corrt 0.144 0.159 (1.33) (1.60)

Total Assetsi,t -0.005*** (-11.25)

Dividendsi,t -0.071** (-2.44)

Dividend Dumi,t -0.001** (-2.40)

Neg Earn Dumi,t -0.010*** (-15.73)

Accrualsi,t 0.014*** (6.97) Macro Controls YES YES YES Firm FE YES YES YES Obs. 288,719 288,719 288,719 Adj. R2 0.004 0.004 0.018 Pr[GDP = Error] 0.718 Pr[GDP = Error_UnCorr] 0.693 0.647

Table 5 documents the real effects of macroeconomic estimation errors on firms’ subsequent profitability. The Table presents the results of regressing future firm profitability on lagged GDP, GDP estimation error, controls, and firm fixed effects. Error_Corr (Error_UnCorr) is the part of the GDP estimation error that is correlated (uncorrelated) with macroeconomic state variables. The sample period spans from Q1:1988 to Q3:2016. Variable definitions are in the Appendix. Standard errors are clustered at the firm and quarter levels. Robust t-statistics are reported in parentheses. ***, **, and * denote statistical significance at the 1, 5, and 10% level, respectively. The last two rows of the panel report the p-values of F-tests testing whether the coefficients of GDP and Error or Error_UnCorr are equal.

50

Table 6. GDP Announcement and Subsequent Profits – Cross-Sectional Analysis Dep Var Earni,t+1 (1) (2) (3) (4) (5) (6)

GDPt 0.103*** 0.104*** 0.173*** 0.182*** 0.079*** 0.075*** (4.63) (5.07) (4.25) (4.79) (2.77) (2.97)

Errort 0.118*** 0.245*** 0.070* (3.47) (4.56) (1.88)

Error_UnCorrt 0.115*** 0.267*** 0.053 (3.48) (5.10) (1.42)

Error_Corrt 0.160* 0.190 0.160 (1.68) (1.04) (1.40)

Make × GDPt 0.070** (2.35)

Make × Errort 0.077*** 0.084*** (3.38) (3.93)

Make × UnCorrt 0.091*** (2.76)

Make × Corrt -0.007 (-0.12)

EVC × GDPt 0.009* 0.010** (1.74) (2.09)

EVC × Errort 0.017** (2.33)

EVC × UnCorrt 0.020*** (2.82)

EVC × Corrt 0.004 (0.25)

Size × GDPt 0.005** 0.006** (1.98) (2.58)

Size × Errort 0.009** (2.22)

Size × UnCorrt 0.011*** (2.77)

Size × Corrt -0.002 (-0.25) FE & Controls YES YES YES YES YES YES Obs. 288,719 288,719 260,297 260,297 288,131 288,131 Adj. R2 0.019 0.019 0.019 0.019 0.019 0.019

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Table 6 documents the real effects of macroeconomic estimation errors on firms’ subsequent profitability for alternative cross-sectional partitions. Error_Corr (Error_UnCorr) is the part of the GDP estimation error that is correlated (uncorrelated) with macroeconomic state variables. Make is an indicator for the firms that are members of an investment good producing industry according to Gomes et al.’s (2009) industry classification. EVC is eigenvector centrality, which measures the connectedness of the firm’s industry. Size is the natural logarithm of the firm’s market capitalization. The sample period spans from Q1:1988 to Q3:2016. Variable definitions are in the Appendix. Standard errors are clustered at the firm and quarter levels. Robust t-statistics are reported in parentheses. ***, **, and * denote statistical significance at the 1, 5, and 10% level, respectively.

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Table 7. Long-Run Effects

Panel A: All Firms

Panel A.1: Capex

Dep Var Capext+1 Capext+2 Capext+3 Capext+4 Capext+5 Capext+6 Capext+7 Capext+8

Error_UnPredt 0.056*** 0.060*** 0.056*** 0.027* 0.013 -0.006 -0.014 -0.018 (5.59) (6.80) (4.75) (1.67) (0.72) (-0.44) (-1.09) (-1.46) Obs. 288,719 284,422 280,855 277,783 274,902 271,729 268,725 265,973 Adj. R2 0.024 0.023 0.019 0.012 0.010 0.009 0.009 0.009 FE & Controls YES YES YES YES YES YES YES YES Panel A.2. Production

Dep Var Prodt+1 Prodt+2 Prodt+3 Prodt+4 Prodt+5 Prodt+6 Prodt+7 Prodt+8

Error_UnPredt 0.367*** 0.302*** 0.178** -0.003 -0.082 -0.141* -0.130* -0.195*** (6.15) (5.17) (2.17) (-0.04) (-0.92) (-1.77) (-1.76) (-2.80) Obs. 288,719 283,680 280,268 277,190 274,302 270,401 267,569 264,801 Adj. R2 0.109 0.104 0.096 0.089 0.082 0.081 0.077 0.077 FE & Controls YES YES YES YES YES YES YES YES Panel A.3. Inventory

Dep Var Invt+1 Invt+2 Invt+3 Invt+4 Invt+5 Invt+6 Invt+7 Invt+8

Error_UnPredt 0.136*** 0.170*** 0.168*** 0.133*** 0.063 0.005 -0.030 -0.073* (3.77) (5.29) (4.37) (3.29) (1.43) (0.10) (-0.65) (-1.68) Obs. 288,719 284,687 281,339 278,326 275,536 272,551 269,809 267,099 Adj. R2 0.131 0.125 0.116 0.108 0.103 0.100 0.099 0.098 FE & Controls YES YES YES YES YES YES YES YES Panel A.4. Earnings

Dep Var Earnt+1 Earnt+2 Earnt+3 Earnt+4 Earnt+5 Earnt+6 Earnt+7 Earnt+8

Error_UnPredt 0.129*** 0.070 -0.063 0.012 -0.038 -0.069* -0.043 -0.057 (4.01) (1.40) (-1.09) (0.37) (-1.51) (-1.75) (-1.42) (-1.61) Obs. 288,719 284,774 281,591 278,719 275,985 273,088 270,502 267,924 Adj. R2 0.018 0.015 0.015 0.074 0.013 0.009 0.007 0.005 FE & Controls YES YES YES YES YES YES YES YES

Panel B: Make Firms Panel B.1: Capex

Dep Var Capext+1 Capext+2 Capext+3 Capext+4 Capext+5 Capext+6 Capext+7 Capext+8

Error_UnPredt 0.058*** 0.070*** 0.060*** 0.035** 0.019 -0.012 -0.018 -0.021 (4.39) (6.13) (5.09) (2.19) (1.03) (-0.71) (-1.10) (-1.50)

Obs. 46,413 45,631 44,986 44,454 43,957 43,416 42,895 42,433

Adj. R2 0.027 0.026 0.021 0.013 0.010 0.009 0.008 0.009

FE & Controls YES YES YES YES YES YES YES YES

Panel B.2: Production

Dep Var Prodt+1 Prodt+2 Prodt+3 Prodt+4 Prodt+5 Prodt+6 Prodt+7 Prodt+8

Error_UnPredt 0.693*** 0.554*** 0.382*** 0.062 -0.118 -0.221* -0.261** -0.302*** (7.63) (5.66) (3.32) (0.44) (-0.82) (-1.68) (-2.13) (-2.79)

Obs. 46,413 45,495 44,889 44,365 43,884 43,209 42,731 42,275

Adj. R2 0.071 0.064 0.053 0.043 0.036 0.036 0.038 0.041

FE & Controls YES YES YES YES YES YES YES YES

Panel B.3: Inventory

Dep Var Invt+1 Invt+2 Invt+3 Invt+4 Invt+5 Invt+6 Invt+7 Invt+8

Error_UnPredt 0.301*** 0.414*** 0.465*** 0.407*** 0.266*** 0.152 0.046 -0.040 (4.23) (5.83) (5.92) (4.66) (2.74) (1.47) (0.43) (-0.38)

Obs. 46,413 45,691 45,088 44,575 44,093 43,599 43,134 42,687

Adj. R2 0.128 0.122 0.109 0.097 0.085 0.075 0.068 0.065

FE & Controls YES YES YES YES YES YES YES YES

Panel B.4: Earnings

Dep Var Earnt+1 Earnt+2 Earnt+3 Earnt+4 Earnt+5 Earnt+6 Earnt+7 Earnt+8

Error_UnPredt 0.259*** 0.155** -0.018 0.044 -0.078 -0.133** -0.128*** -0.104** (5.91) (2.60) (-0.26) (0.85) (-1.65) (-2.32) (-3.13) (-2.10)

Obs. 46,413 45,682 45,086 44,574 44,093 43,582 43,118 42,669

Adj. R2 0.030 0.021 0.021 0.095 0.022 0.016 0.008 0.004

FE & Controls YES YES YES YES YES YES YES YES

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Panel C: Non-Make Firms Panel C.1: Capex Dep Var Capext+1 Capext+2 Capext+3 Capext+4 Capext+5 Capext+6 Capext+7 Capext+8

Error_UnPredt 0.055*** 0.058*** 0.055*** 0.025 0.011 -0.005 -0.014 -0.017 (5.55) (6.52) (4.52) (1.55) (0.64) (-0.38) (-1.07) (-1.41) Obs. 242,306 238,791 235,869 233,329 230,945 228,313 225,830 223,540 Adj. R2 0.024 0.022 0.018 0.012 0.010 0.009 0.009 0.009 FE & Controls YES YES YES YES YES YES YES YES Panel C.2: Production Dep Var Prodt+1 Prodt+2 Prodt+3 Prodt+4 Prodt+5 Prodt+6 Prodt+7 Prodt+8

Error_UnPredt 0.307*** 0.256*** 0.139* -0.016 -0.076 -0.128* -0.108 -0.178*** (5.37) (4.72) (1.80) (-0.18) (-0.94) (-1.78) (-1.62) (-2.80) Obs. 242,306 238,185 235,379 232,825 230,418 227,192 224,838 222,526 Adj. R2 0.118 0.113 0.106 0.099 0.092 0.090 0.085 0.086 FE & Controls YES YES YES YES YES YES YES YES Panel C.1: Inventory Dep Var Invt+1 Invt+2 Invt+3 Invt+4 Invt+5 Invt+6 Invt+7 Invt+8

Error_UnPredt 0.106*** 0.126*** 0.113*** 0.083** 0.026 -0.022 -0.044 -0.080** (3.45) (4.78) (3.64) (2.56) (0.73) (-0.61) (-1.23) (-2.36) Obs. 242,306 238,996 236,251 233,751 231,443 228,952 226,675 224,412 Adj. R2 0.135 0.130 0.122 0.115 0.111 0.109 0.108 0.108 FE & Controls YES YES YES YES YES YES YES YES Panel C.1: Earnings Dep Var Earnt+1 Earnt+2 Earnt+3 Earnt+4 Earnt+5 Earnt+6 Earnt+7 Earnt+8

Error_UnPredt 0.104*** 0.054 -0.072 0.005 -0.031 -0.058 -0.028 -0.049 (3.28) (1.10) (-1.28) (0.18) (-1.35) (-1.51) (-0.91) (-1.47) Obs. 242,306 239,092 236,505 234,145 231,892 229,506 227,384 225,255 Adj. R2 0.017 0.014 0.015 0.070 0.012 0.008 0.006 0.005 FE & Controls YES YES YES YES YES YES YES YES

Table 7 documents long-run real effects of macroeconomic estimation errors on firms’ subsequent real decisions and profitability. Panel A (B, C) presents the results of regressing future firm decisions and profitability on lagged GDP, GDP estimation error, controls, and firm fixed effects for all (Make, Non-Make) firms. Error_UnCorr is the part of the GDP estimation error that is uncorrelated with macroeconomic state variables. Capext+i is t+i quarters-ahead capital expenditures, Prodt+i is t+i quarters-ahead production, Invt+i is t+i quarters-ahead inventory, Earnt+i is t+i quarter-ahead profitability. Suppressed control variables are the same as in Tables 4 to 6. The sample period spans from Q1:1988 to Q3:2016. Variable definitions are in the Appendix. Standard errors are clustered at the firm and quarter levels. Robust t-statistics are reported in parentheses. ***, **, and * denote statistical significance at the 1, 5, and 10% level, respectively, in a two tailed test.

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Table 8. GDP Announcements and Subsequent Economic Growth: Long-Run Effects

Panel A: Estimation Error and Subsequent GDP Growth

Dep Var GDPt+1 GDPt+2 GDPt+3 GDPt+4 GDPt+5 GDPt+6 GDPt+7 GDPt+8

Error_UnCorrt 0.555*** 0.262 0.162 0.102 -0.260* -0.228 -0.059 0.017

(3.95) (1.18) (0.83) (0.47) (-1.66) (-1.07) (-0.24) (0.08)

Obs. 178 177 176 175 174 173 172 171

Adj. R2 0.240 0.090 0.040 0.030 0.000 0.020 0.050 0.030

Panel B: Estimation Error and Subsequent Investment Growth

Dep Var Investt+1 Investt+2 Investt+3 Investt+4 Investt+5 Investt+6 Investt+7 Investt+8

Error_UnCorrt 2.475*** -0.012 -0.110 -0.272 -1.335 -1.846* -1.130 -0.604

(3.47) (-0.01) (-0.12) (-0.32) (-1.57) (-1.78) (-1.00) (-0.77)

Obs. 178 177 176 175 174 173 172 171 Adj. R2 0.280 0.090 0.030 0.040 0.040 0.040 0.070 0.090

Table 8 documents long-run real effects of macroeconomic estimation errors on the aggregate economy. The table presents the results of regressing future macroeconomic variables (GDP/ investment growth (Invest)), on lagged GDP, GDP estimation error, and controls. Error_Corr (Error_UnCorr) is the part of the GDP estimation error that is correlated (uncorrelated) with macroeconomic state variables. GDPt+i is t+i quarters-ahead GDP growth, and Investt+i is t+i quarters-ahead aggregate invetsment. Suppressed control variables are the same as in Table 3. The sample period spans from Q1:1972 to Q3:2016. Variable definitions are in the Appendix. Standard errors are calculated following Newey and West (1987) using a lag order of 4 quarters. Robust t-statistics are reported in parentheses. ***, **, and * denote statistical significance at the 1, 5, and 10% level, respectively.