December 15, 2018 Investment Business the with …Infrastructure Investment News and Business...

Infrastructure Investment News and Business Cycles:Evidence from the VAR with External Instruments

Etsuro Shioji (Hitotsubashi)

CFE2018@PisaDecember 15, 2018

Acknowledgement

Research for this work has been funded by • MEXT through the Hitotsubashi Institute for Advanced Study (HIAS)

• Grant‐in‐aid for Scientific Research– A‐17H00985– C‐15K03418– C‐18K01605

• Nomura Foundation.

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Objective

Propose a new approach to tackle the

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“Fiscal Foresight” Problem

Structure of presentation

1. Introduction2. VAR with External Instruments (VAR‐IV)3. News Indicator: details4. Results from VAR‐IV with news indicator5. Conclusions

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1. Introduction

Why Public Investment?

• Always a subject of heated debate in Japan.

• And... suddenly, also in the US! (since late 2016...)

6

Difficulty in estimating the impact=“Fiscal Foresight” Problem

7

Most fiscal policy measures are pre‐announced.

Main idea

Estimate the effects of a “News Shock”

to public investment

= Changes in the public’s perception about the future course of the policy.

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Step 1: Construction of a news indicator

• Shioji and Morita (2017) constructed a dailyindicator which captures changes in people’s perceptions about future policy. This combines

–News approach (Ramey)

–Stock market approach (Fisher and Peters)

= Look at responses of stock prices of construction companies when major news about policy arrived.

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Step 2: Incorporate the news indicator into a time series analysis

‐‐‐ How??

• Previous paper: Put it into a regular VAR as another endogenous variable.

• This paper: Use this as the instrument in the VAR with External Instruments (VAR‐IV).

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2. On VAR‐IV

VAR‐IV

• Stock and Watson (2012), Mertens and Ravn(2013), Gertler and Karadi (2015)

• Survey paper by Stock and Watson (NBER‐WP24216, January 2018)

• Identification without exclusion restrictions.

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Identifying assumptions

• IV is correlated with the true shock contemporaneously.

• IV is orthogonal to the other types of shocks

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VAR‐IV: 2 variables, 1 lag example

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1,

2,

tt

t

yY

y

1t t tY AY

t tB

Reduced form VAR

Structural relationship

1,

2,

tt

t

Endogenous variables

Structural shocks(mutually orthogonal)

11 12

21 22

b bB

b b

VAR‐IV, continued

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1,

2,

t

t

11 12

21 22

b bb b

Suppose we are just interested in the first shock…

…then we just need to know the first column of B!

Assuming invertibility, ( )t tY C L B 1where ( ) ( )C L I AL

VAR‐IV, continued

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Assumption 1: “relevance”

11

21

then, t t

bE Z

b

1, 0t tE Z

Suppose we have an instrument Z which satisfiest

Assumption 2: “exogeneity”(wrt the other shocks) 2, 0t tE Z

Normalize to equal 1.We can focus on b21.

VAR‐IV, estimation

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2, 21 1, 1 1, 1 2 2, 1 22 2,t t t t ty b y d y d y b

Step 1: IV stage

Step 2: VAR stage

Using Zt as the instrument, estimate:

21ˆget b

1t t tY AY

Estimate the reduced form VAR:

-1ˆ ˆget ( ) ( - )C L I AL

VAR‐IV, Impulse responses

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Compute the h period ahead Impulse Response Function as:

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1ˆˆh hIRF Cb

Our case: Use the news indicator as an IV

• Our news indicator = Captures only a part of shocks to expectations about future policies.

– But it is correlated with true shocks to expectations.

– And it is uncorrelated with the other types of shocks.

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3. News Indicators

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Its construction: a rough sketch

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Identify the dates on which important news arrived.

Examine the reaction of construction companies’ stock

prices.

continued

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Companies that are more dependent on public procurements

Companies that are less dependent.

Study the difference in their responses.

Dependence on Public Investment = Share of Public work in Total (as of 2000)

230%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76

Cross‐group heterogeneity?Example from a big “news” event…

240%

5%

10%

15%

20%

25%

30%

35%

HIGH gov dependence group (>34%) LOW

Great East Japan Earthquake (March 11, 2011)sum of excess returns, March 14‐15

Two stock market indices

• Stock Mkt Index 1 = “High – Low”= (Avg of Upper Half) – (Avg of Bottom Half)

• Stock Mkt Index 2 = “G‐factor”– Extract 5 common factors ‐> Rotate them!– Target rotation: Select a rotation which gives the closest factor loadings to… (see next page)

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(1)Industry‐wide Factor

(2) Home Builders Factor

(3) G‐Factor(Gov. Dependence)

(4) Electric Facilities Builders Factor

(5) Plant Builders Factor

Mid‐sizedContractors

1 0 0/1 0 0

Big FourContractors

1 1 0 0 0

Home Builders (all big)

1 1 0 0 0

Electric Facilities Builders

1 0 0/1 1 0

Plant Builders 1 0 0/1 0 1

Target for rotation

Stock Mkt Index 1 & 2(and 0), Cumulative

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-1.5

-1-.5

0

01jan1990 01jan1995 01jan2000 01jan2005 01jan2010 01jan2015

Mean Excess Returns

-1.5

-1-.5

0.5


High (Blue) vs Low (Red)-.4

-.20

.2.4


High - Low

-60

-40

-20

020


G-Factor1 2

0

News indicator (1 & 2and 0)

Defined as

(News dates)*(Stock mkt index 1 or 2 or 0)

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News indicators (daily)

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News 0: based on the Mean Excess Returns-.0

50

.05

.1.1

5.2


News 1: based on "High-Low"

-.10

.1.2


News 2: based on "G-Factor"

-50

510

1520


News indicators (quarterly aggregates)

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News 0: based on the Mean Excess Returns-.1

0.1

.2

1990q1 1995q1 2000q1 2005q1 2010q1 2015q1

News 1: based on "High-Low"

-.10

.1.2

.3

1990q1 1995q1 2000q1 2005q1 2010q1 2015q1

News 2: basd on "G-Factor"

-10

010

2030

1990q1 1995q1 2000q1 2005q1 2010q1 2015q1

4. VAR‐IV analysis:specification and results

VAR‐IV

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IV = the news indicator

Endogenous variables = See the list on the next page

List of endogenous variables

• X1 =Stock Mkt Index 1 or 2(or 0)

• Construction orders from the public sector (top 50 companies)

• Nominal Public Investment (SNA)

• Public Investment Deflator (SNA)

• X5 = One of the macro variables (GDP etc.)

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Specifications

• All in log differences except for the news variables.

• # of lags = 4

• Dummies for the 3 major earthquakes & Consumption tax hike.

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X1 = “Stock Mkt Index 1”, X5 = Real GDP, IV =News 1

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X1 = Stock Mkt Index 2, X5 = Real GDP, IV =News 2

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For comparison:

X1 = Stock Mkt Index 0, X5 = Real GDP, IV =News 0

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5. Summary

• What we have done:– Proposed a new way to estimate effects of an anticipated shock to public investment.

• Combine stock market info and news.• Use VAR‐IV

• The identified shock has a positive and significant impact on GDP.

Impact elasticity = 0.2‐0.3→ Impact multiplier =2‐6! (too large?)

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Thank you!Your comments welcome!

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Appendix 1Details about the news indicator

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Literature (1) News‐based approach

• Ramey & Shapiro (Carnegie 1997), Ramey (QJE 2011): news about future US military spending.

• For Japan: Fukuda & Yamada (JJIE 2011): News on Emergency Fiscal Stimulus Packages.

• Drawback = No sense of magnitude or surprise

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Literature (2) Stock based approach

• Fisher & Peters (EJ 2010)–Excess return on four large military contractors in the US.

• Drawbacks = They are Contaminated signals.

• Morita (Ph.D. thesis, 2014)– Excess returns of the Construction Industry for Japan.– “Purified”measure based on SVAR.

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[1] News Analysis side: List of FP events

1. Extension of the Fukuda‐Yamada list of Emergency Stimulus Measures beyond 2010.

2. Reconstruction Budget after the Great East Japan Earthquake.

3. Important National Elections.

4. Natural Disasters (three earthquakes and a tunnel collapse).

5. Future Sports Events (Nagano, World‐cup, Tokyo)

6. “Negative” Fiscal Events (Hashimoto reform, Koizumi reform, “Shiwake”).

Identified 38 FP events; 159 dates.

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[2] Stock market side

• Original data: Construction industry’s 177 firms, listed on Tokyo Stock Exchange (1st or 2nd), at some point between 1974 and 2014.

• Returns = log difference of the close price.

• We regress them on the Market (TOPIX) return to obtain excess returns.

• Are they really informative? Let’s see…

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(a) Great East Japan Earthquake (March 14‐15, 2011)

0 20 40 60 80 100 120 140 160 180-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Ranking based on the total market value as of 2012 (if present). 46

Excess returns by firm

(b) Sasako Tunnel Failure (December 3‐5, 2012)

0 20 40 60 80 100 120 140 160 180-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

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(c) IOC gives the Olympics 2020 to Tokyo (Sept 9‐11, 2013)

0 20 40 60 80 100 120 140 160 180-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

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(d) FIFA gives World Cup 2002 to Korea/Japan (June 3, 1996)

0 20 40 60 80 100 120 140 160 180-0.2

-0.15

-0.1

-0.05

0

0.05

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(e) “Shiwake” (Nov 10‐27, 2009)

0 20 40 60 80 100 120 140 160 180-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

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How do we combine the two sides?

• Take a simple average? • But it may reflect all sorts of things.

• Instead, we take advantage of within‐industry heterogeneity.

• From here, data is limited to 76 firms that existed throughout the period 1990‐2014.

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52

0%2%4%6%8%

10%12%14%16%18%20%

HIGH gov dependence group (>34%) LOW

IOC announces Tokyo to hold the Olympics Gamessum of excess returns, Sept. 8‐10, 2013

Appendix 2Factor loadings

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54

‐0.4

‐0.2

0

0.2

0.4

0.6

0.8

1926

1929

1950

1822

1846

1805

1899

1898

1882

1938

1835

1814

1969

1881

1896

1944

1803

1883

1819

1959

1847

1983

1834

1925

1964

6366

Factor1 Factor loadings

More gov dependent Less gov dependent

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‐0.4

‐0.2

0

0.2

0.4

0.6

0.819

26

1929

1950

1822

1846

1805

1899

1898

1882

1938

1835

1814

1969

1881

1896

1944

1803

1883

1819

1959

1847

1983

1834

1925

1964

6366

Factor2

Taisei Kajima ObayashiShimizu

Daiwa

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‐0.4

‐0.2

0

0.2

0.4

0.6

0.819

26

1929

1950

1822

1846

1805

1899

1898

1882

1938

1835

1814

1969

1881

1896

1944

1803

1883

1819

1959

1847

1983

1834

1925

1964

6366

Factor3

Sata

Appendix 3More IRFs

(X1= High‐Low)

57

X5 = Real consumption

58

X5 = Real Business Investment

59

X5 = GDP Deflator

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X5 = Nominal GDP

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