Investement Behavior of chinese firms

8/14/2019 Investement Behavior of chinese firms

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ed in an abrupt economic decline, a deeper understanding of such extreme cyclical downturns remains a high priority for policy makers and academicians alike. Any standard macroeconomics textbook would stress the simple insight that investment is the most volatile component of GDP, and is therefore, central to understanding the degree and nature of output fluctuations. To the extent that economiessuffering from recent financial crises also experienced a dramatic investment collapse, a careful examination of the investment behavior of firms is doubly usef

ul.A substantial theoretical advance in the literature of investment during the last decade pertains to the role of uncertainty as a critical determinant of investment. In particular, the real options literature in investment has underscored that uncertainties facing a firms environment can deter investment, as forcefully argued by Dixit and Pindyck (1994). These new insights have sparked much theoretical work on the relation between uncertainty and investment, but a central feature of this work is the considerable ambiguity on the long-run effects of uncertainty on investment. Particularly, there is little consensus on whether uncertainty deters or promotes investment.In the face of such theoretical ambiguities, empirical evidence on the links between uncertainty and investment remains surprisingly limited. Theoretical litera

ture provides an extensive discussion about the relationship between firm investment decisions and uncertainty. Yet, it concludes that a number of factors including; model specification and the underlying assumptions, market competition andinvestor attitude, the technology involved and the production function may cause uncertainty to either raise or lower the investment. Most empirical studies investigating the relationship find that uncertainty reduces investment. Recent reviews on investment have alluded to this dearth of evidence. As Bond and Jenkinson (1996) note: Agreement on the effects of uncertainty on the level of investment remains elusive. In contrast to the literature on financial constraints, empirical work in this area is still notably scarce. Similarly, Pindyck (1991) observed: The existing literature on these effects of uncertainty and instability isa largely theoretical one...the gap here between theory and empiricism is disturbing.

Whatever the limited evidence is available in this area relates to developed countries, with hard econometric evidence notoriously lacking for developing and emerging market economies. This is a serious omission given that developing countries suffer from greater output fluctuations and, if anything, uncertainty is likely to be a much more important concern in these economies. These economies aregenerally more volatile by nature and information problems in these economies are more prevalent due to deficient markets and institutions. Usually less diversified macro and micro activities increase the likelihood of more adverse shocks.Moreover, information problems in these economies are more prevalent due to deficient markets and institutions. Additionally, because of the rapid transition toa market economy these economies are introducing many policy changes, both at macro and micro level. The studies related to the developing countries focus on aggregate relationships. For instance, recent evidence on aggregate investment has established that socio-political instability can be an impediment to aggregateinvestment (Campos and Nugent, 2003). Similarly, a greater policy uncertainty as manifested in a higher volatility of real effective exchange rates, governmentexpenditures, and nominal money growth hinders private investment in developingcountries (Aizenman and Marion, 1999). Despite this mounting evidence on the effects of various types of uncertainty on aggregate investment levels, the corresponding microeconomic evidence at the level of the firm remains insufficient.A couple of studies using the data from developing or transition economies are Pattillo (1998), Lensink and Sterken (2000) and Bo and Zhang (2002). Yet, investigating the investment-uncertainty relationship seems to be particularly relevantin the context of these economies. The main goal of this research is to fill this empirical deficit by gauging the investment effects of uncertainty in an impo

rtant emerging market economy during a period of its economic boom. To be specific, this dissertation establishes a few stylized facts on the relation between uncertainty and investment using a panel of Chinese firms during the period (1994


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-2005). Given the significant growth of emerging market economies in the last two decades, a special focus on China seems to be relevant. Another practical advantage of solely focusing on China is the availability of company accounting information for a large number of stock market quoted firms. Importantly, high frequency data on share prices, a critical ingredient in computing a measure of uncertainty is now more easily accessible for Chinese firms.China, worlds 4th biggest economy and the 3rd largest exporter, has an active s

tock market. If compared in terms of stock market capitalization, it is the 2ndlargest stock market in the Asia as Japan being the 1st. Currently; there are more than 1300 publicly listed firms and approximately 67 million individual investors participating in both of the stock markets. Trading has been tremendously rational and annual turn over ratio for our sample period (1994-2005) averages approx 500%, the highest in comparison to other major stock markets in the world.Provided, such stock market volatility reflects some fundamental influences, thecorresponding variation in share prices can be meaningfully used to construct an informative measure of uncertainty. With the dramatic economic developments since early 1990s, the estimation period covered in this study is independently interesting. In contrary to other East Asian economies, China is still witnessinga period of high economic growth and pronounced lending boom. Almost for two de

cades, it has maintained her annual real income growth above ,,,,,%.Based on this particular data set, I explore two contingent issues. Firstly, theuncertainty effect on firm level investment by using a dynamic investment model. In other words, whether the stock market volatility deters or spurs the investment decision? Secondly, the econometric issue of estimation in the presence oflagged dependent variables and the explanatory variables which are not strictlyexogenous thus combating with the endogeneity issue.

To investigate the uncertainty impacts on investment, we specify an error correction formulation, introduced in the investment literature by Bean (1981) and developed more systematically for the microeconomic literature on investment in Bond et al. (2003). In order to overcome the endogeneity problem study then appliesboth GMM (DIFF) GMM (SYS) techniques.

The basic empirical findings are: On one hand, the uncertainty is found to havea highly significant and positive influence on firms investment decisions. In addition, the results of sample splits into manufacturing and non-manufacturing and large and small firms show that, the uncertainty has particular strong positive impact on non-manufacturing and large firms. In fact, the real effects of uncertainty are found to be even more pronounced for large firms and they have greater potential to grow as compared to smaller firms. On the other hand, as the reported estimation results show that a greater efficiency gain is achieved by applying the System GMM estimation which combines the equations in levels form, inaddition to, differenced transformed equation. One should be careful as it prolifically generates the number of instrument variables to be used but the validityof instruments used can be tested by Sargan (1958) test.These findings could be used to strengthen the view of how investors make judgments in the face of uncertainty. However, to summarize results from this currentstudy of the firm level investment in China, I find that, the Chinese stock market volatility has an increasingly important role in allocating investable resources; the current investment attitude in China is likely to produce inefficient allocation of resources in some particular sectors and to cause the harmful effects on the real economy.

The study provides a number of important policy implications. First, atthe firm level, and too much reliance on the stock market should be discouraged,so that, firm can make more prudent investment decisions. Second, at least in the very near future, the stock market in China is likely to continue its important role in allocating resources; therefore, sensible regularity measures must be

taken in order to contain the detrimental real effects of an unregulated stockmarket expansion. Third, at the aggregate level, too much faith in the positiveeconomic contribution of the stock market might be misguided.


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The rest of this dissertation is organized as follows; A literature review is provided in Chapter 2. I begin with an overview of the general relationship between uncertainty and investment. I provide some background information of micro structure, economic reforms, emergence and development of stock market in China. Ialso reviewed relative research efforts on understanding the Chinese stock market. In chapter 3, I provided a detail literature about panel data and differentestimation techniques, especially dynamic panel data and System GMM and Diff GMM

estimation techniques. In Chapter 4, I gave an introduction about Investment Models. In Chapter 5, I first provided the theoretical frame work upon which the subsequent empirical estimations are drawn. Second, I presented econometric specifications and discussed the estimation methodology. Then, I provided a detail about key important variable i.e measure of uncertainty. In order to fully examine the uncertainty dynamics, econometric estimations are carefully conducted based on a panel data set I collected and constructed of Chinese listed firms. At the end of Chapter 5, I provide an introduction to this data set and then list some descriptive statistics of the key regression variables Detailed econometric analysis of main issue are carried out in section 5.? .The last Chapter 6, concludes.

Chapter 2: literature ReviewThe standard neo-classical models of investment have produced rich insights butthey have fared rather poorly in empirical practice. In particular, the standarddeterminants of investment emphasized by the neo-classical investment models seem to have met with limited empirical success. This has inspired a series of important extensions to the study of investment behavior, principal among which arethe role of internal finance considerations and uncertainty in influencing investment behavior. A prominent line of investigation in this regard relates to theconnection between uncertainty, irreversibility and investment. It argues thatuncertainty in a firms environment, whether it is due to volatility of aggregate demand or unpredictability of prices and costs, can impede investment in fixedcapital. The study of investment under uncertainty has been a subject of much academic interest recently. This chapter will briefly survey the key theoretical

and empirical insights of this literature.In the view of Hartman (1972) and Abel (1983) a greater uncertainty in the situation when the marginal product of capital is convex spurs the investment. A higher out put variance with fixed mean boosts the expected profitability and thus leading to a higher investment. Given the labor to be more flexible than the capital, firms may adjust labor in response to price variations and amending the labor-capital ratio and thus changing the marginal product of capital more rapidlythan the price movement. Traditionally, it has been contended that uncertaintyin a firms environment is reflected in the behavior of Q, and consequently oncethe Q effects are controlled for, uncertainty has no additional effect on investment (Abel, 1983). A major challenge to this view comes from the influential theoretical work of Dixit and Pindyck (1994), who demonstrated that uncertainty could be a significant deterrent to a firms decision to invest. These effects arise in a situation when investments in fixed capital involve considerable sunk costs which can only be partially recovered through disinvestment. Implicit in this is the idea of an inherent asymmetry in the adjustment of capital stock: downside adjustment of capital stock is easier than the upward adjustment. Investmentin this sense is considered to be irreversible. Bar-Ilan and Strange (1999) report that even if the investment is considered to be irreversible the uncertaintycan have a positive effect and it can be the situation when the intensity of investment is considered. Generally the higher the degree of irreversibility the greater is the likelihood of a negative relation between investment and uncertainty.Under sunk costs and irreversibilities, firms facing an uncertain environment can be reluctant to invest. In the jargon of real options literature, firms that a

re faced with uncertain rewards have an option to invest now or delay the investment until they receive new information on prices, output, and costs. Under irreversibility, firms expanding today consider the impact of future uncertainty, in


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particular, the likelihood that they may be stuck with excess capital or low returns. An investment opportunity is thus viewed as an option. Investing today eliminates this option and imposes an additional cost. It is suggested that the net-present value should then be modified to account for this opportunity cost thecost of forgone option. The effect of this uncertainty is to increase the option value of waiting (opportunity cost); with the possibility that firms facing anuncertain environment may decide to postpone their investment decisions. The up

shot is that in the presence of irreversibilities, uncertainty is predicted to have adverse effects on capital formation. Extensive reviews of this and relatedliterature are provided by Pindyck (1991), Hubbard (1994), Serven (1996), and Bond and Van Reenen (2003). In the spirit of Greenwald and Stiglitz (1990), Ghosaland Loungani (2000) relate the negative link of investment and uncertainty to the extent of capital market imperfections.Recent theoretical innovations, however, tend to cast doubt on some of these assertions. In an important contribution, Caballero (1991) argues that asymmetric adjustment costs alone are insufficient to produce a negative relationship between uncertainty and investment. Instead, the critical factors in signing this relationship are the assumptions concerning market structure and returns to scale. More specifically, it is mainly under decreasing returns to scale and imperfect c

ompetition that higher uncertainty deters investment. Thus, even under irreversibility higher uncertainty can lead to a rise in investment, provided the marketstructure is one of constant returns to scale and perfect competition.Even if the short-run impact of uncertainty is to reduce investment, the long-run impact remains ambiguous. Theoretically, it is not clear if the relationship between uncertainty and investment should be negatively signed. Depending on themodeling environment and the specific assumptions, uncertainty may lead to either a fall or a rise in investment. In principle, it is possible to envision a situation where a higher level of uncertainty is associated with a higher capital stock on average (Abel and Eberly, 1999 and Caballero, 1999). Abel and Eberly (1999) discern between short-run and long run effects of uncertainty when investment is irreversible. The short-run effect is termed as the user cost effect andthe long-run effect is termed as the hangover effect. On the one hand, uncerta

inty increases the user cost of capital, which implies a negative effect of uncertainty on investment. On the other hand, when facing unfavorable states of nature, e.g. a fall in demand, the firm is not able to disinvest because of irreversibility. Therefore, the hangover effect implies a higher level of capital stockin the long run. Since the user cost of capital and hangover effect work in opposing directions at the same time the overall effect of uncertainty on investmentis ambiguous. If there is greater capital accumulation during good times, firmsmay be stuck with too much capital during bad times, with the result that the average capital may be high despite irreversibilities.The extant theoretical literature is not only ambiguous in signing the relationship between uncertainty and investment; it is also less precise in pinning downthe channels that mediate this relationship. Recent research has, however, underscored the role of such factors as the degree of irreversibility, market structure, and risk aversion. An important explanation for a negative link between uncertainty and investment relates to the idea that capital market imperfections create non-linearities in the investors inter-temporal budget constraint. For individual investors, the presence of a credit ceiling can deter investment in goodtimes without mitigating the drop in bad times. As a result, higher volatility could be associated with lower investment (Aizenman and Marion, 1999).The theoretical ambiguities on the link between investment and uncertainty underline the need for robust empirical evidence. On the empirical side, testing forthe effects of uncertainty on investment is fraught with several challenges, however. For one, the analytical complexity of theoretical models does not easily lend itself to empirical testing. In particular, there is little consensus on theappropriate empirical framework for testing the link between uncertainty and in

vestment. Another difficulty relates to constructing a meaningful measure of uncertainty that corresponds well to its theoretical construct. Notwithstanding these challenges, whatever limited evidence is available in this area tends to poin


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t towards a negative relationship between uncertainty and investment.In evaluating the effects of uncertainty on investment, empirical work in this area has tended to rely on micro data from firm-level establishments. Using dataon seven thousand US manufacturing plants, Caballero et al. (1995) establish thenon-linear pattern of adjustment of capital stock to its optimal level. The availability of panel data on individual companies has facilitated a more systematic exploration of the theoretical predictions on uncertainty. Using micro data on

US corporations and an asset returns-based measure of uncertainty, Leahy and Whited (1996) demonstrate that uncertainty has an adverse effect on investment only in the absence of Tobins Q. These results support the assertion that there are no independent effects of uncertainty on investment over and above the effectsof Q. Nilsen and Schiantarelli (2003) employ a discrete hazard model on Norwegian data to establish a convex adjustment cost function and indicate the presenceof fixed costs of investment. The adverse impact of firm-level uncertainty on investment has been similarly established in the Italian context by Guiso and Parigi (1996).Using panel data on Ghanian manufacturing firms, Pattillo (1998) estimates investment thresholds and confirms a negative relationship between uncertainty and investment. Employing a simple reduced form erorr-correction model, Bloom, Bond an

d Van Reenen (2001) investigate the link between investment and uncertainty using panel data for UK firms. The main empirical result of this study is that demand shocks have a smaller effect on current investment in the presence of higher levels of uncertainty. A key methodological limitation of the reduced-form modelsis their failure to systematically control for expected future profitability. Bond and Cummins (2004) offer a possible remedy by offering a better control forexpected future profitability. In particular, they replace average Q with a moredirect measure based on analysts forecasts. Based on annual data for publicly traded US companies and a Q-measure based on analysts forecasts, Bond and Cummins (2004) show that a higher level of uncertainty is associated with lower investment levels.Economic theories could not succeed in predicting the exact functional relationbetween uncertainty and investment. Most of the studies report a negative while

few evidences support a positive relation but there are others who suspect thatit can be nonlinear. Recently Hong and Robert (2005) report the uncertainty andinvestment relation to be nonlinear of inverted U type shape by using a panel ofDutch firms. A lower level of uncertainty up to a threshold level my increase the investment but a higher level of uncertainty after the threshold level my reduce the investment. As is clear from this short review, apart from a handful ofstudies, there have been relatively few attempts at empirically assessing the relation between uncertainty and investment in developing countries. Against thisbackground, we will explore a connection between uncertainty and investment using a sample of Chinese firms.

2.3 Economic Reform and Stock Market Development in ChinaThis chapter provides some background information on the stock market development in China and a description of the microstructure of the Chinese stock market.In addition, this chapter reviews the related research

2.3.1 Economic ReformChina initiated economic reforms in 1978. The reforms process was slow,

gradual and consisted of different phases, aimed to improving the efficiency ofmicro-management institutions by providing some incentive mechanisms. On the international front, the government adopted an open-door policy to promote trade and attract foreign investment. Markets for goods and resources were established and prices were liberalized. More market-oriented reforms were introduced to limit the scale of planning in the economy and to increase the degree of autonomy of

the micro-economic units.Virtually there was no privatization in China before the official commencement of reform and opening. However, since the mid 1990s, China has been straying fro


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m its centrally planned and state-directed policies. The government officially abandoned the concept of planned economy and declared the establishment of a full-fledged socialist market economy. This notion of socialist market economy legitimized the privatization of the state-owned sector. The government has stepped up its privatization effort and has increasingly implemented extensive marketdominated reform measures. Since the reforms in 1978, the financial system hasbeen increasingly diversified and open to competition. Enterprises were granted

more autonomy in making production and investment decisions. Nevertheless, evidence seems to suggest that this policy shift since the mid 1990s might have backfired. Prior to reforms, Chinas financial system was a nonbanking practice withthe state-owned and controlled Peoples Bank of China (PBC) virtually handling all financial operations. Interest rates were partially liberalized and in 1994 the Chinese currency was made convertible on the current account. In 1998, state-owned banks ceased allocating credits according to the credit quota system set by the State Planning Commission.Chinas peaceful and phenomenal transition to the privatization and capitalism kept on going and by the end of year 2001, it got the membership of the WTO (World Trade Organization). It gave a further impetus and pledging more openness to international trade and further liberalization of the financial sector. With the

membership in WTO the foreign banks were allowed to have business in the mainland China.2.3.2 Emergence and Development of Stock market in ChinaThe emergence and establishment of the stock market is one of the most importantinstitutional changes in Chinas financial system. The socialistic China decided to take the full advantage of all forms of institutions including the stock market while overcoming its negative effects. This prompted China to march forward and make socialist use of stocks and bonds. The Shanghai Stock Exchange (SHSE) was officially established in December 1990 and the Shenzhen Stock Exchange(SZSE) opened on July 1991. Since then, the government has been increasingly promoting the stock market, and as a result, the stock market has been expanding rapidly in terms of the number of firms listed, total market capitalization, and the turnover ratio. The successful endorsement of the stock market proved to be d

oubly useful. First, the promotion of the stock market, which is often considered as an ultimate symbol of capitalism, reflects the overall policy shift to endorse more privatization and implement more market-based reforms. Second, the stock market proved to be a handy source of raising funds for SOEs, an expedient for improving their governance structure and efficiency, and for effectively directing investment incentives.The growth and development of stock market in China has been very profound andrapid. The market has enlarged in terms of the firms listed; as compared to the10 companies in

Table 2.3.1 Main indicators of the Chinese Stock Market (1994-2005)

yearNumber ofFirmsNominalGDP Total MarketCapitalization Market Capitalization ofNegotiable Shares Total Trading Volume(Turnover)

Volume % of GDP Volume Volume1994 287 48197.9 3690 7.89 969 81281995 311 60793.7 3474 5.94 938 4036


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1996 514 71176.6 9842 14.50 2867 213321997 720 78973.0 17529 23.44 5204 307221998 825 84402.3 19521 24.52 5745 235271999 922 89677.1 26471 31.82 8214 313202000 1060 99214.6 48090 53.79 16088 608272001 1140 109655.2 43522 45.37 14463 383052002 1213 120332.7 38329 37.43 12484 27990

2003 1277 135822.8 42457 36.38 13179 321152004 1363 159878.3 37055 27.14 11689 423342005 1381 183084.8 32430 17.8 10630Note: (RMB Yuan: 100 Million)1990 the total number of listed companies has reached up to 1381 by the end of 2005. Only the best performing, well functioning and big companies are permittedby CSRC to be listed. Manufacturing firms constitute a majority of all listedfirms (a little over 60%) as is also evident from our sample . The total marketcapitalization has been very encouraging. If compared in terms of market capitalization then China ranks second in the Asia as Japan being the first (Economist,2nd June, 2003). It exceeds the size of the Hong Kong market, is twice as largeas that of Taiwan, and it triples the size of that of South Korea. The total ma

rket capitalization in the year 2007 reached up to 21 trillion Yuan (2.77 trillion U.S dollars) in comparison to 3 trillion Yuan in the year 2005. The trading has been extremely rational but rather highly volatile; the annual turn over ratio in most of the years has been in excess to the other major stock markets in the world. The annual average turn over ratio for our sample period of 1994-2005 is approx 500%, the highest in the World.Table 2.3.2.2 Turnover Rates(Percent) Of Major Stock Exchanges ,(1994-2005)Stock Exchange 1994 1995 1996 1997 1998 1999 2000 20012002 2003 2004 2005 AverageShanghai 1,135 529 913 702 454 471 493 269214 251 289 274 500Shenzhen 583 255 1,350 817 407 424 509 228198 214 288 316 467

New york 53 59 52 66 70 75 88 8795 90 90 99 77Tokyo 25 27 27 33 34 49 59 60 6883 97 115 56London 77 78 58 44 47 57 69 84 97107 117 110 79Hong Kong 40 37 44 91 62 51 61 4440 52 58 50 53Singapore 28 18 14 56 64 75 59 9954 74 61 48 51

Currently the stock market is hosting more than 1,600 trading seats, the SHSE issaid to have the largest trading floor in the Asian and Pacific area. The stockmarket has also allured an enormous amount of investors. In 1992, there were 2million registered investors. By the end of 1999, the number shot up to 45 million but in the year 2007 the number crossed the barrier of ??????? A high percentage of annual turn over ratio can be related to such a great number of market participants who keep the stock for a very short period of time. The stock maniain China has also caused the market index to witness certain ups and downs in its limited time period . The stock fever in China has resulted in unprecedented high levels of market valuations as well as excessive price volatility. Althoughsock prices overall have appreciated significantly, the market has already experienced many boom-and-bust cycles. Most of the time the market has been driven through arbitrage, short term and frequent trading which caused the share prices to be volatile. Our measure of uncertainty, calculated as the annual standard dev

iation of daily returns reveals the story and as shown in the figure (?????) andalso in the table (????) the average volatility of share prices is highest among all the variables.


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2.3.3 Research efforts related to ChinaDespite of the significant growth and rapid development of Chinese stock marketthere have mot not been much studies about its empirical consequences, especially in reference to determining the firm level investment. Internationally there have been only few efforts, perhaps because of no information disclosure or someother issues and language barrier is also of them. Most of the available research material is in Chinese language, but they have also considered some other conc

erns and do not provide any tangible evidences.Especially the research efforts regarding the firm level investment are limitedbecause even in the recent past it was managed by the government. Jefferson, Huand Singh (1999) make an impressive effort to study the investment activity of Chinese industrial firms. They focus on two aspects of firm behaviour. First, they study whether investment decisions of Chinese firms are guided by the profit-seeking and the value-maximizing principle. Second, they investigate whether theinvestment behaviour varies across firms of different ownership types (i.e. state-owned, collectively owned or joint-ventures). Using a data set from the WorldBank (for the time period of 1986-1990) and another data set collected from Chinese Statistical Yearbook (for the period from 1986 to 1995), they find evidenceof considerable efficiency in industrial investment in China. They report that

regardless of ownership types, firms make investment decisions in a manner consistent with neoclassical profitability and availability of internal funds. In addition, interestingly, they find the state owned enterprises in fact respond toprofitability and cash flow to a larger degree than firms of other ownership types.Some studies discuss the early developments of stock market and the prevailing speculations (Hertz, 1998; Li and Wong, 1997). For instance, Li and Wong (1997) describe the development of stock exchanges and investors trading activities fromthe start to 1995. They observe that Chinese investors tend to bet on government policies instead of making informed judgment based on the fundamentals.Given a unique kind of ownership structure some studies have been made to investigate its effects on the performance of listed companies. Xu and Wang (1997) study the performance of listed companies over the time period of 1993 to 1995. The

y define ownership structure as both the ownership mix and the ownership concentration and, introduce three??????????????? measure of performance, i.e., the market to book value of equity, returns on assets (ROA; after-tax profits divided by book value of equity). While controlling for other determining factors of performance such as sales, profit growth, and debt-to-asset ratios, they report a significant positive correlation between firm performance and institutional shares, while on the other hand there is no significance between A shares and firm profitability. More over, state ownership has a negative effect on firm performance. So the policies must be implemented to restrict the state shares and to encourage the portion of institutional sharers in order to increase the efficiency and healthy profits. Similarly, a more recent study by Chen and Lin (2000) investigates the effect of ownership mix on firm performance (as measured by earnings per share as well ROE) for the year 1997???????. They also reported that there isa positive link between the proportion of institutional shares and firm performance while tradable shares do not have significant impacts on firm profitability.

Among the studies analyzing the macroeconomic economic aspects of Chinese stockmarket, Wang (2002) employs the cross-country regression methodology of King andLivine (1993c). His analysis suffers because of short period sample (1993-1999), any how he could not report any significant relation between growth indicatorsand stock market development. Some studies used the CAPM and APT model to magnify the risk and return structure in Chinese stock market (Su and Fleisher, 1998;Yuenan and Amalia, 2007). Nicolaas et al. (2003) studied the efficiency of theChinese stock market and the role of the banks for the period 19922001 by using

efficient markets hypothesis. Ying et al. (2004) applied the new risk management tool, Value at Risk (VaR) methodology, to the stock market in China. These studies however, do not provide any conclusive results as either the Chinese stock


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market is significantly efficient or not. Cheng and Oliver (2000) examined empirical contemporaneous and causal relationships between trading volume, stock returns and return volatility in Chinas four stock exchanges and across these markets.For instance, Leung and Wong (1997) study the performance of daily stock indicesfor the period from September 1992 to August 1994. After conducting tests for autocorrelation, cointegration, and causality, they conclude that the Chinese sto

ck market is inefficient in the week form. On the other hand, using the GARCH-M(1,1) specification, both Song, Liu, and Romilly (1998) and Su & Fleisher (1998)also study the effect of government policies on stock market volatility. They report that spikes of volatility are associated with changes in government regulations and therefore they conclude, Governments market intervention policieshave affected stock-market volatility in China (Su and Fleisher, 1998: 239). Anumber of studies tried to unveil the speculative characteristic of Chinese stock market and others tried to differentiate between A shares and B shares ( Fernald and Rogers, 2000: Li, 2001; Yang,2001)Recently (Wang, Y. et al, 2 006) conducted an empirical study to investigate thelink between firm-level investment and stock market valuations in China. They report that the Chinese stock prices contain very little information about future

operating performance over the current fundamentals. A possible reason of deficient information is that Substantial proportions of the shares of many listed firms are owned by the state, and they cannot be traded freely. The quality of listed firms is poor, which is reflected by poor profitability and poor corporate governance (Allen et al., 2005a,b). Market manipulation, including trading basedand information-based manipulation, is severe because of the weak legal system (Chen and Zhou, 2002).In fact the studies conducted to the date portray only anecdotal and tangentialaspects rather than any fascinating or rigorous views.

Chapter 3: Panel DataPanel data refers to the pooling of observations on a cross-section of household

s, countries, firms or individuals over several time periods thus providing multiple observations on each individual in the sample.The use of panel data in economic research provides several major benefits overconventional cross-sectional or time series data sets, see Hsiao (1985,1986). These include controlling for individual heterogeneity, availability of more informative data and the improved ability to study adjustment dynamics.A key econometric problem arising in empirical studies is the inability to control for individual specific effects (unobserved or mismeasured) which may be correlated with other included variables in the specification of an economic relationship. As panel data utilizes information both on the intertemporal dynamics andthe individuality of the entities being investigated, it allows more scope to control for the effects of missing or unobserved variables.

Through the availability of a large number of data points, in micro data, the degrees of freedom are increased and the collinearity among explanatory variablesis reduced, leading to an improvement in the efficiency of econometric estimates. Time-series studies on the other hand, often tend to suffer from multicollinearity. Also, as panel data are gathered on micro units, like individuals, firms and households, some variables may be more accurately measured at the micro level. However biases resulting from aggregation over individuals or firms are eliminated.

Apparently stable cross-sectional distributions conceal a number of adjustment dynamics in the model which can be better studied using panel data. For example,in economic models, unemployment spells, job turnover, residential and income mo

bility, panels would be much more suited to the study of the duration of economic states of unemployment or poverty. Panel data sets are necessary for the analysis of intertemporal relations, overlapping generations and lifecycle models thu


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s enabling research on more complicated behavioral models.

Applied econometric research is increasingly making use of panel data sets partly because it is becoming more widely available and partly because computer technology makes it relatively easy to do things now that were relatively difficult to do some years ago due to difficulties of handling big datasets.

One of the useful applications of these data sets is that they can be used to estimate dynamic models. Bond (2002), notes that dynamic models are of interest ina range of economic applications. They can be used for example in Euler equations for household consumption, adjustment cost models for firms factor demand and empirical growth models. In some cases allowing for dynamics can be critical in recovering consistent estimates of other parameters in the model even when thecoefficients on lagged dependent variables are themselves of primary interest.

A commonly used method of estimating dynamic models particularly in panels whereN is large and T is small is to apply instrumental variable estimators of the kind suggested by Anderson and Hsiao (1981), Arellano and Bond (1991) and Blundelland Bond(1998). These methods are well known to have desirable asymptotic prope

rties and a number of studies have looked at their small sample properties usingMonte Carlo methods, including Arellano and Bond (1991) and, Blundell and Bond(1998). Almost without exception, these Monte Carlo studies have used rather artificial designs which have not been closely related to any particular empiricalcontext or empirical applications.3.1 Benefits and Limitations of panel data

3.1.1 Benefits of using the Panel data.(1) Controlling for individual heterogeneity. Panel data suggest that individuals, firms, states or countries are heterogeneous. Time-series and cross-section studies not controlling this heterogeneity run the risk of obtaining biased results, e.g. see Moulton (1986, 1987).(2) Panel data give more informative data, more variability less collinearit

y among the variables, more degrees of freedom and more efficiency. Time-seriesstudies are plagued with multicollinearity; for example in the case of demand for .. In fact, the variation in the data can be decomposed into variation between different cross sections of various/different sizes and characteristics,and variation within cross sections/states. The former variation is usually is usually bigger. With additional, more information data one can produce more reliable parameter estimates. Of course, the same relationship has to hold for each cross section/state, i.e. the data have to be poolable and this is a testable assumption.(3) Panel data are better able to study the dynamics of adjustment. Cross-sectional distributions that look relatively stable hide a multitude of change. Panel data are also well suited to study the duration of economic states like unemployment and poverty , and if these panels are long enough.(4) Panel data are better able to identify and measure effects that are simply not detectable in pure cross-section or pure time-series data.(5) Panel data models allow us to construct and test more complicated behavior models than purely cross-section or time-series data .For example technical efficiency is better studied and modeled with panels (see Baltagi and Griffen, 1998b ; Cornwell, Schmidt and Sickles, 1990; Kumbhakar and Lovell, 2000; Baltagi,Griffin and Rich, 1995; Koop and Steel, 2001). Also fewer restrictions can be imposed in panels on a distributed lag model than in a purely time series study (see Hsiao, 2003).(6) Micro panel data gathered on individuals, firms and households may be more accurately measured than similar variables measured at the macro level. Biases resulting from aggregation over firms individuals may be reduced or eliminate

d (see Blundel, 1988; Klevmarken, 1989). For specific advantages and disadvantages of estimating and disadvantages of estimating life cycle models using micropanel data , see Blundell and Meghir (1990).


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(7) Macro panel data on the other hand have a longer time series and unlikethe problem of nonstandard distributions typical of unit roots test in time-series analyst,3.1.2 Limitations of Panel data(1) Design and data collection problems. For an extensive discussion of problems that arise in designing panel surveys as well as data collection and data management issues see Kasprzyk et al. (1989). These include problem of coverage (

incomplete account of the population of interest), non response (respondent notremembering correctly), frequency of interviewing, interview spacing, referenceperiod, the use of bounding and time-in-sample bias (see Bailar, 1989).(2) Distortions of measurement errors. Measurement errors may arise becauseof faulty response due to unclear questions, memory errors, deliberate distortion of response (e.g. prestige bias), inappropriate informants, misrecording of responses and interviewer effects (see Kalton, Kasprzyk and McMillen, 1989)(3) Selectivity problems. These include;a. Self-selectivity. People choose not to work because the reservation wageis higher than the offered wage. In this case we observe the characteristics ofthese individuals but not their wage. Since only their wage is missing, the sample is censored. Information from this truncated sample introduces bias that is

not helped by more data, because of the truncation (see Hausman and Wise, 1979).b. Nonresponse. This can occur at the initial wave of the panel due to refusal to participate, nobody at home, untraced sample unit, and other reasons. Item (or partial) nonresponse occurs when one or more questions are left unansweredor are found not to provide a useful response.c. Attrition. While nonresponse occurs also in cross-section studies, it isa more serious problem in panels because subsequent waves of the panel are still subject to nonresponse. The degree of attrition varies depending on the panelstudies; see Kalton, Kasprzk and McMillen (1989) for several examples. In orderto counter the effects of attrition, rotating panels wave to replenish the sample. A special issue of the Journal of Huaman Resources, Spring 1998, is dedicatedto attrition in longitudinal surveys.(4) Short time-series dimension. Typical micro panel involve annual data cov

ering a short time span for each individual. This means that asymptotic arguments rely crucially on the number of individuals tending to infinity. Increasing the time span of the panel is not without cost either. In fact, this increases thechances of attrition and increases the computational difficulty for limited dependent variable panel data models.(5) Cross-section dependence. Macro panels on countries or regions with longtime series that do not account for cross-country dependence may lead to misleading inference.Panel data is not a panacea and will not solve all the problems that a time series or a cross section study could not handle. Collecting panel data is quite costly, and there is always the question of how often one should interview respondents. Pay off from panel data is over long time periods, five years, ten years, or even longer.Griliches (1986) argued about economic data in general, the more we have of it,the more we demand of it. The economist using panel data or any data for that matter has to know its limitations.

3.2 The static panel data modelThe identification of time-series parameters traditionally relied on notions ofstationarity, and uncorrelated shocks. The identification of cross-sectional parameters appealed to exogenous instrumental variables and random sampling for identification.By combining the time-series and cross-sectional dimensions, panel data sets hav

e enriched the set of possible identification arrangements . A static panel datamodel can be used to take account of heterogeneity across individuals and/or time.


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(3.1)Where the numbers of observations are , and the data is stacked first over time periods for each individual i,

(3.2)and then stacked again across N individual,

(3.3)This allows the model to be rewritten as follows:

(3.4)3.2.1 Fixed effect model

An initial interpretation for the model in (3.1) would be to treat as a dummyvariable to allow for the effects of those omitted variables that are specific to individual cross-section units but stay constant over time. Assuming T to be large, the value of the dependent variable for the unit at time , , would depend on K exogenous variables measured by , that differ among individuals in a cross-section at a given point in time and also exhibit variation through time, aswell as on variables that are specific to the unit and that stay constant over time measured by . With fixed T we need the stronger assumption of strict exogeneity for ordinary least squares estimates discussed below to be consistent a

s . Strict exogeneity assumes that is uncorrelated with and can be characterized by an independently identically distributed random variable with mean zeroand variance . The above mentioned relationships can be depicted by the following model:

(3.5)where is a vector of constants and is a scalar constant representing the effects of those variables particular to the individual. The error term represents the effects of the omitted variables that are peculiar to both the individual units and time periods.Given the above mentioned properties of , the ordinary-least squares (OLS) estimator of (3.5) is the best linear unbiased estimator (BLUE). The OLS estimators of and are obtained by minimizing

(3.6)

Taking partial derivatives of M with respect to and setting them equal to zero,results in

(3.7)Substituting (3.7) into (3.6) and taking the partial derivative of M with respect toGives

(3.8)Where,

(3.9)and since in this model the observed values of the variable for the coefficient

takes the form of dummy variables, is the least-squares dummy-variable (LSDV) estimator . The formulation in (3.5) is referred to as the analysis-of-covariance model and so the LSDV estimator is sometimes referred to as the covariance estimator. It is unbiased and also consistent when either N or T or both tend toinfinity.However, if the true model is fixed effects as in (3.5), OLS on the model whereare unobserved or not explicitly included would yield biased and inconsistent

estimates of the regression parameters in the case where .This estimator referred to as the pooled OLS estimator, given by:

(3.10)is inefficient even if . This is because the error terms in the model would becorrelated over time periods,

(3.11)This would occur due to the presence of no matter what restrictions are placedon

This bias can be eliminated by using techniques involving instrumental variablesthat are discussed later or by the classic approach, which is to undertake a Within transformation.


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arameters is avoided only at the risk of introducing bias. Consider a reformulation of the original model from (3.5) as follows:

(3.18)The component is the random disturbance characterizing the observation and isconstant through time. Since the residuals in (3.18) now consist of two components it is often referred to as the error components or the error decomposition model. In this model the following assumptions are made about the errors:

(3.19)Strict exogeneity of the explanatory variables with both the time varying and the individual specific shocks is also assumed, and for all t and s.3.2.4 Generalized Least Squares estimationSince the errors are defined as they will be serially correlated because of.In this case the pooled OLS estimator although consistent will not be efficientand we can use the generalized-least-squares (GLS) estimator which would be thebest-linear unbiased- estimator (BLUE).As before, it is useful to view the formulation of the model in blocks of observations.For these T observations, let

(3.20)

and (3.21)Following Greene (2000), for the T observations for unit i, let .Then

(3.22)where is a identity matrix and is a column vector of ones as defined in(3.2). Since observations are independent the disturbance covariance matrix forthe full NT observations is given by

(3.23)where denotes the Kronecker product .For GLS is required which is given by . Therefore only is needed,

(3.24)

Where,(3.25)

Then using theta-differencing the model can be transformed to,(3.26)

Where and are again the averages across T observations as defined in (3.11).

This transformation coincides with the Within transformation in the special casewhen . The next step would be to pre-multiply the model with

and similarly for the rows of . For the entire data set generalized least squares is computed by the regression of these partial deviations of on the same transformations of . If is known and theta-differencing is applied to the model, so thatthen

Greene (2000) shows that the GLS estimator is a matrix weighted average of theWithin and Between Groups estimator

(3.27)Where

(3.28)There are some extreme cases to consider. If equals one then generalized least squares is the same as ordinary least squares. This occurs if is zero and a


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classical regression model is applicable. If is zero then the estimator isthe dummy variable estimator discussed in the fixed effects model. This arises as for (see (3.25).3.2.5 Feasible Generalized Least SquaresThe method considered above is applicable when the variance components are known.In applied work the variances will generally be unknown and will need to be esti

mated. Incorporating this, the approach is now called feasible generalized leastsquares (FGLS). FGLS requires consistent estimates of and . This can be doneusing the residuals from the Within estimation and the Between Groups estimation.Using the Within Groups model from (3.13) and rewriting it as deviations from the group means the model is

(3.29)The within residuals are given by

(3.30)Since,

(3.31)using the within estimator a consistent estimator of can be calculated,

(3.32)The degrees of freedom are N opposed to because N observations are used up inthe Within transformation.Now using the model with the Between Groups transformation

(3.33)Where

(3.34)the between residuals are given by

(3.35)So a consistent estimate of the variance of the between residuals is

Since , can be written as

(3.37)Given the above an estimate of can now be inferred,

(3.38)

From (3.32) and (3.38) an estimate for in (3.28) can be derived which will allow feasible generalized least-squares to be used,

(3.39)The efficiency gains from using feasible GLS get smaller in comparison to the within estimator as T gets larger. The two coincide when Hence in the large T case is consistent under the same conditions as .In the small T case the GLS estimator will be biased if is correlated with any of the variables. With T fixed is consistent even if , while is consistent even if . However isefficient relative to when .

The Hausman test can be employed to check for relative robustness of the GLS andWithin Groups estimators. Let

Under

where is the asymptotic variance and h is the Hausman statistic. In the above case if the null hypothesis is rejected the Within estimator is more robust than the GLS estimator. However, since the relative efficiency statement depends he


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avily on the iid assumption of the errors the Hausman test is not robust to heteroskedasticity.3.2.6 Individual-specific variablesGeneralizing model (3.18) to include a vector of individual specific variables that vary across individual units but do not vary over time would give the following formulation:

(3.40)

where are observed variables. Applying the within transformation(3.41)

results in the elimination of the individual specific variables and cannot beestimated. There are, however, three cases in which consistent estimates of can be obtained.Case 1: The first case assumes that all the observed regressors, and are uncorrelated with and strictly exogenous with respect to .

(3.42)Here the GLS can be applied as before, that is, OLS applied to equations after theta- differencing, using a consistent estimate of . This method provides estimates of coefficients in model (3.40) above but requires very strong assumptions.Case 2: In the second case assume that all of the regressors are still uncorre

lated with but some or all of the explanatory variables may be correlated with . We maintain the assumption(3.43)

Consistent estimates of the coefficients can be obtained following a two-step method.In the first instance is estimated which is consistent regardless of any correlation between and .Then the following cross section regression

(3.44)can be used where the unknown is replaced by the consistent as obtained inthe first step. Applying OLS to this equation would give an estimate for whichis consistent as long as the regressors are uncorrelated with .

Case3: The third case is the most general case where some but not all of the variables and some but not all of the regressors are correlated with .If thereare enough variables that are uncorrelated with these can be used as instruments for the variables that are correlated with , and two-stage-least-squares (2SLS) estimation can be employed. In the first stage the and the variables need to be partitioned so that:

(3.45)Where the variables are uncorrelated with ,and the variables are correlatedwith . Similarly

(3.46)Where variables are uncorrelated with , the variables are correlated withand . After partitioning the variables in this way, the first two steps from t

he second case where is obtained and substituted into the cross section equation (3.41), can be applied. Then 2SLS can be employed to estimate using as instruments for . The condition for identification in this case is that is consistent for when this condition is satisfied. However, in the over identifiedcase when is not efficient. More efficient estimators for along these lines were developed by Hausman and Taylor (1981) and Amemiya and MaCurdy (1986).3.3 The dynamic panel data modelMany economic applications are dynamic in character and one of the advantages ofpanel data, mentioned earlier is that it allows the researcher to better understand the slow adjustment process underlying such relationships. These interactions in panel data models are often characterized by the presence of a lagged dependent variable among the regressors,

(3.47)


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where follows a one-way error component model

(3.48)and it is assumed that are observable. In the above formulated modeland are independent of each other and among themselves. This regression is c

haracterized by two sources of persistence over time. Autocorrelation due to thepresence of a lagged dependent variable among the individuals. This results in

the pooled OLS estimator being biased and inconsistent even if the are not serially correlated. The Within transformation removes the but is still biased due to the correlation between andWhereTo show the effects on the standard estimators a simple first-order autoregressive(AR1) process formulated without the explanatory variables can be used:

(3.49)where as in (3.48)3.3.1 Ordinary Least Squares estimation

In the static case in which all the explanatory variables are strictly exogenousand are uncorrelated with the effects, the error component structure can be ignored and OLS applied to the model. The OLS estimator, although less efficient than GLS, is still unbiased and consistent. This is not true for the dynamic modelformulated in (3.49).In this case cannot be strictly exogenous with respect to the since Also since is a function of it immediately follows that will also be a function of . Therefore, is correlated with the error term, unless which would only occur in the case of no unobserved heterogeneity. So, the correlation between the lagged dependent variable and individual-specific effects seriously biasesthe OLS estimator.

(3.50)The asymptotic bias of the OLS estimator is given by the probability limit of L*in(3.50).The probability limits as in Hsiao (2003) for the numerator and denominator ofL* are

where and is zero if are assumed to be arbitrary constant, and positive ifare assumed to be generated by the same process as any other . It is evident

that the asymptotic limit of the OLS estimator depends on the two moments, and . Given that the initial values are bounded theOLS method overestimates the true autocorrelation coefficient when N or T or both tend to infinity. This overestimation is more pronounced the greater the variance of the individual effects,Under the particular assumption of random correlated initial observations as inSevestre and Trognon (1985) the asymptotic bias of is given by

(3.51)

(3.52)

As emphasized in (Trognon 1978) the bias is an increasing function of , at an increasing rate if , constant if and decreasing if .


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Hsiao (2003) reports that, the addition of exogenous variables to a first-orderautoregressive process does not alter the direction of the bias in the OLS estimator of the lagged dependent variable, although its magnitude is reduced. So thecoefficient on the lagged dependent variable remains upward biased and the coefficients of the exogenous variables are biased towards zero.

3.3.2 Within Groups estimation

Consider again the simple AR (1) case in (3.49). By performing a Within transformation the model can be rewritten as

(3.53)can be obtained by applying OLS to the transformed model:

(3.54)The Within Groups estimator exists if the denominator of P* is nonzero and isconsistent if the numerator of P* converges to zero. LetBy continuous substitution equation [2.49] can be rewritten as

(3.55)and the sum of over all t is given by (see Hsiao [2003])

(3.56)Since is independently and identically distributed and is uncorrelated with,using (3.56) Hsiao (2003) shows that as N tends to infinity,

(3.57)and since are random drawings from a normal distribution when T is fixed, as N can be replaced by expectations, E. Thus obtaining

(3.58)The denominator of P*, B can be shown to converge to the following. This resultis derived from Nickell (1981),

(3.59)As T tends to infinity A converges to zero and B converges to a nonzero constantTherefore from (3.54) is a consistent estimate of a. If T is fixed then A isa nonzero constant and is an inconsistent estimate no matter how large

N is. The asymptotic bias is(3.60)

The bias for is due to the elimination of the unknown individual specific effects from each observation, which results in a correlation of order between the explanatory variable and the residuals in the model after applying the Withintransformation. This is a reflection of the fact that is not strictly exogenous with respect to the

For reasonably large values of T, Nickell (1981) shows that

(3.61)Therefore the bias goes to zero as . While for small values of T

(3.62)and

(3.63)This shows that not only is the bias always negative if it gets larger as T becomes smaller and it does not go to zero even if goes to zero

It is useful that we can sign the respective biases for the pooled OLS estimatorand the Within Groups estimator of . We can contrast the two cases and notethat the pooled


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OLS and Within estimators are biased in opposite directions and give an upper and lower bound for . Thus, we would expect that a consistent estimator will lie somewhere between the OLS and Within Groups estimates, or at least not be significantly higher or lower than or a respectively.The random effects GLS estimator is also biased in a dynamic panel data model. This is because after theta-differencing, will be correlated with When T tends to infinity the GLS estimator becomes equivalent to the Within estimator. Then

the GLS estimator is consistent when both N and T tend to infinity. When T is fixed and N tends to infinity the asymptotic bias of the GLS estimator is positive, which shows the positive correlation between and .3.3.3 Instrumental Variables estimationAn alternative transformation that eliminates the individual effects is the first difference transformation. Consider again a simple first-order autoregressiveAR (1) process as in (3.49) formulated as follows

(3.64)where again as in [3.48] but here we assume is unobserved. The first difference transformation removes the individual specific effects from the model togive:

(3.65)

In this case it is easier to deal with the correlation between the predeterminedexplanatory variables and the remaining error terms. This is particularly different from the Within transformation in that it does not introduce all realizations of the disturbances in the residuals of the transformed equation. Considering(3.64) at first sight we have . Applying OLS to this would give a biased andinefficient estimator. The bias of the OLS estimator in this case is given by3.3.4 Anderson and Hsiao estimatorsAnderson and Hsiao (1981) suggested first differencing the model to get rid of the and then using or as instruments which are correlated with and orthogonal to .These instruments are not correlated with as long as the are not serially correlated. The instruments can be used in a 2SLS type estimation procedure that requires for the levels instrument and for The Anderson and Hsiaoestimates of a for each one of the two cases are given by

(3.66)(3.67)

Both and are consistent when or or both. Comparingasymptotic variances of the two estimators Anderson and Hsiao (1981) show that if there is prior belief that successive observations are positively correlated then should be used and should be used if successive observations are negatively correlated.

This instrumental variable (IV) estimation method leads to consistent but not necessarily efficient estimates of the parameters in the model because itdoes not make use of all the available moment conditions (see Ahn and Schmidt, 1995) and it does not take into account the differenced structure of the residualdisturbancesArellano (1989) finds that for simple dynamic error component models the estimator that uses differenced instruments has a singularity point and very large variances over a significant range of parameter values. On the contrary, the estimator that uses instruments in levels has no singularities and much smaller variances. Arellano andBond (1991) proposed a generalized method of moments (GMM) procedure that is more efficient than the Anderson and Hsiao (1982) estimator.3.3.5 First differenced GMM estimation

Arellano and Bond (1991) discuss that additional instruments can be obtained inadynamic panel data model by utilizing the orthogonality conditions that exist between lagged values of and the disturbances This can be illustrated by consi

dering the simple dynamic AR(1) model from (3.64). The basic assumptions of thismodel are as follows:

(3.68)


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(3.69)

(3.70)It assumes that the error terms have lack of serial correlation but not necessarily independence over time. With these assumptions, values of y lagged two periods or more are valid instruments in the equation in first differences (3.65). The first differenced equation for t = 3 is

(3.71)

Here is a valid instrument since it is correlated with and not correlated with given assumption (3.69). Continuing the same type of analysis for

we have the first differenced equation as follows(3.72)

In this case, and are valid instruments for , since both are not correlated with the error term, Continuing in this way and adding one extra valid instrument with each period the set of valid instruments for period T would beBond (2002) notes that, since the model is over identified withand the first differenced error term has a MA (1) with unit root process and

the are serially uncorrelated, 2SLS would still be inefficient even with thecomplete set of instruments and homoskedasticity of the error terms, .Asymptotically efficient estimators can be obtained by using the generalized method of moments (GMM) framework, developed by Hansen (1982). For the dynamic AR (1) model first differenced GMM estimators were developed by Holtz-Eakin, Newey and Rosen (1988) and Arellano and Bond (1991).These methods use assumptions (3.68), (3.69) and (3.70) to imply the following mmoment restrictions that are sufficient to identify and estimate for :

(3.73)

and These moment restrictions can be expressed in matrix notation as(3.74)

where is the matrix given by(3.75)

and is the vector The GMM estimator based on the moment restrictions minimizes the quadratic distance for some weight matrix , where is the matrix and is the vector This results in the following GMM estimator:

(3.76)where the and are the vectors given by

and (3.77) and and are the vectors stacked across individuals in the same way as (see Arellano and Bond (1991)). Different values of the weight matrix result in a set of estimators all of whichare consistent as . In contrast to the within estimator this results holds truefor fixed values of T also.GMM distribution theory implies that a consistent estimator of

(3.78)where

(3.79)Based on the assumptions mentioned above, can be replaced with

(3.80)where are consistent estimates of obtained from

(3.81)for some consistent estimate of . Now setting

(3.82) gives the optimal GMM estimator with the smallest asymptotic variance inthis class of estimators that are efficient for a given set of moment conditions


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.3.3.6 Two-step GMM estimatorThis leads to the introduction of the two-step GMM estimator with

(3.83)The first step is to calculate a consistent estimator, as in (3.76), use it tocompute the residuals as in (3.80) and use the residuals to determine a new wei

ght matrix as in (3.82). The next step is to apply GMM employing the new weightmatrix in (3.76).Asymptotic efficiency results for the estimators are true in large samples but do not necessarily hold in finite samples. In practice the initial choice of theweight matrix in the initial phase of GMM2 (two-step GMM estimation) affects thefinal result. The small sample properties for a are likely to better if a reasonable choice of is used to evaluate . Under the additional assumption of homoskedasticity of disturbances,

(3.84)Arellano and Bond (1991) report a sensible choice of in the initial step is

(3.85)where

(3.86)This leads to an estimator that is asymptotically as efficient as for the special case of homoskedasticity. Therefore, is an appropriate choice not onlyfor initial stage of calculating but also for applied work. Simulation studieshave shown very modest efficiency gains from using the two-step estimation, even in the presence of heteroskedasticity. (see Arellano and Bond (1991), Blundelland Bond (1998) and Blundell, Bond and Windmeijer (2000)). Also the dependenceof the two step weight matrix,

(3.87) on estimated parameters makes the usualasymptotic distribution less reliable for . It has been shown in more simulation studies that asymptotic standard errors tend to be too small for insample sizes where the equivalent tests based on the one-step estimator are quit

e accurate (see Bond and Windmeijer (2002)). It has been shown by Windmeijer (2000) that the downward bias of the asymptotic standard errors of the two-step GMMestimator is due to the presence of estimated parameters in the weight matrix.This extra variation in for small samples can be calculated and Windmeijer (2000) also provides a finite sample corrected estimate of this variance.These methods can be extended to higher order autoregressive models and to models with limited moving-average serial correlation in the disturbances given a minimum value of T required to identify the parameters. For example in the case where is an MA(2) process (results if disturbance term is MA(1)) would not be a valid instrument in the first differenced case but and longer lags still remain in the set of available instruments, and can be identified using this set provided The GMM estimators introduced are for the case where first differencing is used to eliminate the individual specific effect while not introducing disturbances for periods earlier than into the transformed error terms. There exists a wide class of alternative transformations that can be used to achieve the same result. Arellano and Bover (1995) show that optimal estimators are invariant to the transformations used to remove time-invariant individual effects.3.3.7 Ahn and Schmidt conditionsAhn and Schmidt (1995) show that under the standard assumptions used in a dynamic panel data model, (3.68), (3.69) and (3.70), there are additional moment conditions that are ignored by the estimators suggested by Anderson and Hsiao (1981),Holtz- Eakin, Newey and Rosen (1988) and Arellano and Bond (1991). They find the following non-linear moment conditions which are not implied by the linearmoment conditions from (3.73).

(3.88)

These moment conditions are expected to improve efficiency. Asymptotic efficiency comparisons reported in Ahn and Schmidt (1995) report that these moment conditions are particularly informative when is close to unity and/or when is high.


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There are still more moment conditions that can be added to the set of linear and nonlinear conditions from (3.73) and (3.88). This work is also attributed to Ahn and Schmidt (1995). These conditions, however, rely on imposing more assumptions.Including the assumption that the disturbances, , are homoskedastic through time, results in the addition of useful linear orthogonality restrictions. This isgiven by

(3.89) and implies linear orthogonality restrictions

(3.90) and allows a further columns to be added to theinstruments matrix in(3.75).The additional columns can be written as

(3.91)The homoskedasticity through time restrictions also makes available one more nonlinear moment condition which can be written as

(3.92) where

(see Ahn and Schmidt (1995)). Therefore with the inclusion of homoskedasticity through time (3.89) to the original collection of (3.68), (3.69) and (3.70), thecomplete set of linear and non-linear moment condition is (3.73), (3.88), (3.90)and (3.92).3.3.8 System GMM estimation

There are also cases where first differences of can be used as instruments inlevels equations. This was proposed by Arellano and Bover (1995)/Blundel and Bond (1998) and became increasingly popular. Consider the additional assumption

(3.94) which is a restriction on the initial conditions process generating . To guarantee(3.94) we require the initial conditions restriction

(3.95)which is satisfied through the assumption that the initial conditions satisfy mean stationarity so the series , have a constant mean for each individual .This is given by:

(3.96)There are two immediate implications of (3.94). First, it follows that

(3.97)because the AR(1) process implies

(3.98)Second, it also makes the following linear moment conditions available,

(3.99)where as before.These conditions imply the quadratic conditions given in (3.88) because

(3.100)thus making them redundant. Without introducing homoskedasticity, but assuming mean stationarity the complete set of moment conditions now is the m conditions from (3.73) and the conditions from (3.99) which can be implemented as a linearGMM estimator. This forms the basis for a system GMM (GMM-SYS) estimator proposed by Blundell and Bond (1998). A greater efficiency gain is achieved with moreinstruments available now. The GMM-SYS combines the orthogonality conditions inthe first differenced GMM and levels GMM (GMM-LEV) estimators introduced below.GMM-LEV is based on the following m moment conditions

(3.101)which are only related to the equation in levels and can be written in vector no


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tation as

(3.102)where is the as follows

(3.103)

and is the vector . Given these moment conditions GMM estimators can thenbe computed as before.The GMM-SYS estimator uses the complete set of linear moment conditions (3.73) and (3.101), and can be set up as a stacked system comprising both the equationsin first differences and the equations in levels where instruments are observed. The moment conditions for the GMM-SYS can be given as follows

(3.104)(3.105)

where (see Blundell, Bond and Windmeijer (2000)).The moment conditions in vector notations are given by

(3.106)Where(3.107)

(3.108)where is the same as defined in (3.75) and is the non-redundant subset of.Blundell, Bond and Windmeijer (2000) show that the GMM-SYS estimator,

(3.109)where is based on , p is based on and

(3.110)is equivalent to the following linear combination

(3.111)In (3.111) is the first-differenced and is the levels estimator utilizing

only the moment conditions as given in (3.105). is given by(3.112)

where and are the OLS estimates of the initial stage regression coefficientsof the GMM estimation. The implication of this is that as the level of persistence increases, all the weight for the system estimator comes from the levels moment conditions (3.101)11. Blundell and Bond (1998) attribute the bias and thepoor precision of the first-difference GMM estimator found in simulation studieswhere increases towards 1, to the problem of weak instruments as described inNelson and Startz (1990) and Staiger and Stock (1997).Ahn and Schmidt (1995) show that in the case where the initial conditions satisfy (3.97) and the disturbances, are homoskedastic through time, the conditions from (3.88) and the one condition from (3.92) can be replaced by a set ofmoment conditions

(3.113)which are all linear in the parameter . Hence, all the moment conditions, whichare made available through the initial assumptions given by (3.68), (3.69) and(3.70), the added assumptions of homoskedasticity through time (3.88) and the initial condition restriction (3.97), can now be implemented in a linear GMM estimator.Blundell and Bond (1998) show that the additional moment conditions (3.105) remain informative when approaches unity and becomes large, in contrast to themoment conditions (3.104) for the first differenced case. With a persistent series, thus, the addition of these assumptions gives better results. It has been shown that there is an increase in efficiency and a decrease in the finite samplebias and the gains are greater the smaller the value of N.

3.3.9 Autoregressive distributed lag models

The GMM estimators for simple autoregressive models can be extended to apply to


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autoregressive-distributed lag models of the form(3.114)

Where as before and for simplicity is a scalar. Let

which allows the model in (3.114) to be written as

, (3.115)

whereDifferent moment conditions are available depending on the assumption regardingthe correlation between and the two components of the error term, and .The analysis can be divided into two cases, where

(3.116)(3.117)

Furthermore the availability of valid instruments would also depend on the assumptions about the correlation of with the time varying shock, .Case A: Consider, first the assumption where is endogenous, so that it is correlated with current and past , for , allowing for contemporaneous correlation and feedbacks:

(3.118) (3.119)Here is treated symmetrically with the dependent variable , so that lagged values , and longer lags will be valid instrumental variables in the first differenced equations for periods . Thus the new moment condition would be

(3.120)These will be added to the moment conditions from (3.73) to obtain the new moment conditions that can be written as

(3.121)where is the new instrument matrix as follows

(3.122)

and If, (a) is the matrix of observations on stacked in the usual way and (b) is the vector of observations on and (c) is the instrument matrix, then

(3.123)in accordance with the GMM estimation described before and is an arbitrary weight matrix.Making the assumptions (3.118) and (3.119) stronger so that there is no contemporaneous correlation

(3.124)(3.125) and the series is pred

etermined allows the use of as a valid instrument in the first-differenced equation for period t. In this case there will be additional moment conditions given by

(3.126)and these would result in the replacement of by the vector in forming eachrow of the matrix in(3.122).Now making the strongest assumption and letting the process be strictly exogenous, so that

(3.127)the following moment conditions can be added to the original set

(3.128)

This results in the complete time-series being valid instrumental variables ineach of the first-differenced equations. In this case is replaced by the vector in forming each row of the matrix in (3.122).


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If the strict exogeneity assumption is true there is an increase in precision, by using the instrument matrix that contains the complete time-series of and the estimator is asymptotically more efficient. Under the strict exogeneity assumption of the variable, there is no need to assume that the disturbances are serially uncorrelated, and in order to identify and . In this case the optimal GMM2 based on

(3.129)

(3.130)can be used to estimate coefficients in the model. This coincides with the generalized three-step least squares estimator proposed by Chamberlain (1984).In case A it can be seen that with stronger assumptions the size of has increased. This is not a problem asymptotically but in small samples it can lead to anoverfitting bias in the direction of the Within Groups estimator. There is a trade off between asymptotic efficiency and a finite sample bias; the latter can bereduced at the expense of the former by reducing the size of the instrument matrix systematically.Case B: Assuming that is uncorrelated with the unobserved fixed effects makes more moment conditions available. With this assumption there are now valid in

strumental variables for the untransformed levels equations also. Efficient estimation combines the set of moment conditions, those already discussed above forfirst- differenced equations and those implied for the levels equations under the current assumption.If is endogenous with respect to , non-redundant additional moment conditions

(3.131)are added to moment conditions for first-differenced equations. This allows theuse of as an instrumental variable in the levels equation for period t.If is predetermined or strictly exogenous T non-redundant moment conditions ofthe form

(3.132)and

(3.133)3.4 SummaryThe estimations display the expected properties in all reported estimates. Ordinary LeastSquares estimates are biased upwards; the bias depends on the relative varianceof the error components . In section (3.3.1) we discussed why we expect to observe these effects. The Within estimate (discussed in the AR (1) context in Section (3.3.2) is expected to be biased downwards. For IV estimation theory predictsthat all of the instrumental variable estimators we have looked at should be consistent and indeed we see essentially negligible bias for all of them in the estimation results.

Additionally we expect that the System GMM estimator would be more precise thanthe Diff GMM estimator and this theoretical prediction is also confirmed. It istrue that efficiency gains are rather modest and that is also in line with the Table ??? results where we get a very small gain in precision from including moreinstruments.

Chapter 4: Dynamic investment modelsWhile discussing investment models we primarily focus to dynamic investment models. Most of the empirical structural models of investment can be derived from microeconomic optimization exercises. It is nearly about four decades before thatthe first micro econometric dynamic models were introduced in the investment lit

erature Jorgenson (1963), Eisner and Strotz (1963) and Gould (1968)). The dynamic models of investment under uncertainty can be classified in two categories. One category includes the models assuming investment as reversible while other one


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assumes it to be at least partly irreversible. The models of at least partial irreversibility fall in the category of real option literature. Among the numerous investment models the most popular one is the Q model, and under certain assumptions it equates the shadow value of capital to the market-to-book value or average q ratio. In the special case of symmetric adjustment costs the complicatedquadratic functional form is simplified to linear form and linking investment toobservable average q. Despite of its wide spread use, the poor empirical perfor

mance related to it has made a less restricted adjustment cost model more attractive, such as Euler equations and approach by Abel and Blanchard (1986) and Abel (1980) more attractive in the literature of investment.

4.1 The Q modelIn order to explain the Q model (Tobin, 1969) of investment we us assume that

firm has a single (quasi-fixed) homogenous input, labor and other current inputshas zero adjustment cost and the model is fully flexible to employ any numberof inputs needed.The firms net revenue function is given as (4.1)Assuming the markets to be perfectly competitive and by solving the first orderconditions of optimization we can get the shadow value of capital as

(4.2)Here is the rate of depreciation and is the discount factor which discounts back the revenue of period to the period .The shadow value of each extra unit of capital ( ) is an estimated forecast value of the current and expected futurevalue of marginal revenue product of capital. In the case of static factor demand model the optimal capital stock is characterized by or by , and the ratio ofshadow value of capital to the cost is known as marginal q. If the adjustment costs are strictly convex then the marginal adjustment costs are an increasing function of current gross investment and the investment is an increasing functionof the deviation between the actual value of marginal q and the desired value inthe absence of adjustment costs. Another striking finding is that all the useful information about expected profitability of the current investment is containe

d in the marginal q.

As is a common practice in the most applications of the Q model, we assume thatthe cost of capital adjustment is symmetric and is quadratic regarding some standard rate of investment, not necessarily related to the depreciation rate. Principally the, the Q model necessitates the adjustment cost function to be homogenous and linear in and in addition to the production function to be constantreturn to scale. A widely used functional form having all these characteristicsis proposed by Sumer (1981)

(4.3)Where the parameter represents the adjustment cost. The linear form can be obtained as

(4.4)Hayashi (1982) under certain assumptions sets up a unique characteristic of Q model, the equality between unobservable marginal q and the measurable average Q.The necessary condition is that the net revenue function is homogenous and linear and the sufficient condition is that both production function and adjustment cost function exhibit constant return to scale, in addition to the firm to bea price taking.

(4.5)

(4.6)

The basic Q investment model after substituting the average q in place of margin

al q is as(4.7)

All the information is carried through the share prices and if these are not aff


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ected by noise or bubble then the market valuations well reflect the prospects of future earnings and the predictions made on the basis of the Q model are sufficient for investment.Another point worthy to be noted is that the parameters are identified only byassuming the perfect competition and constant return to scale without any specific form of the production function. It can have different consequences depending on the framework. It may be advantageous if the aim is to measure the magnitud

e of the parameters and to test the robustness of specification to the changingfunctional form of the production function. While it may be a shortcoming or might not be enough to estimate the adjustment parameters only if the intensions are to estimate the investment response to the policy change or other aspects of adjustment cost.Although the Q model requires a restrictive structure to equate the marginal q and the average q in order to get a linear relation between investment and marginal q, yet it has advantages over the reduced form models. It can explicitly model expectations influence on the current investment, the parameters obtained arethe technological parameters robust to any structural change in the process generating function and interest rate. It provides an additive advantage to test thehypothesis of perfect capital markets.

Nonetheless there are many ways to show that how a Q model might be falsely specified and is not astonishing that the empirical performance of the Q model has been unsatisfactory. In most of its applications the specification in (4.7) yields low values of thus overstating the marginal cost of adjustment and doubtfully slow adjustment of capital stock. The empirical tests reject the predictionsbased on the specifications as being insufficient more over the inclusion of other variables in the model like sales and cas

Investement Behavior of chinese firms

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