The 40 reference contexts in paper Ulf von Kalckreuth () “Exploring the role of uncertainty for corporate investment decisions in Germany” / RePEc:ses:arsjes:2003-ii-3

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    American Economic Association Meetings in Atlanta, the 2001 Annual Conference of the Royal Economic Society in Durham, the Conference on Fixed Investment at the City University in London, the 49th International Atlantic Conference in Munich and on seminars at the University of Mannheim and the Deutsche Bundesbank are gratefully acknowledged. 1-
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    KNIGHT (1921)
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    argues that the absence of objective probabilities is constitutional for genuinely economic decisions, as opposed to situations of statistical "risk". The latter, he maintains, can be diversified away by pooling ("homogeneous grouping"), whereas the readiness to assume the former type of uncertainty on the part of the entrepreneurs is the true foundation for
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    The latter, he maintains, can be diversified away by pooling ("homogeneous grouping"), whereas the readiness to assume the former type of uncertainty on the part of the entrepreneurs is the true foundation for the existence of profits in a market economy; see
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    KNIGHT (1921),
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    Chapters VIII and IX. It is not clear, however, whether KNIGHT himself really believed in "Knightian uncertainty" as defined above and elsewhere - the use of subjective probabilities seems perfectly compatible with his account.
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    The modern literature distinguishes four main channels by which uncertainty might affect firms' investment outlays: risk aversion, financial constraints, irreversibility and convexity of marginal returns. The oldest and most intuitive account focuses on firms' attitude towards risk; see eg
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    HARTMAN (1976),
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    the textbook exposition by NICKELL (1978) or, most recently, Bo (2001). Risk-averse owners and their managers will systematically trade expected returns for certainty. The standard capital asset pricing model (CAPM) shows how this risk aversion is translated into the equilibrium framework.
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    distinguishes four main channels by which uncertainty might affect firms' investment outlays: risk aversion, financial constraints, irreversibility and convexity of marginal returns. The oldest and most intuitive account focuses on firms' attitude towards risk; see eg HARTMAN (1976), the textbook exposition by NICKELL (1978) or, most recently,
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    Bo (2001).
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    Risk-averse owners and their managers will systematically trade expected returns for certainty. The standard capital asset pricing model (CAPM) shows how this risk aversion is translated into the equilibrium framework.
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    An exception is the study of NISHIMURA and OZAKI (2002), comparing the effect of risk and Knightian uncertainty within the framework of the same model. 3. SCOTT (1976), p. 1-3. 4. For a discussion of decisions under uncertainty with lexicographic preferences, see
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    SINN (1983),
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    p. 59. THE ROLE OF UNCERTAINTY FOR CORPORATE INVESTMENT DECISIONS 175 try might be graver - and the resulting constraints severer - if the prospects of the firm look more uncertain from the outside.
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    GHOSAL and LOUNGANI (2000) argue that the impact of uncertainty on investment might differ across firms, depending on their access to the capital markets. Conversely, STERKEN, LENSINK and
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    Bo (2002)
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    expect the sensitivity of investment with respect to internal finance to depend on the degree of uncertainty. But even financially unconstrained investors who maximise the expected value of their companies given an exogenous discount rate will not be indifferent towards risk.
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    CHIRINKO and SCHALLER (2002) attempt to test for the existence of an irreversibility premium using a structural model. 5. See, for example, CHIRINKO, FAZZARI and MEYER (1999). However, HARHOFF and RAMB (2001), as well as VON
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    KALCKREUTH (2001)
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    find a rather solid interest rate channel in Germany using the Bundesbank data set. For French firms, CHÂTELAIN and TIOMO (2001) find rather small and fragile user cost effects, whereas GAIOTTI and GENERALE (2001) pictures Italian firms as somewhere in between.
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    For an overview, see CHÂTELAIN, GENERALE, HERNANDO, VON KALCKREUTH and VERMEULEN (2003). 176 ULF VON KALCKREUTH A different conclusion is reached by the literature stressing the convexity of the marginal product of capital, as in ABEL (1983) and
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    HARTMAN (1972, 1976).
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    If variable factors, such as labour, energy or raw materials can be optimally adjusted after demand uncertainty is resolved, marginal returns of capital are not linear in product prices any more - the functional relationship will be convex, and Jensen's inequality makes expected profits an increasing function of risk, given optimal adjustment.
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    In general, uncertainty can act as a deterrent from investment, be neutral or even create new incentives; see DIXIT and PINDYCK (1994, Chs. 6 and 11), DARBY, HUGHES-HALLET, IRELAND and PISCI
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    TELLI (1999),
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    or BÖHM, FUNKE and SIEGFRIED (2001). Empirically, it is not easy to test an isolated hypothesis on the effect of uncertainty on investment expenditure. In general, for a given firm or sector, several mechanisms will be at work simultaneously.
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    low adjustment rates.6 There is a relatively recent but growing literature that investigates the significance of uncertainty by means of firm level data, of a very diverse nature. LEAHY and WHITED (1996), MINTON and SCHRAND (1999), and DRIVER, YIP and DAKHIL (1996) use data on US firms. Guiso and PARIGI (1999) work on a panel of Italian companies,
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    PATILLO (1998)
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    utilises a panel of Ghanaian firms and Bo (2002) as well as STERKEN, LENSINK and Bo (2002) work on panels of listed Dutch firms. PEETERS (2001) investigates and compares panels of Belgian and Spanish firms, and BLOOM, BOND and VAN REENEN (2001) use data on companies quoted on the UK stock market.
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    LEAHY and WHITED (1996), MINTON and SCHRAND (1999), and DRIVER, YIP and DAKHIL (1996) use data on US firms. Guiso and PARIGI (1999) work on a panel of Italian companies, PATILLO (1998) utilises a panel of Ghanaian firms and
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    Bo (2002)
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    as well as STERKEN, LENSINK and Bo (2002) work on panels of listed Dutch firms. PEETERS (2001) investigates and compares panels of Belgian and Spanish firms, and BLOOM, BOND and VAN REENEN (2001) use data on companies quoted on the UK stock market.
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    LEAHY and WHITED (1996), MINTON and SCHRAND (1999), and DRIVER, YIP and DAKHIL (1996) use data on US firms. Guiso and PARIGI (1999) work on a panel of Italian companies, PATILLO (1998) utilises a panel of Ghanaian firms and Bo (2002) as well as STERKEN, LENSINK and
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    Bo (2002)
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    work on panels of listed Dutch firms. PEETERS (2001) investigates and compares panels of Belgian and Spanish firms, and BLOOM, BOND and VAN REENEN (2001) use data on companies quoted on the UK stock market.
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    Guiso and PARIGI (1999) work on a panel of Italian companies, PATILLO (1998) utilises a panel of Ghanaian firms and Bo (2002) as well as STERKEN, LENSINK and Bo (2002) work on panels of listed Dutch firms.
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    PEETERS (2001)
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    investigates and compares panels of Belgian and Spanish firms, and BLOOM, BOND and VAN REENEN (2001) use data on companies quoted on the UK stock market. BUTZEN, FUSS and VERMEIDEN (2002) work on a large panel of Belgian firms.
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    Using a measure of market turbulence, DRIVER, YIP and DAKHIL (1996) find that uncertainty on its own has a significant negative effect in only one out of twelve industries, which could be accounted for by a Type I error. Interacting uncertainty with a proxy for vertical market integration results in significant negative effects in four industries.
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    PEETERS (2001)
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    finds negative effects of price uncertainty, but no effects of sales uncertainty. BLOOM, BOND and VAN REENEN (2001) find no long-run effect on the capital, but uncertainty will slow down the reaction of firms to sales shocks.
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    firms, by BÖHM, FUNKE and SIEGFRIED (2001), identifies a positive uncertainty-investment link in a sample of 70 large listed German corporations, which turns negative for firms in very concentrated industries.7 A positive effect is also obtained by LENSINK and STERKEN (2000) in their study on Czech firms. See CARRUTH, DICKERSON and HENLEY (2000) and
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    Bo (2001)
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    for detailed recent surveys of the empirical literature. The Bundesbank's financial statement database {Unternehmensbilanzstatistik, UBS) constitutes the largest source of accounting data for nonfinancial firms in Germany.8 About 70'000 annual accounts were collected per year on a strictly confidential basis by 6.
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    Nevertheless, aggregation will be much less of a problem using firm-level data, and our data set contains many very small firms. 7. Using aggregate data and time series methodology,
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    MAILAND (1998) and WERNER (2001)
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    find negative effects of uncertainty on investment in Germany. 8. This discussion draws on DEUTSCHE BUNDESBANK (1998), FRIDERICHS and SAUVÉ (1999), and STÖSS (2001), which contain more detailed descriptions of the UBS data.
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    Using aggregate data and time series methodology, MAILAND (1998) and WERNER (2001) find negative effects of uncertainty on investment in Germany. 8. This discussion draws on
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    DEUTSCHE BUNDESBANK (1998),
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    FRIDERICHS and SAUVÉ (1999), and STÖSS (2001), which contain more detailed descriptions of the UBS data. With respect to investment demand, the UBS has been utilized by HARHOFF and RAMB (2001) in a user cost study, by VON KALCKREUTH (2001) in a study on the monetary transmission process, by CHIRINKO and VON KALCKREUTH (2002) on financial constraints, by B
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    This discussion draws on DEUTSCHE BUNDESBANK (1998), FRIDERICHS and SAUVÉ (1999), and STÖSS (2001), which contain more detailed descriptions of the UBS data. With respect to investment demand, the UBS has been utilized by HARHOFF and RAMB (2001) in a user cost study, by VON
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    KALCKREUTH (2001)
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    in a study on the monetary transmission process, by CHIRINKO and VON KALCKREUTH (2002) on financial constraints, by BEHR and BELLGARDT (2002) on a dynamic Q equation and by BREITUNG, CHIRINKO and VON KALCKREUTH (2003) in a panel study on the interaction of investment and finance. 178 ULF VON KALCKREUTH the Bundesbank's branch offices, in the context of t
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    With respect to investment demand, the UBS has been utilized by HARHOFF and RAMB (2001) in a user cost study, by VON KALCKREUTH (2001) in a study on the monetary transmission process, by CHIRINKO and VON
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    KALCKREUTH (2002)
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    on financial constraints, by BEHR and BELLGARDT (2002) on a dynamic Q equation and by BREITUNG, CHIRINKO and VON KALCKREUTH (2003) in a panel study on the interaction of investment and finance. 178 ULF VON KALCKREUTH the Bundesbank's branch offices, in the context of the rediscount and lending operations.
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    With respect to investment demand, the UBS has been utilized by HARHOFF and RAMB (2001) in a user cost study, by VON KALCKREUTH (2001) in a study on the monetary transmission process, by CHIRINKO and VON KALCKREUTH (2002) on financial constraints, by BEHR and BELLGARDT (2002) on a dynamic Q equation and by BREITUNG, CHIRINKO and VON
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    KALCKREUTH (2003)
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    in a panel study on the interaction of investment and finance. 178 ULF VON KALCKREUTH the Bundesbank's branch offices, in the context of the rediscount and lending operations.
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    As with all surveys, one has to ensure that the questionnaire is answered in the first place, that it is answered by the right person and that it is answered correctly. Guiso and PARIGI (1999) exploit data on the subjective probability distribution of investors contained in a survey conducted by the Banca dTtalia, and
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    PATILLO (1999)
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    uses a similar data set for 200 entrepreneurs in Ghana constructed with administrative help from the World Bank and the UK government. A cheaper alternative is to make use of regular industry survey data.
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    A second approach is to rely on high-frequency financial market data and to use volatilities, either of commodity prices or exchange rates, or else of stock prices. The first line of research, exemplified in the paper of DARBY, HUGHES-HALLET, IRELAND, and PISCI
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    TELLi (1999),
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    directly quantifies the degree of uncertainty with respect to some crucial economic variables; however, it cannot differentiate between firms. The use of stockmarket data, as in BLOOM, BOND and VAN REENEN (2001), or BÖHM, FUNKE and SIEGFRIED (2001), assumes a strong form of market efficiency and implicitly equates firms' information on future profits to the
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    Finally, one can generate uncertainty indicators from annual or quarterly financial statements of individual firms, measuring the volatility of operating profits, cash flow and other variables. This is the route that will be taken here. GHOSAL and LOUNGANI (1996, 2000), MINTON and SCHRAND (1999),
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    PEETERS (2001), Bo (2002) and
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    BUTZEN, Fuss and VERMEULEN (2002) proceed in the same fashion. Balance sheet or income statement data naturally yield firm-specific indicators and thus can exploit the individual variability of a large panel data set. 180 ULF VON KALCKREUTH 3.2.
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    This equation describes a random walk with a firm-specific drift; the shift parameter a, will indicate the long run deviation of firm i's growth rate from industry average. This equation (1) can be regarded as a version of the famous law of proportional effect, or G IB RAT'S law.
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    GIBRAT (1931)
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    explains the approximately lognormal size distribution of income, firm sizes and other economic variables by positing a stochastic process in which growth rates develop as a series of independent shocks, unrelated to the level (size).
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    Thus the absolute increment to a firm's size in each period will be proportional to the current size of the firm. Since its inception in the early 1930s, GIBRAT'S law has received a lot of attention among industrial economists; see the survey article by
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    SUTTON (1997).
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    More recent studies however, most notably the work by EVANS (1987a,b) and HALL (1987), suggest that the law does not hold. As one of two stylised 10. Of course, sales uncertainty is not the only type of uncertainty relevant for a firm.
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    Since its inception in the early 1930s, GIBRAT'S law has received a lot of attention among industrial economists; see the survey article by SUTTON (1997). More recent studies however, most notably the work by EVANS (1987a,b) and
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    HALL (1987),
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    suggest that the law does not hold. As one of two stylised 10. Of course, sales uncertainty is not the only type of uncertainty relevant for a firm. A prior version of this paper, VON KALCKREUTH (2000), also considers cost uncertainty.
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    More recent studies however, most notably the work by EVANS (1987a,b) and HALL (1987), suggest that the law does not hold. As one of two stylised 10. Of course, sales uncertainty is not the only type of uncertainty relevant for a firm. A prior version of this paper, VON
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    KALCKREUTH (2000),
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    also considers cost uncertainty. The cost uncertainty indicators were discarded as the results lacked robustness with respect to GMM estimation. THE ROLE OF UNCERTAINTY FOR CORPORATE INVESTMENT DECISIONS 181 facts for the relationship between size and growth, SUTTON (1997, p. 46) proposes instead: (a) The probability of survival increases with f
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    The cost uncertainty indicators were discarded as the results lacked robustness with respect to GMM estimation. THE ROLE OF UNCERTAINTY FOR CORPORATE INVESTMENT DECISIONS 181 facts for the relationship between size and growth,
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    SUTTON (1997,
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    p. 46) proposes instead: (a) The probability of survival increases with firm (or plant) size. (b) The proportional rate of growth of a firm (or plant) conditional of survival is decreasing in size.
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    between size and growth, SUTTON (1997, p. 46) proposes instead: (a) The probability of survival increases with firm (or plant) size. (b) The proportional rate of growth of a firm (or plant) conditional of survival is decreasing in size. For our data set, GIBRAT'S law is clearly rejected by two tests on unit roots for panel data proposed by
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    BREITUNG (1997);
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    see Appendix B for a detailed account. Both tests are based on estimations using the GMM procedure for dynamic panel data models discussed in Section 5. The first is a t-test; the second employs the Sargan-Hansen J-statistic as a comprehensive specification test.
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    The model platform corresponds to that used recently by, among others, BOND, EL STON, MAIRESSE and MULKAY (2003), MAIRESSE, HALL and MULKAY (1999) CHIRINKO FAZZARI and MEYER (1999), HARHOFF and RAMB (2001), as well as BLOOM, BOND anc VAN REENEN (2001). CHIRINKO and VON
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    KALCKREUTH (2002)
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    present a detailed discussion. The point of departure is a static neoclassical equation for capital demand. Using a generalised CES production function, EISNER and NADIRI (1968) derive the following linear equation from the first-order conditions of profit maximisation: log Ku = 9 log Su - a log UCC:t + log h]A, (6)
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    estimation Equation (8) corresponds to the common two-way error component regression model,13 which can be written in shorthand as yu = ß'xi.t + £/./, with Çu = Vi + vu (10) 12. On the interpretation of a cash flow variable in the context of a neoclassical investment function see CHIRINKO and VON
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    KALCKREUTH (2002),
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    Appendix A. 13. See the textbook expositions by BALTAGI (2001), Ch. 2 and 3, or HSIAO (2003), Ch 3. 186 ULF VON KALCKREUTH where yiìt is the endogenous variable for firm i, x,-., the vector of explanatory variables, including time dummies, and ß the vector of coefficients including the time effects A,.
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    On the interpretation of a cash flow variable in the context of a neoclassical investment function see CHIRINKO and VON KALCKREUTH (2002), Appendix A. 13. See the textbook expositions by BALTAGI (2001), Ch. 2 and 3, or
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    HSIAO (2003),
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    Ch 3. 186 ULF VON KALCKREUTH where yiìt is the endogenous variable for firm i, x,-., the vector of explanatory variables, including time dummies, and ß the vector of coefficients including the time effects A,.
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    with &,, = <# + vu (14) Using the standard mean difference transformation (13) will introduce correlation between the (transformed) error terms and the (transformed) lagged dependent variable. One solution to both problems is to use some sort of instrumental variable (IV) estimation. ANDERSON and
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    HSIAO (1981)
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    propose transforming the regression equation by first differencing in order to get rid of the individual specific effects. In the case of equation (14), this leads to Ayu = aAyu-i + ß'Axu + Avu (15) Now a set of instruments has to be chosen.
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    We report the second step results on the basis of an asymptotically efficient weighting matrix for the orthogonality conditions. The standard deviations are corrected for the small sample bias reported in ARELLANO and BOND (1991); see
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    WINDMEIJER (2000).
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    The Sargan-Hansen statistics are insignificant both with and without uncertainty, and so is the Lagrange Multiplier statistic that tests for autocorrelation of second order in the residuals of the transformed equation.
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    statistics are insignificant both with and without uncertainty, and so is the Lagrange Multiplier statistic that tests for autocorrelation of second order in the residuals of the transformed equation. For the ADL model, the long run elasticity of capital with respect to real sales is calculated as 7/A-.5 = B(l)/A(l); see CHIRINKO and VON
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    KALCKREUTH (2002),
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    Appendix A. For the models excluding and including the uncertainty variables, this elasticity is calculated as 0.40 and 0.41, respectively. The measured uncertainty coefficient in column (2) is insignificant and close to zero.
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    , the instruments are all available lags of length 1 and higher for the first differences of 7^)/A',-,/-i, A log 5',,/, CFu/I<u-i, apart from a constant and time dummies; additionally all available lags of length 0 and higher of U(3) for the estimation in column (4). Windmeijer-corrected, robust standard errors are in parentheses; see
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    WINDMEIJER (2000).
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    SH is the p-value for the Sargan-Hansen statistic testing overidentifying restrictions. LM is the p-value for the Lagrange Multiplier statistic testing for qth-order autocorrelation of the error terms: columns (1) and (2), q = 2; columns (3) and (4), q = 1.
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    The instruments, apart from a constant and time dummies, are lags of length 1 and higher for the first differences of IU)/KU-\, A log Sut, CF^/A^-i, and all available lags of length 0 and higher for the relevant uncertainty variable. Windmeijer-corrected, robust standard errors are in parentheses, see
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    WINDMEIJER (2000).
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    SH is the p-value for the Sargan-Hansen statistic testing overidentifying restrictions. LM(1) is the p-value for the Lagrange Multiplier statistic testing first order autocorrelation of the error terms.
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    As is well known, in the time series context such a test is compii200 ULF VON KALCKREUTH cated by the fact that under the unit-root hypothesis, the OLS estimates of the autoregressive coefficients have a non-standard limiting distribution for T —> oo, such that the critical values for the normal distribution cannot be used.20
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    BREITUNG (1997)
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    shows that this feature is absent in the panel data context, as long as the limiting distributions for fixed T and N —+ oo are considered. Thus, in a panel with many individuals, the standard inference procedures will be applicable irrespective of whether the coefficients are on the unit circle or within.
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    distribution cannot be used.20 BREITUNG (1997) shows that this feature is absent in the panel data context, as long as the limiting distributions for fixed T and N —+ oo are considered. Thus, in a panel with many individuals, the standard inference procedures will be applicable irrespective of whether the coefficients are on the unit circle or within.
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    BREITUNG (1997)
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    proposes two different tests for unit roots in panel equations. The first one is a simple £-test on the autoregressive coefficient, estimated using GMM. Subtracting log Sij on both sides, we can write equation (Bl) as: A log Su = o,- + (6 - 1) log Su-i +qt+ eu (B2) and test the hypothesis 6 = 1 by looki
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    At the second step, the procedure yielded a coefficient estimate of -0.5367, corresponding to a value of 6 = 0.4633, with a Windmeijer-corrected standard error of 0.01975. The null hypothesis of 6 = 1 can thus be rejected on the basis of a ^-statistic of -28.9. On the basis of a simulation study,
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    BREITUNG (1997)
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    prefers yet another test on unit roots. If we assume a second-order autoregressive process with firm-specific fixed effects, log Su = dj -f 6i log Su-i + b2Sjj-2 + qt + elA (B-l) this will include the AR(1) in equation (1) as a special case.
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