The 23 reference contexts in paper Christopher F. Baum, Andreas Stephan, Oleksandr Talavera (2004) “The Effects of Uncertainty on the Leverage of Non-Financial Firms” / RePEc:boc:bocoec:602

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    Corresponding author: Oleksandr Talavera, tel. (+49) (0)30 89789 407, fax. (+49) (0)30 89789 104, e-mail: otalavera@diw.de, mailing address: DIW Berlin, 10108 Berlin, Germany. 1 1 Introduction The determinants of capital structure have always attracted considerable attention in the literature. In their seminal work,
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    Modigliani and Miller (1958)
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    derive the theoretical result that under the assumption of perfect capitalmarkets, financial and real decisions are separable so that the firm’s leverage has no effect on the market value of the firm, nor on its capital investment plans.
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    show a positive relationship between liquid asset holdings and firms’ investment decisions.1Other studies show that firm leverage depends on firm-specific characteristics such as cash holdings, total assets, and the investment-to-capital ratio.2Furthermore, empirical evidence on the interaction of macroeconomic uncertainty and capital structure indicators is rather scarce. As an exception,
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    Baum, Caglayan, Ozkan and Talavera(2006)
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    find a negative relationship between macroeconomic uncertainty and the cross-sectional dispersion of cash-to-asset ratios for US non-financial firms. Hence, their study supports the view that macroeconomic uncertainty is an important factor in firms’ decision-making.
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    The model is based upon anempirically testable hypothesis regarding the association between the optimal level of debt and uncertainty arising from macroeconomic or idiosyncratic sources. The model predicts that an increase in either type of uncertainty leads to a decrease in leverage. 1See for example
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    Gilchrist and Himmelberg (1998); Fazzari, Hubbard and Petersen (1988).
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    2See Shuetrim, Lowe and Morling (1993); Auerbach (1985); Weill (2001). 2 To test the model’s predictions, we apply the System GMM estimator (Blundell and Bond, 1998) to a panel of US non-financial firms obtained from the quarterlyCOMPUSTATdatabase over the 1993–2002 period.
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    The model is based upon anempirically testable hypothesis regarding the association between the optimal level of debt and uncertainty arising from macroeconomic or idiosyncratic sources. The model predicts that an increase in either type of uncertainty leads to a decrease in leverage. 1See for example Gilchrist and Himmelberg (1998); Fazzari, Hubbard and Petersen (1988). 2See
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    Shuetrim, Lowe and Morling (1993); Auerbach (1985); Weill (2001).
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    2 To test the model’s predictions, we apply the System GMM estimator (Blundell and Bond, 1998) to a panel of US non-financial firms obtained from the quarterlyCOMPUSTATdatabase over the 1993–2002 period.
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    The model predicts that an increase in either type of uncertainty leads to a decrease in leverage. 1See for example Gilchrist and Himmelberg (1998); Fazzari, Hubbard and Petersen (1988). 2See Shuetrim, Lowe and Morling (1993); Auerbach (1985); Weill (2001). 2 To test the model’s predictions, we apply the System GMM estimator
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    (Blundell and Bond, 1998) to
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    a panel of US non-financial firms obtained from the quarterlyCOMPUSTATdatabase over the 1993–2002 period. After some screening procedures it includes more than 31,000 manufacturing firm-quarter observations,with about 950 firms per quarter.
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    Finally, Section 4 concludes and gives suggestions for further research. 3 2 TheQModel of Firm Value Optimization 2.1 Model Setup The theoretical model proposed in this paper is based on the firm value optimization problem and represents a generalization of the standardQmodels of investment by
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    Whited (1992) and Hubbard and Kashyap (1992).
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    The present value of the firm is equated to the expected discounted stream ofDt, dividends paid to shareholders, where βis the discount factor. Vt(Kt) = max {It+s,Bt+s}∞s=0 Dt+Et "∞ X s=1 βsDt+s # ,(1) Kt+1= (1−δ)Kt+It,(2) Dt= Π(Kt, φt)−C(It, Kt)−It+Bt−Bt−1R(τt)η(Bt−1, Kt, ξt),(3) Dt≥0,(4) lim T→∞   T−1Y j=t βj  BT= 0,∀t(5) The firm maximizes equation (1) subject to three constraints.
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    We incorporate financial frictions assuming that risk-neutral shareholders require an external premium,η(Bt−1, Kt, ξt), which depends on firm-specific characteristics such as debt and capital stock as well as a stochastic shockξt. Similar to
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    Gilchrist and Himmelberg (1998),
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    we also assume ∂η/∂Bt−1>0: i.e., highly indebted firms must pay an additional premiumto compensate debt-holders for additional costs because of monitoring orhazard problems. Moreover, ∂η/∂Kt<0: i.e., large firms enjoy a lower risk premium.
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    ExpressionβΘtmay serve as a stochastic time-varying discount 3This is in line with the stylized fact—particularly for large US publicly-traded firms–that equity finance is a rarely-considered option. “The net equity issuances by the US non-financial corporate sector have been negative since the 1980s.”
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    Frank and Goyal (2005),
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    p. 32. To a first approximation, ignoring equity finance would seem to be largely in line with observed practice. 5 factor which is equal toβin the absence of financial constraints (λt+1=λt).4 From the first-order conditions for debt we derive: Et h βΘtRt+1 ηt+1+ηBt+1Bt <i = 1.(7) In the steady stateβEt{(R(τt+1))Θt}=βE{R(τt+1)}= 1,which implies thatηBt+1Bt= 1−ηt+1.
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    equity finance would seem to be largely in line with observed practice. 5 factor which is equal toβin the absence of financial constraints (λt+1=λt).4 From the first-order conditions for debt we derive: Et h βΘtRt+1 ηt+1+ηBt+1Bt <i = 1.(7) In the steady stateβEt{(R(τt+1))Θt}=βE{R(τt+1)}= 1,which implies thatηBt+1Bt= 1−ηt+1.Since we assumeηBt+1>0, Btis guaranteed to be positive only ifηt+1≤1.
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    Gilchrist and Himmelberg (1998)
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    suggest that the risk premium may be negative ifηt+1 is considered as net of tax advantages or agency benefits. Combining the first order conditions we derive the optimal level for borrowing Bt= Et{ΠK,t+1Θt}+ (1−δ)Et{ΘtCI,t+1}−Et{Θtηt+1R(τt+1)}−1/βCI,t ηBEt{ΘtR(τt+1)}+ηKE{R(τt+1)} (8) which allows us to derive ∂Bt ∂τt+1 = ∂Bt ∂Et{R(τt+1)} ∂Et{R(τt+1)} ∂τt+1 <0(9) Similarly, we construct the relati
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    {Θtηt+1R(τt+1)}−1/βCI,t ηBEt{ΘtR(τt+1)}+ηKE{R(τt+1)} (8) which allows us to derive ∂Bt ∂τt+1 = ∂Bt ∂Et{R(τt+1)} ∂Et{R(τt+1)} ∂τt+1 <0(9) Similarly, we construct the relationship between idiosyncratic uncertainty and firm lending. Equation (8) provides a positive relationship betweenexpected profitability and leverage. The negative relationship between profitabilityand uncertainty is justified in
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    Batra and Ullah (1994)
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    : ∂Bt ∂φt+1 = ∂Bt ∂Et{Π(φt+1)} ∂Et{Π(φt+1)} ∂φt+1 <0(10) 4For simplicity, we ignore the derivative of the investment adjustment cost function with respect to the capital stock,CK,t. In our data the mean ofIt/Kt=0.04, and the squared term will be 0.0016 given thatCK,t= (It/Kt)2.
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    In order to implement Euler equation estimation we linearize the product ofβt, ΘtandAt,whereAt= ΠK,t+1+ (1−δ) (CI,t+1+ 1)−R(τt+1)ηK,t+1Bt+1.We utilize a first–order Taylor approximation around the means. Ignoring constant terms, the approximation is equal to:5 βtΘtAt=βγΘt+βAt+γβt whereβis the average discount factor andγdenotes the unconditional mean ofAt. As in
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    Chirinko (1987) and Hayashi (1982),
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    we utilize a traditional adjustment cost function given byC(It, Kt) =α2(It/Kt−νi)2Kt.6The parameterνimight be interpreted as a firm-specific optimal level of investment. The marginal adjustment cost of 5See also Love (2003). 6We acknowledge that there is support in the literature for potentially non-linear adjustment cost functions (e.g.
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    As in Chirinko (1987) and Hayashi (1982), we utilize a traditional adjustment cost function given byC(It, Kt) =α2(It/Kt−νi)2Kt.6The parameterνimight be interpreted as a firm-specific optimal level of investment. The marginal adjustment cost of 5See also
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    Love (2003).
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    6We acknowledge that there is support in the literature for potentially non-linear adjustment cost functions (e.g., Abel and Eberly (2002). As this feature is not the focus of our study, we make the standard assumption of quadratic adjustment costs. 7 investment of a firmiat timetis given by: CI,it=α > Iit T Ait −νi (11) whereT Ait, a proxy for capitalKit, measures total assets of firmiat timet.
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    As in Chirinko (1987) and Hayashi (1982), we utilize a traditional adjustment cost function given byC(It, Kt) =α2(It/Kt−νi)2Kt.6The parameterνimight be interpreted as a firm-specific optimal level of investment. The marginal adjustment cost of 5See also Love (2003). 6We acknowledge that there is support in the literature for potentially non-linear adjustment cost functions (e.g.,
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    Abel and Eberly (2002).
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    As this feature is not the focus of our study, we make the standard assumption of quadratic adjustment costs. 7 investment of a firmiat timetis given by: CI,it=α > Iit T Ait −νi (11) whereT Ait, a proxy for capitalKit, measures total assets of firmiat timet.
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    The level of financing constraint for a representative firmiat timet, Θit, is a function of their stock of cash and level of debt: Θit=a0i+a1Cashit+a2Bi,t−1(14) whereCashitis the cash and cash equivalent,Bitis the level of debt anda0iis a firm-specific measure of financial constraints. Debt generates interest and principal 7The discussion in
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    Gilchrist and Himmelberg (1998)
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    suggeststhat a sales-based measure of the marginal profit of capital is more desirable comparing to operating income measure. 8 obligations and increases the probability of financial distress, while the availability of liquid assets relaxes the external finance constraint (see also Hubbard, Kashyap and Whited (1995); Almeida, Campello and Weisbach (2004); Gilchrist and Himmelberg (1998)).
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    Debt generates interest and principal 7The discussion in Gilchrist and Himmelberg (1998) suggeststhat a sales-based measure of the marginal profit of capital is more desirable comparing to operating income measure. 8 obligations and increases the probability of financial distress, while the availability of liquid assets relaxes the external finance constraint (see also
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    Hubbard, Kashyap and Whited (1995); Almeida, Campello and Weisbach (2004); Gilchrist and Himmelberg (1998)).
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    Finally, the base interest rateR(τt) is assumed to be a linear function of macroeconomic uncertainty and the index of leading indicators, a proxy of the state of the macroeconomy: R(τt) =ω1τt+ω2Leadingt(15) The resulting empirical specification is:8 Bit T Ait =β0+β1 Bit−1 T Ait−1 +β2 Cashit T Ait +β3 Sit T Ait +β4 Iit+1 T Ait+1 +β5 Iit T Ait (16) +β6τt−1+β7φi,t−1+β8Leadinct−1+fi+Indi+eit AsCOMPUST
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    The main aim of our study is to investigate whether robust results are obtained for these hypotheses relating to uncertainty measures and not to identify the coefficients of the structural model.9 2.3 Identifying Uncertainty The macroeconomic uncertainty identification approach resembles that of
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    Baum et al. (2006).
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    Firms’ debt decisions depend on anticipation of future profits and investment. The difficulty of evaluating the optimal amount of debt issuing increases with the level of macroeconomic uncertainty. The literature suggests various methods to obtain a proxy for macroeconomic uncertainty.
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    The difficulty of evaluating the optimal amount of debt issuing increases with the level of macroeconomic uncertainty. The literature suggests various methods to obtain a proxy for macroeconomic uncertainty. In our investigation, as in
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    Driver, Temple and Urga (2005) and Byrne and Davis (2005),
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    we use aGARCHmodel to proxy for macroeconomic uncertainty. We believe that this approach is more appropriate compared to alternatives such as proxies obtained from moving standard deviations of the macroeconomic series (e.g.
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    In our investigation, as in Driver, Temple and Urga (2005) and Byrne and Davis (2005), we use aGARCHmodel to proxy for macroeconomic uncertainty. We believe that this approach is more appropriate compared to alternatives such as proxies obtained from moving standard deviations of the macroeconomic series (e.g.,
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    Ghosal and Loungani (2000))
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    or survey-based measures based on the dispersion of forecasts (e.g., Graham and Harvey (2001), Schmukler, Mehrez and Kaufmann (1999)). While the 9It is possible to show that allβs are functions of model parameters, but it is not possible toidentify every parameter of the structural model with reasonable assumptions. 10 former approach suffers from substantial serial correlation problems in the co
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    We believe that this approach is more appropriate compared to alternatives such as proxies obtained from moving standard deviations of the macroeconomic series (e.g., Ghosal and Loungani (2000)) or survey-based measures based on the dispersion of forecasts (e.g.,
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    Graham and Harvey (2001), Schmukler, Mehrez and Kaufmann (1999)).
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    While the 9It is possible to show that allβs are functions of model parameters, but it is not possible toidentify every parameter of the structural model with reasonable assumptions. 10 former approach suffers from substantial serial correlation problems in the constructed series the latter potentially contains sizable measurement errors.
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    The specifics of theGARCHmodel are provided in Table 1. The estimated conditional variance series is then employed in a revised version of equation (16). One can employ different proxies to capture firm-specific risk. For instance,
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    Bo and Lensink (2005)
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    use three measures: stock price volatility,estimated as the difference between the highest and the lowest stock price normalized bythe lowest price; volatility of sales measured by the coefficient of variation of sales overa seven–year window; and the volatility of number of employees estimated similarly to volatility of sales.
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    For instance, Bo and Lensink (2005) use three measures: stock price volatility,estimated as the difference between the highest and the lowest stock price normalized bythe lowest price; volatility of sales measured by the coefficient of variation of sales overa seven–year window; and the volatility of number of employees estimated similarly to volatility of sales.
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    Bo (2002)
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    employs a slightly different approach, setting up the forecasting AR(1) equation for the underlying uncertainty variable driven by sales and interest rates. The unpredictable part of the fluctuations, the estimated residuals, are obtained from that equation and their three-year moving average standard deviation is computed.
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    , paper products, printing and publishing, chemicals, petroleum and coal products, rubber and plastics, and leather products makers. 13 3.2 Empirical results We estimate Equation (17) using the lagged conditional variance of the index of leading indicators as the proxy for macroeconomic uncertainty. Results for all manufacturing firms are given in Table 4. These specifications represent the
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    Blundell and Bond (1998)
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    one-step System GMM estimator employing the first differences transformation.12As instruments we useB/T At−3toB/T At−5,CASH/T At−2toCASH/T At−5,I/T At−2 toI/T At−5, andS/T At−2toS/T At−5for the equations in differences and ∆S/T At−1, ∆CASH/T At−1, and ∆I/T At−1for the equations in levels.
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    Durable goods makers, high-liquidity firms, highly leveraged firms and small firms also exhibit sensitivity to firm-specific volatility. From the policy perspective, we suggest that macroeconomicuncertainty has an effect on nonfinancial firms’ capital structure which in turn affectstheir dynamics of investment. Other studies (see
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    Bernanke and Gertler (1989))
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    have shown that balance sheet shocks may affect the amplitude of investment cycles in a simple neoclassical model. Moreover, in many countries monetary policy tends to be persistent in the direction of change of the monetary instrument, with rare reversals (perhaps reflecting central banks’ interest rate smoothing objectives).
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