Das, Pranab Kumar (2008): Fundamentals, financial factors and firm investment in India: A Panel VAR approach. Published in: The Indian Economic Journal
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This study analyses the role of fundamentals and financial factors in determining firm investment in India with imperfect capital market in a panel VAR framework. Previous research in this area is based on the test of significance (or some variant of this) of the cash flow variable in the investment equation. In this strand of research, cash flow is considered to be a financial factor. The major theoretical problem of this approach is that in a forward-looking model cash flow might be correlated to fundamental variable(s) of a firm. Because in a forward looking model, current cash flow of a firm also incorporates expectation of fundamentals. There is a problem of disaggregating the fundamental effect of cash flow from the finance effect. Thus, a statistically significant cash flow may not imply that financial market imperfections determine investment. This could be resolved in a VAR framework as it uses a simultaneous equations model. An econometric model is formulated with the joint determination of investment, marginal profit (marginal with respect to capital stock), cash flow and balance sheet variables. The latter captures financial market imperfections while cash flow is modelled to reflect both fundamental factors as well as financial market imperfections while marginal profit is a pure fundamental variable. Using VAR methodology with dynamic panel is a newer approach in the panel regressions. By suitable orthogonal decomposition (such as Choleski decomposition) of the shocks (to cash flow or to fundamental or financial variables)and then looking at the impulse responses one can understand the nature of dynamic adjustments of investment, fundamentals and financial factors.
|Item Type:||MPRA Paper|
|Original Title:||Fundamentals, financial factors and firm investment in India: A Panel VAR approach|
|Keywords:||Firm investment, India, Panel VAR|
|Subjects:||C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C23 - Panel Data Models ; Spatio-temporal Models
D - Microeconomics > D8 - Information, Knowledge, and Uncertainty > D82 - Asymmetric and Private Information ; Mechanism Design
|Depositing User:||Dr. Pranab Kumar Das|
|Date Deposited:||01 Oct 2012 13:24|
|Last Modified:||28 Jan 2016 20:46|
Anderson, T.W. and C. Hsiao (1982). “Formulation and Estimation Using Panel Data” Journal of Econometrics 18: 47-82.
Arellano, M. and S. Bond (1991).: “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equation”, Review of Economic Studies 58: 277-97.
Bagchi, A.K., P.K. Das and B. Moitra (2002). “Are Listed Indian Firms Finance Constrained? Evidence for 1991-92 to 1997-98", Economic and Political Weekly 37(8): 727-36, Feb. 23.
Banerjee, A.V. and E. Duflo (2002). “Do Firms Want to Borrow More? Testing Credit Constraints using a Directed Lending Programme”. MIT. (Mimeo).
Banerjee, A.V., S. Cole and E. Duflo (2003). “Bank Financing in India”. MIT. (Mimeo).
Chamberlin, G. (1985). “Heterogeneity, Omitted Variable Bias, and Duration Dependence”, in J.J. Heckman and B. Singer (eds.), Longitudinal Analysis of Panel Data. Cambridge: CUP.
Chirinko, Robert S. (1993). “Business Fixed Investment Spending: A Critical Survey of Modelling Strategies, Empirical Results, and Policy Implications”, Journal of Economic Literature 31(4): 1875-1911.
Fazzari, Steven M., R. Glenn Hubbard and Bruce C. Petersen (1988). “Financing Constraints and Corporate Investment”, Brookings Papers on Economic Activities. pp.141-95.
————. (1996). “Financing Constraints and Corporate Investment: Response to Kaplan and Zingales”, NBER Working Paper No. 5462.
Fazzari, Steven M. and Bruce C. Petersen (1993). “Working Capital and Fixed Investment: New Evidence on Financing Constraints”, RAND Journal of Economics 24(4): 328-41.
Ganesh-Kumar, A., K. Sen and R.R. Vaidya (2001). “Outward Orientation, investment and Finance constraints: a study of Indian firms”, Journal of Development Studies 37(4): 133-49.
Gilchrist, S. and C. Himmelberg (1995). “Evidence on the Role of Cash Flow for Investment”, Journal of Monetary Economics 36: 541-72.
Gilchrist, S. and C. Himmelberg (1998). “Investment, Fundamentals and Finance”, NBER Working Paper No. 6652.
Hayashi, Fumio (1982). “Tobin’s Average q and Marginal q: A Neoclassical Interpretation”, Econometrica 50: 213-24, Jan.
Holtz-Eakin, D., W. Newey and H. S. Rosen (1988). “Estimating Vector Autoregressions with Panel Data”, Econometrica, 56: 1371-1395.
Hubbard, R.Glenn. (1998). “Capital Market Imperfection and Investment”, Journal of Economic Literature 36: 193-225, March.
Jaffee, D. and T. Russel (1976). “Imperfect Information and Credit Rationing”, Quarterly Journal of Economics 90: 651-66.
Kaplan, Steven N. and Luigi Zingales (1997). “Do Financing Constraint Explain Why Investment Is Correlated With Cash Flow?”, Quarterly Journal of Economics. 112(1): 565-92.
Kiviet, J.F. (1995). “On Bias, Inconsistency, and Efficiency of Various Estimators in Dynamic Panel Data Models”, Journalof Econometrics 68: 53-78.
Marjit, S., P.K. Das and S. Chattopadhyay, S. (2004). “Contemporary Macroeconomic Events of India: Some Analytical Discussions”, India Macroeconomics Annual 2003-2004. pp.1-49.
Myres, S.C. and N.S. Majluf (1984). “Corporate Financing and Investment Decisions When Firms Have Information That Investors Do Not Have”, Journal of Financial Economics 13: 187-221.
Nickell, S. (1981). “Biases in Dynamic Models with Fixed Effects”, Econometrica 49: 1417-26.
Runkle, D.E. (1987). “Vector Autoregressions and Reality”, Journal of Business and Economic Statistics 5: 437-42.
Stiglitz, Joseph E. and Andrew Weiss (1981). “Credit Rationing in Markets with Imperfect Information”, American Economic Review 71(3): 393-410.
Whited, Tony M. (1992). “Debt, Liquidity Constraint, and Corporate Investment: Evidence from Panel Data”, Journal of Finance 47(4): 1425-60.
Williamson, S.J. (1987). “Recent Developments in Modelling Financial Intermediation”, Federal Reserve Bank of Minneapolis Quarterly Review: 19-29, Summer.