During the last decade exploring the link between financial factors and investment has become a major field of theoretical and empirical publications. Firms in a weak financial position cannot raise capital at conditions that allow financing all profitable investment projects. Some empirical findings indicate that young, fast growing, low dividend paying firms face more liquidity constraints compared to other firms and therefore exhibit stronger reactions in investment to changes of their internal financial flow. If the credit conditions of firms will be influenced through expansionary or rather restrictive monetary conditions the cycle of real activity might be accelerated through these monetary conditions and the final effects might exceed the pure effects of the interest channel alone. In the analysis in hand the role of financial factors in the firm's investment decision is explored within the framework of the Q-theory of investment. The Q-theory is applied to a large database of German firms, covering balance sheet data for 2,314 German firms, thereof 1,342 manufacturing firms. This database, the Deutsche Bundesbank's Corporate Balance Sheet Statistics, gives the unique opportunity to analyze heterogeneity at the firm level, which is not possible using macro- or mesoeconomic data. Access to this data base was made possible during a stay at a visiting researcher in the research department at the Deutsche Bundesbank in 2002. I would like to thank Heinz Herrmann for his support and the fruitful discussions of questions arising during the research project.
Behr
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1. Introduction.- 2. The Q-theory of investment and the role of internal funds.- 2.1. The Q-Model.- 2.2. Investment, information and incentive problems in capital markets.- 2.3. Conclusion.- 3. Dynamic panel data estimation.- 3.1. The problem of bias caused by lagged dependant variables.- 3.2. Dynamic panel data estimators.- 3.3. Monte Carlo study.- 3.4. Conclusion.- 4. The data source and measurement problems.- 4.1. The data source.- 4.2. The capital stock at replacement costs: A new algorithm.- 4.3. The Calculation of Q.- 4.4. Conclusion.- 5. Empirical findings.- 5.1. Nonparametric regression results.- 5.2. Aggregate and disaggregate Q-investment functions.- 5.3. Testing for liqudity effects in three sectors: manufacturing, construction, commerce.- 5.4. An analysis of size effects.- 5.5. An eclectic approach.- 6. Summary.- References.
PD Dr. Andreas Behr lehrt am Institut für Ökonometrie und Wirtschaftsstatistik der Universität Münster.
