Some Evidence on the Finite Sample Behavior of an Instrumental Variables Estimator of the Linear Quadratic Inventory Model

Abstract

We evaluate some aspects of the finite sample distribution of an instrumental variables estimator of a first order condition of the Holt et al. (1960) linear quadratic inventory model. We find that for some but not all empirically relevant data generating processes and sample sizes, asymptotic theory predicts a wide dispersion of parameter estimates, with a substantial finite sample probability of incorrectly signed estimates. Simulations indicate that the asymptotic theory usually but not always provides a good approximation to the finite sample distribution.

We thank John Hulbert for research assistance and Riccardo Fiorito, Glenn Rudebusch, and participants in seminars at the Federal Reserve Board of Governors, the Johns Hopkins University, the ISIR session at the 1993 AEA meetings, the 1993 meeting of the NBER Working Group on Common Elements in Trends and Fluctuations, the Research Triangle Econometrics Workshop, and the University of Siena for helpful comments. West thanks the National Science Foundation, the Sloan Foundation and the University of Wisconsin Graduate School for financial support. The views expressed here are those of the authors and not necessarily those of the Board of Governors of the Federal Reserve System or of other members of its staff.