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Pseudo-maximum likelihood estimation of a dynamic structural investment model

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  • Sánchez Mangas, Rocío

Abstract

This paper belongs to the recent investment literature focused on the modelling of microeconomic investment decisions. The increasing concern about this topic is related to the growing availability of microeconomic datasets which show the investment behavior taking place at the firm level. This behavior is far from the smooth capital adjustment pattern derived from the traditional investment models. Rather it is characterized by infrequent and lumpy adjustment. New investment models must be considered to capture this behavior. In this paper we formulate a dynamic structural investment model with irreversibility and nonconvex adjustment costs and try to stress the importance of these costs in the firms' investment decisions. From the methodological point of view, we set the investment decision on the dynamic programming framework. More specifically, we consider a discrete choice dynamic programming problem in which firms decide to invest or not to invest. The estimation strategy we adopt is the Nested Pseudo-Likelihood (NPL) algorithm recently proposed by Aguirregabiria and Mira (2002). It is an estimation method which has clear advantages over previous techniques proposed in this context. Up to our knowledge, this paper constitutes the first empirical application of this estimation method.

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  • Sánchez Mangas, Rocío, 2002. "Pseudo-maximum likelihood estimation of a dynamic structural investment model," DES - Working Papers. Statistics and Econometrics. WS ws026218, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:ws026218
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    References listed on IDEAS

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    1. Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
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    3. Victor Aguirregabiria & Pedro Mira, 2002. "Swapping the Nested Fixed Point Algorithm: A Class of Estimators for Discrete Markov Decision Models," Econometrica, Econometric Society, vol. 70(4), pages 1519-1543, July.
    4. Victor Aguirregabiria, 1999. "The Dynamics of Markups and Inventories in Retailing Firms," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 66(2), pages 275-308.
    5. Øivind Anti Nilsen & Fabio Schiantarelli, 2003. "Zeros and Lumps in Investment: Empirical Evidence on Irreversibilities and Nonconvexities," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 1021-1037, November.
    6. Barnett, Steven A. & Sakellaris, Plutarchos, 1998. "Nonlinear response of firm investment to Q:: Testing a model of convex and non-convex adjustment costs1," Journal of Monetary Economics, Elsevier, vol. 42(2), pages 261-288, July.
    7. Margaret E. Slade & G.R.E.Q.A.M., 1998. "Optimal Pricing with Costly Adjustment: Evidence from Retail-Grocery Prices," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(1), pages 87-107.
    8. Alonso-Borrego, César & Sánchez Mangas, Rocío, 2001. "GMM estimation of a production function with panel data : an application to Spanish manufacturing firms," DES - Working Papers. Statistics and Econometrics. WS ws015527, Universidad Carlos III de Madrid. Departamento de Estadística.
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    Cited by:

    1. Aguirregabiria, Victor & Mira, Pedro, 2010. "Dynamic discrete choice structural models: A survey," Journal of Econometrics, Elsevier, vol. 156(1), pages 38-67, May.
    2. Aguirregabiria, Victor, 2009. "Estimation of Dynamic Discrete Games Using the Nested Pseudo Likelihood Algorithm: Code and Application," MPRA Paper 17329, University Library of Munich, Germany.
    3. De Pinto, Alessandro & Nelson, Gerald C., 2004. "A Dynamic Model Of Land Use Change With Spatially Explicit Data," 2004 Annual meeting, August 1-4, Denver, CO 20314, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).

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