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Pseudo-Maximum Likelihood Estimation Of A Dynamic Structural Investment Model

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Rocío Sánchez-Mangas ()
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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Paper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws026218.

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Date of creation: Dec 2002
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Handle: RePEc:cte:wsrepe:ws026218

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  1. Oivind Anti Nilsen & Fabio Schiantarelli, 1996. "Zeroes and Lumps in Investment: Empirical Evidence on Irreversibilities and Non-Convexities," Boston College Working Papers in Economics 337., Boston College Department of Economics, revised 01 Nov 2000. [Downloadable!]
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  2. 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. [Downloadable!] (restricted)
  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. [Downloadable!] (restricted)
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  4. Aguirregabiria, Victor, 1999. "The Dynamics of Markups and Inventories in Retailing Firms," Review of Economic Studies, Blackwell Publishing, vol. 66(2), pages 275-308, April. [Downloadable!] (restricted)
  5. M Arellano & O Bover, 1990. "Another Look at the Instrumental Variable Estimation of Error-Components Models," CEP Discussion Papers 07, Centre for Economic Performance, LSE.
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  6. César Alonso-Borrego & Rocío Sánchez-Mangas, 2001. "Gmm Estimation Of A Production Function With Panel Data: An Application To Spanish Manufacturing Firms," Statistics and Econometrics Working Papers ws015527, Universidad Carlos III, Departamento de Estadística y Econometría. [Downloadable!]
  7. Slade, M.E., 1996. "Optimal Pricing with Costly Adjustment: Evidence from Retail-Grocery Prices," UBC Departmental Archives 96-02, UBC Department of Economics.
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  1. Victor Aguirregabiria & Pedro mira, 2007. "Dynamic Discrete Choice Structural Models: A Survey," Working Papers tecipa-297, University of Toronto, Department of Economics. [Downloadable!]
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