Pseudo-maximum likelihood estimation of a dynamic structural investment model
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.
|Date of creation:||Dec 2002|
|Date of revision:|
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- Victor Aguirregabiria & Pedro Mira, 2002.
"Swapping the Nested Fixed Point Algorithm: A Class of Estimators for Discrete Markov Decision Models,"
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MIT Press, vol. 85(4), pages 1021-1037, November.
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- 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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