Exploiting Sequential Learning to Estimate Establishment-Level Productivity Dynamics and Decision Rules

David Greenstreet

No 345, Economics Series Working Papers from University of Oxford, Department of Economics

Abstract: This paper develops a sequential learning estimator of production functions and productivity dynamics for unbalanced establishment panels. Extending an idea from the literature on dynamic industry models, establishments are uncertain about their own idiosyncratic productivities and update productivity beliefs using information revealed by their production experience. The estimator relies on the structure of this iterative learning process and thereby avoids placing any restriction on establishment strategic behavior. Consequently, the estimator is suitable for comparative studies of the behavioral sources of technological change across all types of industry. Estimation of productivity dynamics and of behavioral decision rules are separated into recursive stages. Using sequential learning estimates of productivity beliefs from the first stage, decision rules for exit, investment, and innovation effort can be estimated in a second stage. A test application with four Chilean industries confirms that the estimator produces plausible estimates with small standard errors. Decision rule estimates show that productivity beliefs affect investment and exit hazards in the expected direction.

Keywords: Microeconomic Productivity Dynamics; Unbalanced Panels; Sequential Learning; Exit Hazard; Investment Rates; Chilean Manufacturing (search for similar items in EconPapers)
JEL-codes: C23 D24 D83 L25 L60 (search for similar items in EconPapers)
Date: 2007-08-01
New Economics Papers: this item is included in nep-bec, nep-eff and nep-tid
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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