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Exploiting Sequential Learning to Estimate Establishment-Level Productivity Dynamics and Decision Rules Author info | Abstract | Publisher info | Download info | Related research | Statistics David Greenstreet
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.
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Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number
345.
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Date of creation: 2007Date of revision:
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Keywords: Microeconomic Productivity Dynamics ; Unbalanced Panels ; Sequential Learning ; Exit Hazard ; Investment Rates ; Chilean Manufacturing. ; Other versions of this item:
Find related papers by JEL classification: D24 - Microeconomics - - Production and Organizations - - - Production; Capital and Total Factor Productivity; Capacity L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General
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