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Bayesian estimation of NOEM models: identification and inference in small samples

  • Enrique Martínez-García
  • Diego Vilán
  • Mark Wynne

This paper studies the (potential) weak identification of these relationships in the context of a fully specified structural model using Bayesian estimation techniques. We trace the problems to sample size, rather than misspecification bias. We conclude that standard macroeconomic time series with a coverage of less than forty years are subject to potentially serious identification issues, and also to model selection errors. We recommend estimation with simulated data prior to bringing the model to the actual data as a way of detecting parameters that are susceptible to weak identification in short samples.

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File URL: http://www.dallasfed.org/assets/documents/institute/wpapers/2012/0105.pdf
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Paper provided by Federal Reserve Bank of Dallas in its series Globalization and Monetary Policy Institute Working Paper with number 105.

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Date of creation: 2012
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Handle: RePEc:fip:feddgw:105
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