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A Generalized Bayesian Instrumental Variable Approach under Student t-distributed Errors with Application

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  • Matthew J. Salois
  • Kelvin G. Balcombe

Abstract

type="main"> Bayesian analysis is given of an instrumental variable model that allows for t-distributed errors in both the structural equation and the instrument equation. Specifically, the approach for dealing with t-distributed errors is extended to the Bayesian instrumental variable estimator by modelling the variance for each error using a Gamma distributed hierarchical prior. The computation is carried out by using a Markov chain Monte Carlo sampling algorithm for the heteroskedastic case. An example using data illustrates the approach and shows that ignoring the presence of error terms with heavy tails in the instrument equation when it exists may lead to biased estimates.

Suggested Citation

  • Matthew J. Salois & Kelvin G. Balcombe, 2015. "A Generalized Bayesian Instrumental Variable Approach under Student t-distributed Errors with Application," Manchester School, University of Manchester, vol. 83(5), pages 499-522, September.
  • Handle: RePEc:bla:manchs:v:83:y:2015:i:5:p:499-522
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    File URL: http://hdl.handle.net/10.1111/manc.12048
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    References listed on IDEAS

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    1. repec:kap:enreec:v:73:y:2019:i:4:d:10.1007_s10640-018-0298-9 is not listed on IDEAS
    2. Octavio Fernández-Amador & Doris A. Oberdabernig & Patrick Tomberger, 2019. "Testing for Convergence in Carbon Dioxide Emissions Using a Bayesian Robust Structural Model," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 73(4), pages 1265-1286, August.

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