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Consumer willingness-to-pay for blockchain-based QR code traceability of leafy greens. [PDF]
PLoS OneCollart AJ +3 more
europepmc +1 more source
Forecasting a time series of Lorenz curves: one-way functional analysis of variance. [PDF]
J Appl StatLin Shang H.
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Econometric Causality: The Central Role of Thought Experiments. [PDF]
J EconomHeckman J, Pinto R.
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Econometrica, 1985
The widespread use of prior information in formulating, estimating, and using econometric models is reviewed. Attempts to avoid the use of prior information by formulating multivariate statistical VAR and ARMA time series models for economic time series data have resulted in heavily over-parametrized models.
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The widespread use of prior information in formulating, estimating, and using econometric models is reviewed. Attempts to avoid the use of prior information by formulating multivariate statistical VAR and ARMA time series models for economic time series data have resulted in heavily over-parametrized models.
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2019
Bayesian Econometric Methods examines principles of Bayesian inference by posing a series of theoretical and applied questions and providing detailed solutions to those questions. This second edition adds extensive coverage of models popular in finance and macroeconomics, including state space and unobserved components models, stochastic volatility ...
Chan, Joshua +3 more
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Bayesian Econometric Methods examines principles of Bayesian inference by posing a series of theoretical and applied questions and providing detailed solutions to those questions. This second edition adds extensive coverage of models popular in finance and macroeconomics, including state space and unobserved components models, stochastic volatility ...
Chan, Joshua +3 more
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2007
This volume in the Econometric Exercises series contains questions and answers to provide students with useful practice, as they attempt to master Bayesian econometrics. In addition to many theoretical exercises, this book contains exercises designed to develop the computational tools used in modern Bayesian econometrics.
Koop, G.M., Poirier, D., Tobias, J.
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This volume in the Econometric Exercises series contains questions and answers to provide students with useful practice, as they attempt to master Bayesian econometrics. In addition to many theoretical exercises, this book contains exercises designed to develop the computational tools used in modern Bayesian econometrics.
Koop, G.M., Poirier, D., Tobias, J.
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Bayesian Model Averaging for Spatial Econometric Models [PDF]
SSRN Electronic Journal, 2006 We extend the literature on Bayesian model comparison for ordinary least‐squares regression models to include spatial autoregressive and spatial error models. Our focus is on comparing models that consist of different matrices of explanatory variables. A Markov Chain Monte Carlo model composition methodology labeled MC3 by Madigan and York is developed
Olivier Parent, James P. Lesage
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Bayesian econometrics and forecasting
Journal of Econometrics, 2001Contemporary Bayesian forecasting methods draw on foundations in subjective probability and preferences laid down in the mid-twentieth century, and utilize numerical methods developed since that time in their implementation. These methods unify the tasks of forecasting and model evaluation.
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Introduction to Bayesian Econometrics in MATLAB
2022This seminar provides an introduction to Bayesian econometrics. It covers the general theory underlying Bayesian econometrics and Bayesian inference in the linear regression model including an introduction of Bayesian machine learning methods for Big Data regression.
Gary Koop, Jamie Cross, Aubrey Poon
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Bayesian analysis in econometrics
Journal of Econometrics, 1988Five basic propositions on Bayesian analysis in econometrics are put forward, discussed and illustrated through applications in Bayesian estimation, prediction, control and decision processes. The five propositions deal with the unity of science principle, the Jeffreys- Wrinch simplicity postulate, the prediction principle, a subjective concept of ...
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