Exact small-sample inference in stationary, fully regular, dynamic demand models

DOI10.1016/S0304-4076(99)00066-4zbMath0998.62098MaRDI QIDQ1580339

Philippe J. Deschamps

Publication date: 13 September 2000

Published in: Journal of Econometrics (Search for Journal in Brave)

zbMATH Keywords

Markov chain Monte Carlovector autoregressionstruncated normalexact likelihoodAllais intensities

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Numerical analysis or methods applied to Markov chains (65C40) Consumer behavior, demand theory (91B42)

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Cites Work

• Estimation of functions of population means and regression coefficients including structural coefficients. A minimum expected loss (MELO) approach
• Simple conditions for the convergence of the Gibbs sampler and Metropolis-Hastings algorithms
• Bayes inference in regression models with ARMA\((p,q)\) errors
• Full maximum likelihood estimation of dynamic demand models
• Markov chains for exploring posterior distributions. (With discussion)
• Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models
• Marginal Likelihood from the Gibbs Output
• The Random Utility Hypothesis and Inference in Demand Systems
• Estimation and Hypothesis Testing in Dynamic Singular Equation Systems
• An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator
• Analysis of multivariate probit models
• Bayes Factors
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