Econometric Inference Using Simulation TechniquesHerman K. van Dijk, Alain Monfort, Bryan W. Brown This book provides a comprehensive assessment of the latest simulation techniques, and examines the three main areas of econometric inference where the use of simulation methods has been successful; Bayesian inference, classical inference, and the solution and stochastic simulation of dynamic econometric models, in particular general equilibrium models. |
Contents
Bayesian Estimation of Manufacturing Effects in a Fuel Economy Model | 21 |
Bayesian Treatment of the Independent Studentt Linear Model | 35 |
A Bayesian Analysis | 57 |
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Common terms and phrases
algorithm analysis approximation arrears Assumption az² Bayesian computed conditional density conditional variances consistent estimator converges correlation covariance matrix debt defined denote density function Dijk disturbances dynamic Econometrica EM algorithm EMSM estimator equation error estimation methods evaluation exogenous variables filter GARCH(1 Gaussian Geweke Gibbs sampler given Gouriéroux Hajivassiliou hours supply importance sampling indirect estimator inference institutional wage labour market latent variables likelihood function linear model maximum likelihood McFadden minimum wage ML estimator Monfort Monte Carlo integration non-linear non-stationarity normally distributed observed optimal plim posterior density posterior distribution posterior odds ratio prior distribution probability procedure random numbers random variable real business cycle Section simulated moments Simulation Techniques SQML estimator standard deviation stationarity stationarity condition Statistical stochastic structural parameters Student-t Table values variance-covariance matrix vector weak stationarity Wmax wmin worker y₁