Econometric inference using simulation techniques
Noted international researchers present the very latest knowledge in the field. This essential work covers the three main areas of econometric inference where the use of simulation methods has been successful--Bayesian inference, classical inference, the solution and stochastic simulation of dynamic econometric models, especially general equilibrium models.
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Bayesian Estimation of Manufacturing Effects in a Fuel Economy Model
Bayesian Treatment of the Independent Studentf Linear Model
A Bayesian Analysis
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algorithm analysis approximation arrears Assumption Bayesian computed conditional density conditional variances consistent estimator converges covariance matrix debt defined denote density function distributed disturbances dynamic Econometrica efficient employer EMSM estimator error estimation methods evaluation exogenous variables filter Gaussian Geweke Gibbs sampler given Gourieroux hours supply importance sampling independent indirect estimator institutional wage iterations labour market latent variables LDV models likelihood function linear model Markov maximum likelihood method of simulated minimum wage ML estimator Monfort Monte Carlo integration multivariate non-linear non-stationarity normally distributed observed obtained optimal paper plim posterior density posterior distribution posterior mean posterior odds ratio prior distribution probability probit probit model procedure random numbers random variable real business cycle repayment problems Section simulated moments Simulation Techniques SQML estimator standard deviation stationarity stationarity condition Statistical stochastic structural parameters Table Theorem values variance-covariance matrix vector weak stationarity wmax wmin worker