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The second-order bias and mean squared error of nonlinear estimators
Optimal bandwidth choice for density-weighted averages
Nonparametric regression using Bayesian variable selection
A Bayesian analysis of nested logit models
A bayesian multivariate nonstationary time series model for estimating mutual relationships among variables
On priors and Bayes factors
Priors for unit root models
Markov-Normal analysis of iterative simulations before their convergence
Inference in successive sampling discovery models
Bayesian reduced rank regression in econometrics
Calculating posterior distributions and modal estimates in Markov mixture models
Models, prior information, and Bayesian analysis
The exact general formulae for the moments and the MSE dominance of the Stein-rule and positive-part Stein-rule estimators
Interpreting cointegrating vectors and common stochastic trends
A Bayesian approach to additive semiparametric regression
Estimating systems of equations with different instruments for different equations
Testing the joint hypothesis of rationality and neutrality under seasonal cointegration: The case of Korea
Efficient estimation and stratified sampling
Bayesian estimation of an autoregressive model using Markov chain Monte Carlo
Qualitative and asymptotic performance of SNP density estimators
Testing the adequacy of smooth transition autoregressive models
Closing the GARCH gap: Continuous time GARCH modeling
Fractionally integrated generalized autoregressive conditional heteroskedasticity
分数整合广义自回归条件异方差模型
The new class of Fractionally Integrated Generalized AutoRegressive Conditionally Heteroskedastic (FIGARCH) processes is introduced. The conditional variance of the process implies a slow hyperbolic rate of decay for the influence of lagged squared innovations. Unlike I(d) processes for the mean, Maximum Likelihood Estimates (MLE) of the FIGARCH parameters are argued to be T^(1/2)-consistent. The small-sample behavior of an approximate MLE procedure is assessed through a…