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Higher-order improvements of the parametric bootstrap for long-memory Gaussian processes
Bootstrapping cointegrating regressions
Bootstrapping the Box–Pierce Q test: A robust test of uncorrelatedness
A bootstrap theory for weakly integrated processes
A consistent bootstrap test for conditional density functions with time-series data
Bootstrap specification tests for linear covariance stationary processes
Bootstrap conditional distribution tests in the presence of dynamic misspecification
Alternative bootstrap procedures for testing cointegration in fractionally integrated processes
Exact permutation tests for non-nested non-linear regression models
Bootstrapping GMM estimators for time series
The power of bootstrap and asymptotic tests
GMM estimators with improved finite sample properties using principal components of the weighting matrix, with an application to the dynamic panel data model
Testing for stochastic dominance using the weighted McFadden-type statistic
A flexible prior distribution for Markov switching autoregressions with Student-t errors
Monte Carlo methods for estimating, smoothing, and filtering one- and two-factor stochastic volatility models
Estimating the probability of leaving unemployment using uncompleted spells from repeated cross-section data
Semiparametric efficient adaptive estimation of asymmetric GARCH models
Bounding parameters in a linear regression model with a mismeasured regressor using additional information
Asymptotic normality of narrow-band least squares in the stationary fractional cointegration model and volatility forecasting
Simulation-based estimation of peer effects
Estimation of models with grouped and ungrouped data by means of “2SLS”
Estimation of stochastic frontier production functions with input-oriented technical efficiency
Limited information Bayesian analysis of a simultaneous equation with an autocorrelated error term and its application to the U.S. gasoline market
自相关误差联立方程的有限信息贝叶斯分析及美国汽油市场应用
Using Markov Chain Monte Carlo algorithms within the limited information Bayesian framework, we estimate the parameters of the structural equation of interest and test weak exogeneity in a simultaneous equation model with white noise as well as autocorrelated error terms. A numerical example and an estimation of the supply and demand equations of the U.S. gasoline market show that if we ignore autocorrelation we obtain unreasonable posterior distributions of the parameters…