Software

The results in "A New Tests for Nonlinear Hypotheses under Distributional and Local Parametric Misspecification" can be replicated by using the code given in Supplementary Materials.

The simulation results in "Distribution of Test Statistics under Parameter Uncertainty for Time Series Data: An Application to Testing Skewness, Kurtosis and Normality" can be replicated by using code. The code is written in MATLAB.

The stochastic tail index model in our paper "Bayesian Estimation of Stochastic Tail Index from High-Frequency Financial Data" can be estimated by using code. The code is written in MATLAB.

The SARAR (1,1) model described in our paper can be estimated by using the rgmm_sac function. This function, written in MATLAB, requires the Spatial Econometric Toolbox provided by James LeSage. The user should add the toolbox to the path before using the rgmm_sac function. For a demo illustration, see the rgmm_sacd file.

The SARAR(1,0) model described in Lin and Lee (2010) can be estimated by the rgmm_sar function. This function also requires the Spatial Econometric Toolbox provided by James LeSage.

The SARAR(1,1) model described in Kelejian and Prucha (2010) can be estimated by the sac_gmm_het function. This function also requires the Spatial Econometric Toolbox provided by James LeSage.