This paper compares the performance of several tests for stochastic dominance up to order three using Monte Carlo methods. The tests considered are the Davidson and Duclos (2000) test, the Anderson test (1996) and the Kaur, Rao and Singh (1994) test. Only unpaired samples of independent...

In this paper we construct a test for the difference parameter d in the fractionally integrated autoregressive moving-average (ARFIMA) model. Obtaining estimates by smoothed spectral regression estimation method, we use the moving blocks bootstrap method to construct the test for d. The results...

This paper is concerned with model selection based on penalized maximized log likelihood function. Its main emphasis is on how these penalities might be chosen in small samples to give good statistical properties.

A Bayesian approach is presented for nonparametric estimation of an additive regression model with autocorrelated errors.

This paper presents a Markov chain Monte Carlo (MCMC) algorithm to estimate parameters and latent stochastic processes in the asymmetric stochastic volatility (SV) model, in which the Box-Cox transformation of the squared volatility follows an autoregressive Gaussian distribution and the...

We present a local linear estimator with variable bandwidth for multivariate nonparametric regression. We prove its consistency and asymptotic normality in the interior of the observed data and obtain its rates of convergence. This result is used to obtain practical direct plug-in bandwidth...

In this paper we study a statistical method of implementing quasi-Bayes estimators for nonlinear and nonseparable GMM models, that is motivated by the ideas proposed in Chernozhukov and Hong (2003) and Creel and Kristensen (2011) and that combines simulation with nonparametric regression in the...

We propose a new generic method ROPES (Regularized Optimization for Prediction and Estimation with Sparse data) for decomposing, smoothing and forecasting two-dimensional sparse data. In some ways, ROPES is similar to Ridge Regression, the LASSO, Principal Component Analysis (PCA) and...

The aim of this paper is to examine the measurement of persistence in a range of time series models nested in the framework of Cramer (1961). This framework is a generalization of the Wold (1938) decomposition for stationary time series which, in addition to accommodating the standard I(0) and...

This paper considers the construction of model selection procedures based on choosing the model with the largest maximised log-likelihood mimus a penalty, when key parameters are restricted to be in a closed interval. The approach adopted is based on King et al.'s (1995) representative models...