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.

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...

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 have demonstrated the implications of incorrectly normalising the parameters of a reduced rank regression model to achieve global identification, and presented a method for estimating this model without using the ordering restrictions imposed in previous Bayesian and frequentist...

A Bayesian approach to option pricing is presented, in which posterior inference about the underlying returns process is conducted implicitly, via observed option prices. A range of models which allow for conditional leptokurtosis, skewness and time-varying volatility in returns, are considered,...

Modelling the incidence of self-employment has traditionally proved problematic. Whilst the individual supply side characteristics of the self-employed are well documented, the literature has largely neglected (or misspecified) demand side aspects. In this paper we present results from an...

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...