In this paper, we consider a partially linear panel data model with cross-sectional dependence and non-stationarity. Meanwhile, we allow fixed effects to be correlated with the regressors to capture unobservable heterogeneity. Under a general spatial error dependence structure, we then...

We provide Markov chain Monte Carlo (MCMC) algorithms for computing the bandwidth matrix for multivariate kernel density estimation. Our approach is based on treating the elements of the bandwidth matrix as parameters to be estimated, which we do by optimizing the likelihood cross-validation...

The CAPM as the benchmark asset pricing model generally performs poorly in both developed and emerging markets. We investigate whether allowing the model parameters to vary improves the performance of the CAPM and the Fama-French model. Conditional asset pricing models scaled by conditional...

We apply a logistic smooth transition market model (LSTM) to a sample of returns on Australian industry portfolios to investigate whether bull and bear market betas differ. Unlike other studies, our LSTM model allows for smooth transition between bull and bear states and allows the data to...

Multivariate kernel regression is an important tool for investigating the relationship between a response and a set of explanatory variables. It is generally accepted that the performance of a kernel regression estimator largely depends on the choice of bandwidth rather than the kernel function....

We examine the response of real exchange rates to shocks in real exchange rate determinants, a monetary policy shock, and a fiscal policy shock in 30 countries over the period 1970-2008. The country set is divided into 4 groups - European, developed-country, Asian developing-country, and non...

This paper investigates international responses of key macroeconomic variables, particularly real exchange rates, to simultaneous shocks to productivity in the traded sector in eight Asian emerging and developing countries. We use panel estimation techniques to construct component submodels in a...

In There is evidence that exponential smoothing methods as well as time varying parameter models perform relatively well in forecasting comparisons. The aim of this paper is to introduce a new forecasting technique by integrating the exponential smoothing model with regressors whose coefficients...

Theoretical results on the properties of forecasts obtained using singular spectrum analysis are presented in this paper. The mean squared forecast error is derived under broad regularity conditions, and it is shown that the forecasts obtained in practice will converge to their population...

In this paper, we propose a panel data semiparametric varying-coefficient model in which covariates (variables affecting the coefficients) are purely categorical. This model has two features: first, fixed effects are included to allow for correlation between individual unobserved heterogeneity...