This paper introduces a new specification for the heterogeneous autoregressive (HAR) model for the realized volatility of S&P500 index returns. In this new model, the coefficients of the HAR are allowed to be time-varying with unknown functional forms. We propose a local linear method for...

In this paper we propose a categorical time-varying coefficient translog cost function to estimate technical change and productivity. The primary feature of this model is that each of its coefficients is expressed as a nonparametric function of a categorical time variable, thereby allowing each...

We investigate some estimation and testing issues for a class of high-dimensional near unit root time series models. We first study the asymptotic behavior of the first k largest eigenvalues of the sample covariance matrices of the time series model. Then we propose a new estimator for the...

This paper studies nonlinear cointegrating models with time-varying coefficients and multiple nonstationary regressors using classic kernel smoothing methods to estimate the coefficient functions. Extending earlier work on nonstationary kernel regression to take account of practical features of...

This paper studies a model with both a parametric global trend and a nonparametric local trend. This model may be of interest in a number of applications in economics, finance, ecology, and geology. The model nests the parametric global trend model considered in Phillips (2007) and Robinson...

We propose an estimation methodology for a semiparametric quantile factor panel model. We provide tools for inference that are robust to the existence of moments and to the form of weak cross-sectional dependence in the idiosyncratic error term. We apply our method to CRSP daily data

In this paper, we consider a class of time-varying panel data models with individual-specific regression coefficients and common factors where both the serial correlation and cross-sectional dependence among error terms can be present. Based on an initial estimator of factors, we propose a...

This paper studies a semi-parametric single-index predictive regression model with multiple nonstationary predictors that exhibit co-movement behaviour. Orthogonal series expansion is employed to approximate the unknown link function in the model and the estimator is derived from an optimization...

This paper considers the estimation of a semi-parametric single-index regression model that allows for nonlinear predictive relationships. This model is useful for predicting financial asset returns, whose observed behavior is described by a stationary process, when the multiple non-stationary...