This paper introduces a new speci…cation for the heterogeneous autoregressive (HAR) model for the realized volatility of S&P500 index returns. In this new model, the coe¢ cients of the HAR are allowed to be time-varying with unknown functional forms. We propose a local linear method for estimating this TVC-HAR model as well as a bootstrap method for constructing con…dence intervals for the time...

IIn recent years, policymakers have generally relied on regulatory policies to address financial stability concerns. However, our understanding of these policies and their efficacy in curbing housing prices is limited. In this paper, we examine the impact of three regulatory tools i.e. LTV (loan to value) ratio, reserve requirement rate (RR), and capital adequacy ratio (CAR) on housing price in...

In myoelectric prostheses design, it is normally assumed that the necessary control information can be extracted from the surface myoelectric signals. In the pattern classification paradigm for controlling myoelectric prosthesis, the autoregressive (AR) model coefficients are generally considered an efficient and robust feature set. However, no formal statistical methodologies or tests are repo...

A novel modelling scheme that can be used to estimate and track time-varying properties of nonstationary signals is investigated. This scheme is based on a class of time-varying AutoRegressive with an eXogenous input (TVARX) models where the associated time-varying parameters are represented by multi-wavelet basis functions. The orthogonal least square (OLS) algorithm is then applied to refine ...

Over the last decades more and more attention has been paid to the problem how to fit a parametric model of time series with time-varying parameters. A typical example is given by autoregressive models with time-varying parameters (tvAR processes). We propose a procedure to fit such time-varying models to general nonstationary processes. The estimator is a maximum Whittle likelihood estimator o...

Abstract We develop a new statistical model to analyse time-varying ranking data. The can be used with large number of ranked items, accommodates exogenous covariates and partial rankings, is estimated via the maximum likelihood in straightforward manner. Rankings are modelled using Plackett–Luce distribution worth parameters that follow mean-reverting time series process. To capture dependence...