Choice of Regression Models in Time Series Data

Abstract

Time series data possess distinct properties compared to cross section data. They have high temporal dependence, trend component and may have seasonal as well as cyclical patterns. A much-discussed issue in time series data is non-stationarity that highly influences the efficiency and consistency of regression estimates. In addition, time series regressions are most likely to suffer from spurious relationship. Thus, correct choice should be made regarding the regression models to obtain consistent estimates of the parameters and avoid spurious regression. This paper discusses the properties of time series data and the choice of appropriate regression models specifically in the context of finite samples. By discussing the relative strengths and limitations of the regression models that are used in time series data, this paper aims to contribute to the selection framework of regression models in time series analysis.