Using Cyclical Components to Improve the Forecasts of the Stock Market and Macroeconomic Variables

Kenneth R. Szulczyk, Shibley Sadique
2018 Journal of Modern Applied Statistical Methods  
Economic variables such as stock market indices, interest rates, and national output measures contain cyclical components. Forecasting methods excluding these cyclical components yield inaccurate out-of-sample forecasts. Accordingly, a three-stage procedure is developed to estimate a vector autoregression (VAR) with cyclical components. A Monte Carlo simulation shows the procedure estimates the parameters accurately. Subsequently, a VAR with cyclical components improves the root-mean-square
more » ... oot-mean-square error of out-of-sample forecasts by 50% for a stock market model with macroeconomic variables. Economic variables such as stock market indices, interest rates, and national output measures contain cyclical components. Forecasting methods excluding these cyclical components yield inaccurate out-of-sample forecasts. Accordingly, a three-stage procedure is developed to estimate a vector autoregression (VAR) with cyclical components. A Monte Carlo simulation shows the procedure estimates the parameters accurately. Subsequently, a VAR with cyclical components improves the root-mean-square error of out-of-sample forecasts by 50% for a stock market model with macroeconomic variables.
doi:10.22237/jmasm/1539003896 fatcat:2t5ozekutjafra3f6255j6sy74