Evaluating a seasonal autoregressive moving average model with an exogenous variable for short-term timber price forecasting

Abstract

In this paper roundwood prices were forecasted by means of the following time series models: (1) classical univariate autoregressive moving average (ARIMA), (2) seasonal univariate (SARIMA), and (3) seasonal bivariate with an exogenous variable (SARIMAX). The data consisted of time series of the nominal prices of pine, spruce, beech, birch, and alder roundwood and the construction confidence index (CCI) in Poland for the years 2005–2020. The results indicate that roundwood prices can be evaluated by the implemented time series models, with forecast accuracy depending on the time horizon, the model applied, and tree species. The incorporation of a separate component directly describing seasonal fluctuations into a conventional ARIMA time series model significantly improved the accuracy of roundwood price forecasts exhibiting seasonal variation. The CCI lagged by one to three quarters were found to be significantly correlated with the prices of roundwood. A SARIMAX model incorporating the CCI as a leading variable was able to account for changes in the business cycle, thus increasing the accuracy of short-term forecasts of the prices of roundwood used in the construction industry.