Forecasting Industrial Production and Inflation in Turkey with Factor Models

In this paper, industrial production growth and core inflation are forecasted using a large number of domestic and international indicators. Two methods are employed, factor models and forecast combination, to deal with the curse of dimensionality problem stemming from the availability of ever growing data sets. A comprehensive analysis is carried out to understand the sensitivity of the forecast performance of factor models to various modelling choices. In this respect, effects of factor extraction method, number of factors, data aggregation level and forecast equation type on the forecasting performance are analyzed. Moreover, the effect of using certain data blocks such as European Union variables and interest rates on the forecasting performance is evaluated as well. Out-of-sample forecasting exercise is conducted for two consecutive periods to assess the stability of the forecasting performance. Results show that best performing specifications depend on the type of the variable that one wants to forecast, the forecast horizon and the sample period used to evaluate the out-of-sample forecasting performance. Factor models perform better than the combination of bi-variate forecasts.