Nowcasting Turkish GDP Growth with Targeted Predictors: Fill in the Blanks

This paper analyzes four dimensions of forecasting GDP growth using monthly data. Firstly, we use AR, VAR, BVAR, mean-growth and zero month-on-month change for forecasting the missing monthly data at the end of forecasting sample due to asynchronous nature of the release of the indicators. Second dimension is using a relatively large data set and testing some indicators that are not frequently used for forecasting GDP growth but due to timeliness have the potential to contribute to the forecasting performance. We analyze data from a career website, freight information from maritime transportation, capacity utilization of available plane seats, tax revenues of the central government and credit and debit card transaction volumes. Third dimension is comparing the performance of model averaging and factor models that are used to incorporate information content of large data sets to the forecasting process. Finally, we look at the forecasting performance of a core data set that is selected by a shrinkage method, namely LASSO. Our findings show that using VAR models with financial and survey indicators for forecasting missing monthly data improves short term GDP forecasting performance relative to other alternatives. We find that forecasting using targeted predictors rather than using an unscreened large data set helps to reduce forecasting errors considerably. Factor model approach performs better than forecast combination. So, using a targeted data set for factor extraction and forecasting missing monthly data with VAR performs relatively better than other specifications for producing timely and accurate nowcasts.