Nowcasting and Short-term Forecasting Turkish GDP: Factor-MIDAS Approach

Nowcasting and Short-term Forecasting Turkish GDP: Factor-MIDAS Approach

21/11

Selçuk Gül, Abdullah Kazdal

English

July 2021

This paper compares several nowcast approaches that account for mixed-data frequency and “ragged-edge” problems. More specifically, it examines the relative performance of the factor-augmented MIDAS approach (Marcellino and Schumacher; 2010) in nowcasting Turkish GDP with respect to benchmark forecasts. By using 40 monthly indicators in factor extraction, several combinations of the factor-MIDAS models are estimated. Recursive pseudo-out-of sample forecasting exercise in evaluating the alternative models’ performance suggests that factor-augmented MIDAS performs better than the benchmarks, especially in nowcasting. However, they do not provide much information content to forecasting a quarter ahead. Results indicate that taking into account the “ragged-edge” characteristic of the data helps improve the predictive ability of the nowcast models. Besides, dynamic factor extraction methods provide better predictions than the static factor extraction methods.

Forecasting, Mixed frequency, Factor-MIDAS  

C52; C53; E37