Nowcasting real GDP in Tunisia using large datasets and mixed-frequency models

Arbeitspapier

This study aims to construct a new monthly leading indicator for Tunisian economic activity and to forecast Tunisian quarterly real GDP (RGDP) using several mixed-frequency models. These include a mixed dynamic factor model, unrestricted mixed-data sampling (UMIDAS), and a threepass regression filter (3PRF) developed at the Central Bank of Tunisia, based on a monthly/quarterly set of economic and financial indicators as predictors. Our methodology is based on direct and indirect approaches, and the direct approach nowcasts aggregate RGDPs . The indirect approach is a disaggregated approach based on the output side of GDP (manufacturing, non-manufacturing, and services) using a set of available monthly indicators by sector. Furthermore, mixed-frequency dynamic factor models and unrestricted MIDAS perform well in terms of root mean squared errors compared to the benchmark model VAR (2). The forecast errors derived from the disaggregated approach during the recent COVID period are smaller than those derived from classical models such as VAR (2). In our model, we used indicators such as electricity consumption by sector, stock market index detailed by sector, and international economic surveys to capture the pandemic effect. The financial variables improve forecasting for all horizons. Additionally, we find that it is better to employ several UMIDAS-ARs by each component of GDP at constant prices and to pool the results rather than relying on aggregated GDP, specifically in volatile times.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Graduate Institute of International and Development Studies Working Paper ; No. HEIDWP02-2022

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications ; Large Data Sets: Modeling and Analysis ; Trade: Forecasting and Simulation ; Macroeconomic Analyses of Economic Development
• Thema: Mixed-Frequency Data Sampling ; Nowcasting ; short-term forecasting

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Ben Rhomdhane, Hagher ; Benlallouna, Brahim Mehdi
• Ereignis: Veröffentlichung
• (wer): Graduate Institute of International and Development Studies
• (wo): Geneva
• (wann): 2022

Weitere Informationen

• Handle: http://hdl.handle.net/10419/253254
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ