Tackling Large Outliers in Macroeconomic Data with Vector Artificial Neural Network Autoregression

Arbeitspapier

We develop a regime switching vector autoregression where artificial neural networks drive time variation in the coefficients of the conditional mean of the endogenous variables and the variance covariance matrix of the disturbances. The model is equipped with a stability constraint to ensure non-explosive dynamics. As such, it is employable to account for nonlinearity in macroeconomic dynamics not only during typical business cycles but also in a wide range of extreme events, like deep recessions and strong expansions. The methodology is put to the test using aggregate data for the United States that include the abnormal realizations during the recent Covid-19 pandemic. The model delivers plausible and stable structural inference, and accurate out-of-sample forecasts. This performance compares favourably against a number of alternative methodologies recently proposed to deal with large outliers in macroeconomic data caused by the pandemic.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: CESifo Working Paper ; No. 9395

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Neural Networks and Related Topics ; Econometric Modeling: General ; Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
• Thema: nonlinear time series ; regime switching models ; extreme events ; Covid-19 ; macroeconomic forecasting

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Polito, Vito ; Zhang, Yunyi
• Ereignis: Veröffentlichung
• (wer): Center for Economic Studies and ifo Institute (CESifo)
• (wo): Munich
• (wann): 2021

Weitere Informationen

• Handle: http://hdl.handle.net/10419/248940
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ