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• W4382361851 abstract "Abstract Most nonlinear vector autoregressive methods in the econometric literature are based on specific functional forms, such as the smooth transition autoregressive model. This study proposes a general form of the nonlinear vector autoregressive model based on global approximators, such as neural networks, Volterra, and Weiner series. The simulation results of 20 linear and nonlinear multivariate time series processes indicate that nonlinear vector autoregressive methods, especially multi‐output neural networks, are more accurate based on the root mean square error and model confidence set criteria. Applying the global approximator approach to a small‐scale macroeconometric model reveals that the new approach can improve forecast accuracy compared to linear and other nonlinear vector error correction models. In addition, forecasting the relevant variables in a typical exchange rate and monetary policy models based on nonlinear specifications gives more successful results than in the linear case." @default.
• W4382361851 created "2023-06-29" @default.
• W4382361851 creator A5015525103 @default.
• W4382361851 creator A5071941378 @default.
• W4382361851 date "2023-06-28" @default.
• W4382361851 modified "2023-10-14" @default.
• W4382361851 title "Forecast accuracy of the linear and nonlinear autoregressive models in macroeconomic modeling" @default.
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• W4382361851 doi "https://doi.org/10.1002/for.3002" @default.
• W4382361851 hasPublicationYear "2023" @default.
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