Essays in Econometric Forecasting with Applications to Macroeconomics and Finance
This thesis comprises three chapters on econometric forecasting, with applications to macroeconomics and finance. The first chapter introduces a testing procedure for evaluating superior predictive ability (SPA) in unstable environments. Therefore, it allows one to detect time-varying superiority. A...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/691982 |
| Acceso en línea: | http://hdl.handle.net/10803/691982 |
| Access Level: | acceso embargado |
| Palabra clave: | Econometric forecasting Pronóstico econométrico Macroeconomics Macroeconomía Finance Finanzas 33 |
| Sumario: | This thesis comprises three chapters on econometric forecasting, with applications to macroeconomics and finance. The first chapter introduces a testing procedure for evaluating superior predictive ability (SPA) in unstable environments. Therefore, it allows one to detect time-varying superiority. Applied to downside risk forecasts for the U.S. economy, it reveals substantial time-varying heterogeneity in the forecasting performance of the commonly used method (a quantile regression equipped with financial conditions). In the second chapter, we present a variant of the standard SPA test that assesses the predictive ability of one particular method against many alternatives when forecasting a panel of time series. Our empirical analysis assesses the forecasting performance of the factor model against different machine learning techniques when predicting macroeconomic variables in the U.S. (FRED-MD). We find that while the factor model dominates at short horizons, yet it is surpassed by simple methods at longer horizons, with results varying across different macroeconomic categories. Finally, the third chapter proposes a hybrid model combining factor models and largedimensional regularized regressions for intra-daily volume prediction in large panels of stocks. Applied to the STOXX 600 Index, our results demonstrate its superior performance over traditional univariate models. |
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