Forecasting bubbles with mixed causal-noncausal autoregressive models

Voisin, Elisa and Hecq, Alain (2019): Forecasting bubbles with mixed causal-noncausal autoregressive models.

Abstract

This paper investigates one-step ahead density forecasts of mixed causal-noncausal models. It analyses and compares two data-driven approaches. The paper focuses on explosive episodes and therefore on predicting turning points of bubbles. Guidance in using these approximation methods are presented with the suggestion of using both of the approaches as they jointly carry more information. The analysis is illustrated with an application on Nickel prices.

• Item Type: MPRA Paper
• Language: English
• Keywords: Noncausal models, forecasting, predictive densities, bubbles, simulations-based forecasts
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics

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