Data and Code for: "Forecasting in the Presence of Instabilities"
Principal Investigator(s): View help for Principal Investigator(s) Barbara Rossi, ICREA-Univ. Pompeu Fabra, BGSE and CREI
Version: View help for Version V1
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| code | 10/25/2021 05:28:PM | ||
| data | 10/26/2021 08:17:AM | ||
| environment | 10/25/2021 05:28:PM | ||
| metadata | 10/25/2021 05:28:PM | ||
| results | 10/25/2021 05:28:PM | ||
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text/x-web-markdown | 1.6 KB | 10/25/2021 01:27:PM |
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application/pdf | 30.6 KB | 10/25/2021 01:27:PM |
Project Citation:
Rossi, Barbara. Data and Code for: “Forecasting in the Presence of Instabilities.” Nashville, TN: American Economic Association [publisher], 2021. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2021-10-26. https://doi.org/10.3886/E147225V1
Project Description
Summary:
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These are the data and codes to replicate the figures in: "Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them".
This paper provides guidance on how to evaluate and improve the forecasting ability of models in the presence of instabilities, which are widespread in economic time series. Empirically relevant examples include predicting the financial crisis of 2007-2008, as well as, more broadly, fluctuations in asset prices, exchange rates, output growth and inflation. In the context of unstable environments, I discuss how to assess models' forecasting ability; how to robustify models' estimation; and how to correctly report measures of forecast uncertainty. Importantly, and perhaps surprisingly, breaks in models' parameters are neither necessary nor sufficient to generate time variation in models' forecasting performance: thus, one should not test for breaks in models' parameters, but rather evaluate their forecasting ability in a robust way. In addition, local measures of models' forecasting performance are more appropriate than traditional, average measures.
This paper provides guidance on how to evaluate and improve the forecasting ability of models in the presence of instabilities, which are widespread in economic time series. Empirically relevant examples include predicting the financial crisis of 2007-2008, as well as, more broadly, fluctuations in asset prices, exchange rates, output growth and inflation. In the context of unstable environments, I discuss how to assess models' forecasting ability; how to robustify models' estimation; and how to correctly report measures of forecast uncertainty. Importantly, and perhaps surprisingly, breaks in models' parameters are neither necessary nor sufficient to generate time variation in models' forecasting performance: thus, one should not test for breaks in models' parameters, but rather evaluate their forecasting ability in a robust way. In addition, local measures of models' forecasting performance are more appropriate than traditional, average measures.
Funding Sources:
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ERC (615608);
Cerca Programme/Generalitat de Catalunya (Not available);
Spanish Ministry of Economy and Competitiveness, through the Severo Ochoa Programme for Centres of Excellence in R&D (SEV-2015-0563)
Scope of Project
Subject Terms:
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Forecasting;
Instability;
Time-variation;
Inflation;
Breaks;
Density forecasts;
Great Recession;
Output growth;
business cycles
JEL Classification:
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E30 Prices, Business Fluctuations, and Cycles: General (includes Measurement and Data)
E37 Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
E40 Money and Interest Rates: General
E47 Money and Interest Rates: Forecasting and Simulation: Models and Applications
E52 Monetary Policy
E30 Prices, Business Fluctuations, and Cycles: General (includes Measurement and Data)
E37 Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
E40 Money and Interest Rates: General
E47 Money and Interest Rates: Forecasting and Simulation: Models and Applications
E52 Monetary Policy
Methodology
Data Source:
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The data are collected from several public sources listed in the paper.
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