We provide a new econometric methodology to detect the bias due to strategic interaction induced by subjective learning. This methodology relies on (i) a new definition of coherence based on the Likelihood Principle, specifically designed for economic forecasting; (ii) an environment named “Scoring Structure”, where a Forecast User interacts with a Forecast Producer and Reality. A formal test for the null hypothesis of linearity in the Structure is introduced. Linearity implies that forecasts are strategically coherent with evaluations and viceversa. The new test has good small-sample properties and behaves consistently with theoretical requirements. Three case studies on the Federal Reserve Bank’s, the Bank of England’s and the Norges Bank’s forecasts support the endemic nature of the strategic judgment in Macroeconomics. The economic interpretation of the results are discussed.

(2021). Strategic judgment: its game-theoretic foundations, its econometric elicitation [working paper]. Retrieved from http://hdl.handle.net/10446/196554

Strategic judgment: its game-theoretic foundations, its econometric elicitation

Zanetti Chini, Emilio
2021-01-01

Abstract

We provide a new econometric methodology to detect the bias due to strategic interaction induced by subjective learning. This methodology relies on (i) a new definition of coherence based on the Likelihood Principle, specifically designed for economic forecasting; (ii) an environment named “Scoring Structure”, where a Forecast User interacts with a Forecast Producer and Reality. A formal test for the null hypothesis of linearity in the Structure is introduced. Linearity implies that forecasts are strategically coherent with evaluations and viceversa. The new test has good small-sample properties and behaves consistently with theoretical requirements. Three case studies on the Federal Reserve Bank’s, the Bank of England’s and the Norges Bank’s forecasts support the endemic nature of the strategic judgment in Macroeconomics. The economic interpretation of the results are discussed.
2021
ZANETTI CHINI, Emilio
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/196554
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