We answer positively to this question by using Maximum Lq-Likelihood (or Deformed Likelihood) estimator. This is based on a parameter which measures the aggregate quote of judgment in the forecasting (game-based) system formed by three players—Forecaster, Policy Maker and Reality. For the first time in econometric literature, we apply this estimator to a dynamic system and derive a robust version of the Kalman Filter—the Deformed Kalman Filter (DKF). The evidence from U.S. data suggests that the judgmental dynamics exists and is correlated (but not coincident) with the phases of the Business Cycle. Furthermore its knowledge improves in-sample as well as out-of-sample estimation.

(2023). Can we estimate macroforecasters’ mis-behavior? [journal article - articolo]. In JOURNAL OF ECONOMIC DYNAMICS & CONTROL. Retrieved from https://hdl.handle.net/10446/240210

Can we estimate macroforecasters’ mis-behavior?

Zanetti Chini, Emilio
2023-01-01

Abstract

We answer positively to this question by using Maximum Lq-Likelihood (or Deformed Likelihood) estimator. This is based on a parameter which measures the aggregate quote of judgment in the forecasting (game-based) system formed by three players—Forecaster, Policy Maker and Reality. For the first time in econometric literature, we apply this estimator to a dynamic system and derive a robust version of the Kalman Filter—the Deformed Kalman Filter (DKF). The evidence from U.S. data suggests that the judgmental dynamics exists and is correlated (but not coincident) with the phases of the Business Cycle. Furthermore its knowledge improves in-sample as well as out-of-sample estimation.
articolo
2023
ZANETTI CHINI, Emilio
(2023). Can we estimate macroforecasters’ mis-behavior? [journal article - articolo]. In JOURNAL OF ECONOMIC DYNAMICS & CONTROL. Retrieved from https://hdl.handle.net/10446/240210
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/240210
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