Empirical Calibration of Adaptive Learning
Michele Berardi () and
Jaqueson Galimberti ()
No 15-392, KOF Working papers from KOF Swiss Economic Institute, ETH Zurich
Adaptive learning introduces persistence in the evolution of agents' beliefs over time. For applied purposes this is a convenient feature to help explain why economies present sluggish adjustments towards equilibrium. The pace of learning is directly determined by the gain parameter, which regulates how quickly new information is incorporated into agents' beliefs. We document renewed empirical calibrations of plausible gain values for adaptive learning applications to macroeconomic data. We cover a broad range of model speci- fications of applied interest. Our analysis also includes innovative approaches to the en- dogenous determination of time-varying gains in real-time, and a thorough discussion of the different theoretical interpretations of the learning gain. We also evaluate the merits of different approaches to the gain calibration according to their performance in forecasting macroeconomic variables and in matching survey forecasts. Our results indicate a great degree of heterogeneity in the gain calibrations according to the variable forecasted and the lag length of the model specifications. Calibrations to match survey forecasts are found to be lower than those derived according to the forecast- ing performance, suggesting some degree of bounded rationality in the speed with which agents update their beliefs.
Keywords: Expectations; Forecasting; Bounded rationality; Real-time; Recursive estimation (search for similar items in EconPapers)
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Journal Article: Empirical calibration of adaptive learning (2017)
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