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Empirical Calibration of Adaptive Learning
- Source :
- KOF Working Papers, 392
- Publication Year :
- 2015
- Publisher :
- KOF Swiss Economic Institute, ETH Zurich, 2015.
-
Abstract
- 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.<br />KOF Working Papers, 392
- Subjects :
- Economics
real-time
COMPUTER APPLICATIONS IN ECONOMICS
forecasting
bounded rationality
Data processing, computer science
MACROECONOMIC MODELS (OPERATIONS RESEARCH)
ddc:330
COMPUTERANWENDUNGEN/WIRTSCHAFTSWISSENSCHAFTEN
MACHINE LEARNING (ARTIFICIAL INTELLIGENCE)
Recursive estimation
MASCHINELLES LERNEN (KÜNSTLICHE INTELLIGENZ)
MAKROÖKONOMISCHE MODELLE (OPERATIONS RESEARCH)
Expectations
Bounded rationality
Forecasting
Real-time
E37
D83
E03
ddc:004
expectations
recursive estimation
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- KOF Working Papers, 392
- Accession number :
- edsair.doi.dedup.....7db350a0a8584dfd78f39901b918e871