Behavioral learning equilibria in New Keynesian models

• 成果类型: Article
• 署名作者: Hommes, Cars; Mavromatis, Kostas; Ozden, Tolga; Zhu, Mei
• 署名单位: Bank of Canada; University of Amsterdam; European Central Bank; De Nederlandsche Bank NV; University of Amsterdam; Shanghai University of Finance & Economics; Shanghai University of Finance & Economics
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE1533
• 发表日期: 2023
• 页码: 1401-1445
• 关键词: Bounded rationality; adaptive learning; estimation; behavioral New Keynesian macro model; monetary policy; C11; D83; D84; E03; E62

摘要

We introduce Behavioral Learning Equilibria (BLE) into a multivariate linear framework and apply it to New Keynesian DSGE models. In a BLE, boundedly rational agents use simple, but optimal AR(1) forecasting rules whose parameters are consistent with the observed sample mean and autocorrelation of past data. We study the BLE concept in a standard 3-equation New Keynesian model and develop an estimation methodology for the canonical Smets and Wouters (2007) model. A horse race between Rational Expectations (REE), BLE, and constant gain learning models shows that the BLE model outperforms the REE benchmark and is competitive with constant gain learning models in terms of in-sample and out-of-sample fitness. Sample-autocorrelation learning of optimal AR(1) beliefs provides the best fit when short-term survey data on inflation expectations are taken into account in the estimation. As a policy application, we show that optimal Taylor rules under AR(1) expectations inherit history dependence and require a lower degrees of interest rate smoothing than REE.