Recurrent Hyperinflations and Learning

By Albert Marcet, Juan Pablo Nicolini. 2003

295 citations recorded by the source corpus.

This paper uses a model of boundedly rational learning to account for the observations of recurrent hyperinflations in the last decade. We study a standard monetary model where the fully rational expectations assumption is replaced by a formal definition of quasi-rational learning. The model under learning is able to match remarkably well some crucial stylized facts observed during the recurrent hyperinflations experienced by several countries in the 80's. We argue that, despite being a small departure from rational expectations, quasi-rational learning does not preclude falsifiability of the model and it does not violate reasonable rationality requirements.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/2487b6fdf85af6f2fa3ab73e947f91931a9bbc9c

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In Economics and Econometrics

Citations in the corpus

Monetary policy, learning and the speed of convergence (2007, background): “Evans and Honkapohja (1993), Timmerman (1996), Sargent (1999) and Marcet and Nicolini (2003) make use of learning models not only to study the asymptotic properties of the equilibrium attainable under learning, but as a compelling alternative to study economic behaviors in the short and medium-run.”
Liquidity traps, learning and stagnation ☆ (2008, background): “See Evans, Honkapohja, and Marimon (2001) and Marcet and Nicolini (2003).”
Learning from Inflation Experiences (2016, background): “Marcet and Nicolini (2003) propose a model where individuals update their expectations with decreasing gain stable periods, but they switch to a constant gain when a large prediction error is detected”
Optimal Monetary Policy When Agents are Learning (2010, unknown): “Endogenous expectation formation could be formulated for example along the lines of Marcet and Nicolini (2003) where agents dynamically switch between predictor use depending on the last forecast error.”
Anchored Inflation Expectations (2020, unknown): “Following Marcet and Nicolini (2003) firms use two different, commonly used, models: either i) a decreasing-gain algorithm or ii) a constant-gain algorithm. The first estimator is ordinary least squares which is consistent with estimating a time-invariant inflation mean. The gain is the inverse of t”
Stock Market Volatility and Learning (2008, methodology): “We now introduce a learning model in which the gain switches over time, as in Marcet and Nicolini (2003).49 The idea is to combine constant gain with OLS, using the former in periods in which forecast errors are large and the latter when the forecast error is low.”
Learning and Macroeconomics (2009, background): “Marcet and Nicolini (2003) aim to provide a unified theory to explain the empirical regularities of Latin American hyperinflations experienced by many countries in the 1980s using the seigniorage model of inflation.”
Wanting robustness in macroeconomics (2010, background): “See Marcet and Nicolini (2003), Sargent, Williams, and Zha (2006), Sargent, Williams, and Zha (2009), and Carboni and Ellison (2009) for empirical applications.”