5 research outputs found

    Learning and Endogenous Business Cycles in a Standard Growth Model

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    Cyclical or chaotic competitive equilibria that do not exist under perfect foresight are shown to occur in a decentralized growth model under constant gain adaptive learning. This paper considers an economy populated by boundedly rational households making one-period ahead constant gain adaptive input price forecasts, and using simple expectation rules to predict long-run physical capital holdings and consumption. Under these hypotheses, lifetime decisions are derived as time unfolds, and analytical solutions to the representative household's problem exist for a standard class of preferences. Under various characteristics of the model's functional forms, competitive equilibrium trajectories under learning may exhibit opposite local stability properties depending whether the underlying information set accommodates all contemporary data. Calibrated to the U.S. economy, the model with boundedly rational households may exhibit endogenous business cycles around the permanent regime which is a saddle point under perfect foresightbounded rationality, constant gain adaptive learning, endogenous business cycles

    Least squares learning and business cycles

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    This paper investigates whether the neoclassical growth framework augmented with least squares estimated heuristic rules may reproduce U.S. business cycles. I consider various assumptions about the length of the information set, the influence of contemporaneous data on current forecasts, and the limit case in which learning is completed. Calibrated to the U.S. economy, this model may generate endogenous business cycles that do not exist under perfect foresight. If random productivity shocks are introduced, then the model is more volatile than under rational expectations or constant gain learning and reproduces some key U.S. business cycles stylized facts.Bounded rationality Least squares learning Endogenous business cycles