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Learning Dynamics with Data (Quasi-) Differencing

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  • Pei Kuang

Abstract

The paper studies learning with data (quasi-)differencing where agents need to (quasi-)difference data and then use an otherwise standard least squares learning procedure. It (1) establishes that the E-stability Principle is still valid for analyzing the convergence of the learning with (quasi-) differencing data process to the Rational Expectations Equilibrium (REE), (2) provides new perspectives on the stability of the Rational Expectations bubble solutions, and equilbrium selection under adaptive learning, (3) demonstrates the importance of consideringagents' uncertainty and learning about the long-run growth of endogenous variables in dynamic macroeconomic models, (4) provides recommendations and a caveat on addressing model misspecifications in econometric practice, and (5) showslearning with (quasi-) differencing data helps understand some salient features of fluctuations in asset prices, inflation and aggregate economic activities.

Suggested Citation

  • Pei Kuang, 2014. "Learning Dynamics with Data (Quasi-) Differencing," Discussion Papers 15-06, Department of Economics, University of Birmingham.
    • Handle: RePEc:bir:birmec:15-06
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    File URL: https://repec.cal.bham.ac.uk/pdf/15-06.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    Expectations; Convergence; Long-Run Growth; Serial Correlation; Bubbles; Underparameterization;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)

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