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Aggregate Comovements, Anticipation, and Business Cycles

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  • David R.F. Love

    (Department of Economics, Brock University)

Abstract

This paper points out that negative comovements between macroeconomic aggregates are commonly observed in US data and that this is not explained by conventional business cycle models which emphasize positive comovements only. We discuss how these facts can be readily explained in simple Neoclassical models by the dynamic responses to signals (news) about future economic fundamentals unrelated to current fundamentals. These "anticipation effects" are contrasted with the effects of immediate shocks to current fundamentals which are the main source of fluctuations in standard RBC models. Simulation results illustrate that the enriche model dynamics under the anticipation assumption can replicate both the positive and negative comovements observed in the data and magnifies the effects of shocks, without negative implications for the model's predictions regarding other moments.

Suggested Citation

  • David R.F. Love, 2007. "Aggregate Comovements, Anticipation, and Business Cycles," Working Papers 0704, Brock University, Department of Economics, revised Jun 2007.
    • Handle: RePEc:brk:wpaper:0704
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    1. David R. F. Love, 2011. "Aggregate Comovements, Anticipation, and Business Cycles," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2011(1), pages 93-110.

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    More about this item

    Keywords

    Comovements; Anticipation; News; Real Business Cycles; Equilibrium Dynamics;
    All these keywords.

    JEL classification:

    • E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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