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Identifying Nonlinear Components by Random Fields in the US GNP Growth. Implications for the Shape of the Business Cycle

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  • Dahl Christian M.

    (Purdue University)

  • Gonzalez-Rivera Gloria

    (University of California, Riverside)

Abstract

Within a flexible parametric regression framework (Hamilton, 2001) we provide further evidence on the existence of a nonlinear component in the quarterly growth rate of the US real GNP. We implement a battery of new tests for neglected nonlinearity based on the theory of random fields (Dahl and Gonzalez-Rivera, 2003). We find that the nonlinear component is driven by the fifth lag of the growth rate. We show that our model is superior to linear and nonlinear parametric specifications because it produces a business cycle that when dissected with the BBQ algorithm mimics very faithfully the characteristics of the actual US business cycle. On understanding the relevance of the fifth lag, we find that the nonparametrically estimated conditional mean supports parametric specifications that allow for three phases in the business cycle: rapid linear contractions, aggressive short-lived convex early expansions, and moderate/slow relatively long concave late expansions.

Suggested Citation

  • Dahl Christian M. & Gonzalez-Rivera Gloria, 2003. "Identifying Nonlinear Components by Random Fields in the US GNP Growth. Implications for the Shape of the Business Cycle," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(1), pages 1-35, April.
    • Handle: RePEc:bpj:sndecm:v:7:y:2003:i:1:n:2
    DOI: 10.2202/1558-3708.1123
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    References listed on IDEAS

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    Cited by:

    1. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008.
    2. Saad Ahmad, 2020. "Identifying a robust policy rule for the Fed's response to financial stress," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 25(4), pages 565-578, October.
    3. Kalli, Maria & Griffin, Jim E., 2018. "Bayesian nonparametric vector autoregressive models," Journal of Econometrics, Elsevier, vol. 203(2), pages 267-282.
    4. Ricardo Gonçalves Silva, 2004. "Bayesian Semiparametric Regression for Autoregressive Models with Possible Unit Roots," Econometrics 0405002, University Library of Munich, Germany.
    5. Blake LeBaron, 2013. "Heterogeneous Agents and Long Horizon Features of Asset Prices," Working Papers 63, Brandeis University, Department of Economics and International Business School, revised Sep 2013.
    6. White, Halbert & Pettenuzzo, Davide, 2014. "Granger causality, exogeneity, cointegration, and economic policy analysis," Journal of Econometrics, Elsevier, vol. 178(P2), pages 316-330.

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