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Time series estimation of the dynamic effects of disaster-type shocks

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  • Davis, Richard
  • Ng, Serena

Abstract

This paper provides three results for SVARs under the assumption that the primitive shocks are mutually independent. First, a framework is proposed to accommodate a disaster-type variable with infinite variance into a SVAR. We show that the least squares estimates of the SVAR are consistent but have non-standard asymptotics. Second, the disaster shock is identified as the component with the largest kurtosis. An estimator that is robust to infinite variance is used to recover the mutually independent components. Third, an independence test on the residuals pre-whitened by the Choleski decomposition is proposed to test the restrictions imposed on a SVAR. The test can be applied whether the data have fat or thin tails, and to over as well as exactly identified models. Three applications are considered. In the first, the independence test is used to shed light on the conflicting evidence regarding the role of uncertainty in economic fluctuations. In the second, disaster shocks are shown to have short term economic impact arising mostly from feedback dynamics. The third uses the framework to study the dynamic effects of economic shocks post-covid.

Suggested Citation

  • Davis, Richard & Ng, Serena, 2023. "Time series estimation of the dynamic effects of disaster-type shocks," Journal of Econometrics, Elsevier, vol. 235(1), pages 180-201.
    • Handle: RePEc:eee:econom:v:235:y:2023:i:1:p:180-201
    DOI: 10.1016/j.jeconom.2022.02.009
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    References listed on IDEAS

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    1. Gouriéroux, Christian & Monfort, Alain & Renne, Jean-Paul, 2017. "Statistical inference for independent component analysis: Application to structural VAR models," Journal of Econometrics, Elsevier, vol. 196(1), pages 111-126.
    2. Dante Amengual & Gabriele Fiorentini & Enrique Sentana, 2022. "Moment tests of independent components," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(1), pages 429-474, May.
    3. Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015. "Measuring Uncertainty," American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
    4. José Luis Montiel Olea & Mikkel Plagborg-Møller & Eric Qian, 2022. "SVAR Identification from Higher Moments: Has the Simultaneous Causality Problem Been Solved?," AEA Papers and Proceedings, American Economic Association, vol. 112, pages 481-485, May.
    5. Kadiyala, Koteswara Rao, 1972. "Regression with Non-Gaussian Stable Disturbances: Some Sampling Results," Econometrica, Econometric Society, vol. 40(4), pages 719-722, July.
    6. Wan, Phyllis & Davis, Richard A., 2022. "Goodness-of-fit testing for time series models via distance covariance," Journal of Econometrics, Elsevier, vol. 227(1), pages 4-24.
    7. W J Wouter Botzen & Olivier Deschenes & Mark Sanders, 2019. "The Economic Impacts of Natural Disasters: A Review of Models and Empirical Studies," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 13(2), pages 167-188.
    8. Alessio Moneta & Doris Entner & Patrik O. Hoyer & Alex Coad, 2013. "Causal Inference by Independent Component Analysis: Theory and Applications," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(5), pages 705-730, October.
    9. Sydney C. Ludvigson & Sai Ma & Serena Ng, 2021. "Uncertainty and Business Cycles: Exogenous Impulse or Endogenous Response?," American Economic Journal: Macroeconomics, American Economic Association, vol. 13(4), pages 369-410, October.
    10. Òscar Jordà, 2005. "Estimation and Inference of Impulse Responses by Local Projections," American Economic Review, American Economic Association, vol. 95(1), pages 161-182, March.
    11. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575, September.
    12. Sydney C. Ludvigson & Sai Ma & Serena Ng, 2021. "COVID-19 and the Costs of Deadly Disasters," AEA Papers and Proceedings, American Economic Association, vol. 111, pages 366-370, May.
    13. Beare, Brendan K & Toda, Alexis Akira, 2020. "On the emergence of a power law in the distribution of COVID-19 cases," University of California at San Diego, Economics Working Paper Series qt9k5027d0, Department of Economics, UC San Diego.
    14. David S. Matteson & Ruey S. Tsay, 2017. "Independent Component Analysis via Distance Covariance," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 623-637, April.
    15. Serena Ng, 2021. "Modeling Macroeconomic Variations after Covid-19," NBER Working Papers 29060, National Bureau of Economic Research, Inc.
    16. repec:taf:jnlbes:v:30:y:2012:i:3:p:381-390 is not listed on IDEAS
    17. Maxand, Simone, 2020. "Identification of independent structural shocks in the presence of multiple Gaussian components," Econometrics and Statistics, Elsevier, vol. 16(C), pages 55-68.
    18. Marc Paolella & Eric Renault & Gennady Samorodnitsky & David Veredas, 2013. "Latest developments in heavy-tailed distributions," ULB Institutional Repository 2013/136284, ULB -- Universite Libre de Bruxelles.
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    More about this item

    Keywords

    Heavy-tails; Independent component analysis; Distance covariance;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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