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Estimation and Inference by the Method of Projection Minimum Distance

Author

Listed:
  • Oscar Jorda
  • Sharon Kozicki

    (Department of Economics, University of California Davis)

Abstract
A covariance-stationary vector of variables has a Wold representation whose coefficients can be semiparametricallyestimated by local projections (Jordà, 2005). Substituting the Wold representationsfor variables in model expressions generates restrictions that can be used by the method of minimumdistance to estimate model parameters. We call this estimator projection minimum distance(PMD) and show that its parameter estimates are consistent and asymptotically normal. In manycases, PMD is asymptotically equivalent to maximum likelihood estimation (MLE) and nests GMMas a special case. In fact, models whose ML estimation would require numerical routines (such asVARMA models) can often be estimated by simple least-squares routines and almost as efficiently byPMD. Because PMD imposes no constraints on the dynamics of the system, it is often consistent inmany situations where alternative estimators would be inconsistent. We provide several Monte Carloexperiments and an empirical application in support of the new techniques introduced.

Suggested Citation

  • Oscar Jorda & Sharon Kozicki, 2007. "Estimation and Inference by the Method of Projection Minimum Distance," Working Papers 148, University of California, Davis, Department of Economics.
    • Handle: RePEc:cda:wpaper:148
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    Cited by:

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    2. Hall, Alastair R. & Inoue, Atsushi & Nason, James M. & Rossi, Barbara, 2012. "Information criteria for impulse response function matching estimation of DSGE models," Journal of Econometrics, Elsevier, vol. 170(2), pages 499-518.
    3. Theodoridis, Konstantinos, 2011. "An efficient minimum distance estimator for DSGE models," Bank of England working papers 439, Bank of England.
    4. Òscar Jordà & Massimiliano Marcellino, 2010. "Path forecast evaluation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 635-662.
    5. Poghosyan, Karen & Boldea, Otilia, 2013. "Structural versus matching estimation: Transmission mechanisms in Armenia," Economic Modelling, Elsevier, vol. 30(C), pages 136-148.
    6. Yu, Qionglei & McManus, Richard & Yen, Dorothy A. & Li, Xiang (Robert), 2020. "Tourism boycotts and animosity: A study of seven events," Annals of Tourism Research, Elsevier, vol. 80(C).
    7. Kilian, Lutz & Kim, Yun Jung, 2009. "Do Local Projections Solve the Bias Problem in Impulse Response Inference?," CEPR Discussion Papers 7266, C.E.P.R. Discussion Papers.
    8. Poghosyan, K., 2012. "Structural and reduced-form modeling and forecasting with application to Armenia," Other publications TiSEM ad1a24c3-15e6-4f04-b338-3, Tilburg University, School of Economics and Management.
    9. Francisco RUGE-MURCIA, 2014. "Indirect Inference Estimation of Nonlinear Dynamic General Equilibrium Models : With an Application to Asset Pricing under Skewness Risk," Cahiers de recherche 15-2014, Centre interuniversitaire de recherche en économie quantitative, CIREQ.

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    More about this item

    Keywords

    impulse response; local projection; minimum chi-square; minimum distance;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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