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Estimation and Inference by the Method of Projection Minimum Distance

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  • 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 semiparametrically estimated by local projections (Jordà, 2005). Substituting the Wold representations for variables in model expressions generates restrictions that can be used by the method of minimum distance to estimate model parameters. We call this estimator projection minimum distance (PMD) and show that its parameter estimates are consistent and asymptotically normal. In many cases, PMD is asymptotically equivalent to maximum likelihood estimation (MLE) and nests GMM as a special case. In fact, models whose ML estimation would require numerical routines (such as VARMA models) can often be estimated by simple least-squares routines and almost as efficiently by PMD. Because PMD imposes no constraints on the dynamics of the system, it is often consistent in many situations where alternative estimators would be inconsistent. We provide several Monte Carlo experiments and an empirical application in support of the new techniques introduced.

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Bibliographic Info

Paper provided by University of California, Davis, Department of Economics in its series Working Papers with number 78.

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Length: 68
Date of creation: 20 Jul 2007
Date of revision:
Handle: RePEc:cda:wpaper:07-8

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Keywords: impulse response; local projection; minimum chi-square; minimum distance;

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References

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  1. Alastair Hall & Atsushi & James M Nason & Barbara Rossi, 2009. "Information Criteria For Impulse Response Function Matching Estimation Of Dsge Models," Working Papers 09-09, Duke University, Department of Economics.
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  17. Jim Nason & Barbara Rossi & Atsushi Inoue & Alastair Hall, 2007. "Information Criteria for Impulse Response Function Matching Estimation," 2007 Meeting Papers 293, Society for Economic Dynamics.
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Citations

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Cited by:
  1. Hall, Alastair & Inoue, Atsushi & Nason M, James & Rossi, Barbara, 2007. "Information Criteria for Impulse Response Function Matching Estimation of DSGE Models," Working Papers 07-04, Duke University, Department of Economics.
  2. Poghosyan, K. & Boldea, O., 2011. "Structural versus Matching Estimation: Transmission Mechanisms in Armenia," Discussion Paper 2011-104, Tilburg University, Center for Economic Research.
  3. Poghosyan, K., 2012. "Structural and reduced-form modeling and forecasting with application to Armenia," Open Access publications from Tilburg University urn:nbn:nl:ui:12-5590845, Tilburg University.
  4. �scar Jord� & Massimiliano Marcellino, 2010. "Path forecast evaluation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 635-662.
  5. 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.
  6. Theodoridis, Konstantinos, 2011. "An efficient minimum distance estimator for DSGE models," Bank of England working papers 439, Bank of England.

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