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Business Cycle Analysis and VARMA models

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  • Christian Kascha
  • Karel Mertens

Abstract

An important question in empirical macroeconomics is whether structural vector autoregressions (SVARs) can reliably discriminate between competing DSGE models. Several recent papers have suggested that one reason SVARs may fail to do so is because they are finite-order approximations to infinite-order processes. In this context, we investigate the performance of models that do not suffer from this type of misspecification. We estimate VARMA and state space models using simulated data from a standard economic model and compare true with estimated impulse responses. For our examples, we find that one cannot gain much by using algorithms based on a VARMA representation. However, algorithms that are based on the state space representation do outperform VARs. Unfortunately, these alternative estimates remain heavily biased and very imprecise. The findings of this paper suggest that the reason SVARs perform weakly in these types of simulation studies is not because they are simple finite-order approximations. Given the properties of the generated data, their failure seems almost entirely due to the use of small samples.

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Bibliographic Info

Paper provided by European University Institute in its series Economics Working Papers with number ECO2006/37.

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Date of creation: 2006
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Handle: RePEc:eui:euiwps:eco2006/37

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Keywords: Structural VARs; VARMA; State Space Models; Identification; Business Cycles;

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References

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Citations

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Cited by:
  1. Christian Kascha & Carsten Trenkler, 2011. "Cointegrated VARMA models and forecasting US interest rates," ECON - Working Papers 033, Department of Economics - University of Zurich.
  2. Alfredo García-Hiernaux, 2009. "Forecasting linear dynamical systems using subspace methods," Documentos de Trabajo del ICAE 0902, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
  3. Jiménez-Martín, Juan-Ángel & Cinca, Alfonso Novales, 2010. "State-uncertainty preferences and the risk premium in the exchange rate market," Economic Modelling, Elsevier, vol. 27(5), pages 1043-1053, September.
  4. Jean-Marie Dufour & Tarek Jouini, 2011. "Asymptotic Distributions for Some Quasi-Efficient Estimators in Echelon VARMA Models," CIRANO Working Papers 2011s-25, CIRANO.
  5. D.S. Poskitt & Wenying Yao, 2012. "VAR Modeling and Business Cycle Analysis: A Taxonomy of Errors," Monash Econometrics and Business Statistics Working Papers 11/12, Monash University, Department of Econometrics and Business Statistics.
  6. Christopher Gust & Robert Vigfusson, 2009. "The power of long-run structural VARs," International Finance Discussion Papers 978, Board of Governors of the Federal Reserve System (U.S.).
  7. Mertens, Elmar, 2012. "Are spectral estimators useful for long-run restrictions in SVARs?," Journal of Economic Dynamics and Control, Elsevier, vol. 36(12), pages 1831-1844.
  8. Costantini, Mauro & Gunter, Ulrich & Kunst, Robert M., 2012. "Forecast Combination Based on Multiple Encompassing Tests in a Macroeconomic DSGE-VAR System," Economics Series 292, Institute for Advanced Studies.

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