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ON THE ASYMPTOTIC DISTRIBUTION OF IMPULSE RESPONSE FUNCTIONS WITH LONG-RUN RESTRICTIONS

Published online by Cambridge University Press:  01 October 2004

Peter J.G. Vlaar
Affiliation:
De Nederlandsche Bank

Abstract

This paper adopts a two-step technique to estimate structural vector error correction models and provides the asymptotic distribution of the impulse response functions of such a system. The method combines two popular tools in econometrics, namely, vector autoregressive cointegration analysis in the first step and structural vector autoregression analysis in the second. The proposed structural model structure is very general in the sense that all just-identifying or overidentifying schemes that can be expressed as linear restrictions on either the contemporaneous or long-run impact of the structural shocks are allowed for. The long-run restrictions complicate the derivation of the asymptotic distribution of the structural parameter estimates as these restrictions are a function of the reduced form parameters. Consequently, the asymptotic distribution involves an extra partial derivative.Useful comments by Peter Boswijk, Günter Coenen, Neil Ericsson, Sören Johansen, Klaus Neusser, Franz Palm, Paolo Paruolo, Peter van Els, Anders Warne, and ESEM 1998 participants are gratefully acknowledged. The paper also significantly benefited from suggestions by the co-editor Pentti Saikkonen and two anonymous referees.

Type
Research Article
Copyright
© 2004 Cambridge University Press

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References

REFERENCES

Abadir, K.M., K. Hadri, & E. Tzavalis (1999) The influence of VAR dimensions on estimated biases. Econometrica 67, 163181.Google Scholar
Amisano, G. & C. Giannini (1997) Topics in Structural VAR Econometrics. Springer-Verlag.
Baillie, R.T. (1987) Inference in dynamic models containing “surprise” variables. Journal of Econometrics 35, 101117.Google Scholar
Benkwitz, A., H. Lütkepohl, & M.H. Neumann (2000) Problems related to confidence intervals for impulse responses of autoregressive processes. Econometric Reviews 19, 69103.Google Scholar
Bernanke, B.S. (1986) Alternative explanations of the money-income correlation. Carnegie-Rochester Conference Series on Public Policy 25, 49100.Google Scholar
Blanchard, O.J. (1989) A traditional interpretation of economic fluctuations. American Economic Review 79, 11461164.Google Scholar
Blanchard, O.J. & D. Quah (1989) The dynamic effects of aggregate demand and supply disturbances. American Economic Review 79, 655673.Google Scholar
Blanchard, O.J. & M.W. Watson (1986) Are business cycles all alike? In R.J. Gordon (ed.), The American Business Cycle: Continuity and Change, pp. 123182. University of Chicago Press.
Engle, R.F. & C.W.J. Granger (1987) Cointegration and error correction: Representation, estimation and testing. Econometrica 55, 251276.Google Scholar
Faust, J. & E.M. Leeper (1997) When do long-run identifying restrictions give reliable results? Journal of Business and Economic Statistics 15, 345353.Google Scholar
Hubrich, K. & P.J.G. Vlaar (2004) Monetary transmission in Germany: Lessons for the euro area. Empirical Economics 29, 383414.Google Scholar
Johansen, S. (1988) Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control 12, 231254.Google Scholar
Johansen, S. (1991) Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica 59, 15511581.Google Scholar
King, R.G., C.I. Plosser, J.H. Stock, & M.W. Watson (1991) Stochastic trends and economic fluctuations. American Economic Review 81, 819840.Google Scholar
Lütkepohl, H. (1989) A note on the asymptotic distribution of the impulse response functions of estimated VAR models with orthogonal residuals. Journal of Econometrics 42, 371376.Google Scholar
Lütkepohl, H. (1990) Asymptotic distribution of impulse response functions and forecasting error variance decomposition of vector autoregressive models. Review of Economics and Statistics 72, 116125.Google Scholar
Lütkepohl, H. & H.E. Reimers (1992) Impulse response analysis of cointegrated systems with an application to German money demand. Journal of Economic Dynamics and Control 16, 5378.Google Scholar
Magnus, J.R. & H. Neudecker (1986) Symmetry, 0–1 matrices and Jacobians: A review. Econometric Theory 2, 157190.Google Scholar
Mittnik, S. & P. Zadrozny (1993) Asymptotic distributions of impulse responses, step responses, and variance decompositions of estimated linear dynamic models. Econometrica 61, 857870.Google Scholar
Phillips, P.C.B. (1998) Impulse response and forecast error variance asymptotics in nonstationary VARs. Journal of Econometrics 83, 2156.Google Scholar
Serfling, R.F. (1980) Approximation Theorems of Mathematical Statistics. Wiley.
Sims, C.A. (1980) Macroeconomics and reality. Econometrica 48, 148.Google Scholar
Stock, J.H. & M.W. Watson (1988) Testing for common trends. Journal of the American Statistical Association 83, 10971107.Google Scholar
Vlaar, P.J.G. (1998) On the Asymptotic Distribution of Impulse Response Functions with Long Run Restrictions. Staff reports 22, De Nederlandsche Bank, http://www.dnb.nl.
Vlaar, P.J.G. (2004) Shocking the eurozone. European Economic Review 48, 109131.Google Scholar
Warne, A. (1993) A Common Trends Model: Identification, Estimation and Inference. Seminar paper 555, IIES, Stockholm University.