Dynamic Analysis With Time Series Models: Simulation And Empirical Evidence
The performance of the FPE, AIC, HQ and SC criteria in choosing lag-length, and the effect on the impulse-response functions, are studied in a Monte Carlo simulation. The experiments include stationary, cointegrated, and mixed unit root VAR and MA cases.
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- Lutz Kilian, 1998. "Small-Sample Confidence Intervals For Impulse Response Functions," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 218-230, May.
- Runkle, David E, 1987. "Vector Autoregressions and Reality," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(4), pages 437-442, October.
- Phillips, Peter C. B., 1998.
"Impulse response and forecast error variance asymptotics in nonstationary VARs,"
Journal of Econometrics,
Elsevier, vol. 83(1-2), pages 21-56.
- Peter C.B. Phillips, 1995. "Impulse Response and Forecast Error Variance Asymptotics in Nonstationary VAR's," Cowles Foundation Discussion Papers 1102, Cowles Foundation for Research in Economics, Yale University.
- David E. Runkle, 1987. "Vector autoregressions and reality," Staff Report 107, Federal Reserve Bank of Minneapolis.
- Spencer, David E, 1989. "Does Money Matter? The Robustness of Evidence from Vector Autoregressions," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 21(4), pages 442-454, November.
- Lutkepohl, Helmut & Reimers, Hans-Eggert, 1992. "Impulse response analysis of cointegrated systems," Journal of Economic Dynamics and Control, Elsevier, vol. 16(1), pages 53-78, January.
- Hirotugu Akaike, 1969. "Fitting autoregressive models for prediction," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 21(1), pages 243-247, December. Full references (including those not matched with items on IDEAS)
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