Carrasco Gutierrez, Carlos Enrique and Castro Souza, Reinaldo and Teixeira de Carvalho Guillén, Osmani (2009): Selection of optimal lag length in cointegrated VAR models with weak form of common cyclical features. Published in: Brazilian Review of Econometrics
Preview |
PDF
MPRA_paper_22550.pdf Download (190kB) | Preview |
Abstract
An important aspect of empirical research based on the vector autoregressive (VAR) model is the choice of the lag order, since all inferences in this model depend on the correct model specification. There have been many studies on how to select the lag order of a nonstationary VAR model subject to cointegration restrictions. In this work, we consider an additional weak-form (WF) restriction of common cyclical features in the model to analyze the appropriate way to select the correct lag order. We use two methodologies: the traditional information criteria (AIC, HQ and SC) and an alternative criterion (IC(p,s)) that selects the lag order p and the rank structure s due to the WF restriction. We use a Monte Carlo simulation in the analysis. The results indicate that the cost of ignoring additional WF restrictions in vector autoregressive modeling can be high, especially when the SC criterion is used.
Item Type: | MPRA Paper |
---|---|
Original Title: | Selection of optimal lag length in cointegrated VAR models with weak form of common cyclical features |
Language: | English |
Keywords: | C32, C53 |
Subjects: | C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection |
Item ID: | 22550 |
Depositing User: | Carlos Enrique Carrasco Gutierrez |
Date Deposited: | 09 May 2010 01:35 |
Last Modified: | 26 Sep 2019 18:04 |
References: | Anderson, T. W, (1951) Estimating linear restrictions on regression coefficients for multivariate normal distributions. Ann. Math. Statist. 22 327-351. [Correction (1980) Ann. Statist. 8 1400]. Box GE, Tiao GC. (1977). A canonical analysis of multiple time series. Biometrika, 64:355-365. Braun PA, Mittnik S. (1993). Misspecifications in Vector Autoregressions and Their Effects on Impulse Responses and Variance Decompositions. Journal of Econometrics, 59, 319-41. Caporale GM. (1997). Common Features and Output Fluctuations in the United Kingdom. Economic Modelling, 14: 1-9. Centoni M, Cubadda G, Hecq. (2007) Common Shocks, Common Dynamics, and the International Business Cycle. Economic Modelling, 24, 149-166. Carrasco CG, Gomes F. (2009). Evidence on Common Features and Business Cycle Synchronization in Mercosur. Brazilian Review of Econometrics. Forthcoming. Engle R, Granger C. (1987). Cointegration and error correction: representation, estimation and testing. Econometrica, , 55, 251-76. Engle R, Kozicki S.(1993). Testing for common features. Journal of Business and Economic Statistics, 11: 369-395. Engle R, Issler JV. (1995). Estimating Common Sectoral Cycles. Journal of Monetary Economics, 35:83-113. Engle R, Issler JV.(1993). Common Trends and Common Cycles in Latin America. Revista Brasileira de Economia, vol. 47, nº 2, pp. 149-176, 1993. Gonzalo, J. (1994). Five Alternative Methods of Estimating Long-Run Equilibrium Relationships. Journal of Econometrics, 60 issue 1-2, 203-233. Guillén, Issler, Athanasopoulos. (2005). Forecasting Accuracy and Estimation Uncertainty using VAR Models with Short- and Long-Term Economic Restrictions: A Monte-Carlo Study. Ensaios Econômicos EPGE, 589. Hecq A. (2006). Cointegration and Common Cyclical Features in VAR Models: Comparing Small Sample Performances of the 2-Step and Iterative Approaches. Mimeo. Hecq A, Palm FC, Urbain JP. (2006). Testing for Common Cyclical Features in VAR Models with Cointegration, Journal of Econometrics, 132: 117-141. Hecq, A. (2002), Common Cycles and Common Trends in Latin America. Medium Econometrische Toepassingen, 10, 20-25. Izenman AJ. (1975). Reduced rank regression for the multivariate linear model, Journal of Multivariate Analysis, 5: 248-264. Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica, 59, 1551-1580. Johansen S. (1995). Likelihood-based Inference in Cointegrated Vector Autoregressive Models. Oxford Univ. Press. Z. Kilian L. (2001). Impulse response analysis in vector autoregressions with unknown lag order. Journal of Forecasting, 20: 161-179. Lütkepohl H. (1993). Introduction to Multiple Time Series Analysis. Springer, New York. Mamingi N, Iyare SO. (2003). Convergence and Common Features in International Output: A Case Study of the Economic Community of West African States, 1975-1997. Asian African Journal of Economics and Econometrics, 3: 1-16. Tso MK. (1981). Reduced rank regression and canonical analysis. Journal of the Royal Statistical Society, 43: 183-189. Vahid F, Engle R. (1993). Common trends and common cycles. Journal of Applied Econometrics, 8: 341-360. Vahid F, Issler JV. (2002). The Importance of Common Cyclical Features in VAR Analysis: A Monte Carlo Study. Journal of Econometrics, 109: 341-363. Velu RP, Reinsel GC, Wichern DW. (1986). Reduced rank models for multiple times series. Biometrika, 73: 105-118. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/22550 |