IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/22550.html
   My bibliography  Save this paper

Selection of optimal lag length in cointegrated VAR models with weak form of common cyclical features

Author

Listed:
  • Carrasco Gutierrez, Carlos Enrique
  • Castro Souza, Reinaldo
  • Teixeira de Carvalho Guillén, Osmani

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.

Suggested Citation

  • Carrasco Gutierrez, Carlos Enrique & Castro Souza, Reinaldo & Teixeira de Carvalho Guillén, Osmani, 2009. "Selection of optimal lag length in cointegrated VAR models with weak form of common cyclical features," MPRA Paper 22550, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:22550
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/22550/1/MPRA_paper_22550.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Vahid, F & Engle, Robert F, 1993. "Common Trends and Common Cycles," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(4), pages 341-360, Oct.-Dec..
    2. Centoni, Marco & Cubadda, Gianluca & Hecq, Alain, 2007. "Common shocks, common dynamics, and the international business cycle," Economic Modelling, Elsevier, vol. 24(1), pages 149-166, January.
    3. Hecq, Alain & Palm, Franz C. & Urbain, Jean-Pierre, 2006. "Common cyclical features analysis in VAR models with cointegration," Journal of Econometrics, Elsevier, vol. 132(1), pages 117-141, May.
    4. Gianluca Cubadda, 1999. "Common serial correlation and common business cycles: A cautious note," Empirical Economics, Springer, vol. 24(3), pages 529-535.
    5. Gutierrez, Carlos Enrique Carrasco & Gomes, Fábio Augusto Reis, 2009. "Evidence on Common Features and Business Cycle Synchronization in Mercosur," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 29(1), May.
    6. Athanasopoulos, George & Issler, João Victor & Guillen, Osmani Teixeira Carvalho, 2005. "Forecasting accuracy and estimation uncertainty using VAR models with short- and long-term economic restrictions: a Monte-Carlo study," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 589, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    7. repec:fgv:epgrbe:v:47:n:2:a:1 is not listed on IDEAS
    8. Engle, Robert F & Kozicki, Sharon, 1993. "Testing for Common Features," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(4), pages 369-380, October.
    9. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    10. Engle, Robert F & Kozicki, Sharon, 1993. "Testing for Common Features: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(4), pages 393-395, October.
    11. Vahid, Farshid & Issler, Joao Victor, 2002. "The importance of common cyclical features in VAR analysis: a Monte-Carlo study," Journal of Econometrics, Elsevier, vol. 109(2), pages 341-363, August.
    12. Ronald Bewley & Minxian Yang, 1998. "On The Size And Power Of System Tests For Cointegration," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 675-679, November.
    13. Braun, Phillip A. & Mittnik, Stefan, 1993. "Misspecifications in vector autoregressions and their effects on impulse responses and variance decompositions," Journal of Econometrics, Elsevier, vol. 59(3), pages 319-341, October.
    14. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    15. Engle, Robert F. & Issler, João Victor, 1993. "Common trends and common cycles in Latin America," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 47(2), April.
    16. Alain Hecq & Franz Palm & Jean-Pierre Urbain, 2001. "Testing for Common Cyclical Features in Var Models with Cointegration," CESifo Working Paper Series 451, CESifo.
    17. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-1580, November.
    18. Izenman, Alan Julian, 1975. "Reduced-rank regression for the multivariate linear model," Journal of Multivariate Analysis, Elsevier, vol. 5(2), pages 248-264, June.
    19. Gonzalo, Jesus, 1994. "Five alternative methods of estimating long-run equilibrium relationships," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 203-233.
    20. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    21. Caporale, Guglielmo Maria, 1997. "Common features and output fluctuations in the United Kingdom," Economic Modelling, Elsevier, vol. 14(1), pages 1-9, January.
    22. Kilian, Lutz, 2001. "Impulse Response Analysis in Vector Autoregressions with Unknown Lag Order," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(3), pages 161-179, April.
    23. Engle, Robert F. & Issler, Joao Victor, 1995. "Estimating common sectoral cycles," Journal of Monetary Economics, Elsevier, vol. 35(1), pages 83-113, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mohammad Naim Azimi & Mohammad Musa Shafiq, 2020. "Hypothesizing directional causality between the governance indicators and economic growth: the case of Afghanistan," Future Business Journal, Springer, vol. 6(1), pages 1-14, December.
    2. Luo, Yulong & Zeng, Weiliang & Wang, Yueqiang & Li, Danzhou & Hu, Xianbiao & Zhang, Hua, 2021. "A hybrid approach for examining the drivers of energy consumption in Shanghai," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    3. Roy, Gobinda & Sharma, Swati, 2021. "Measuring the role of factors on website effectiveness using vector autoregressive model," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    4. Mansour-Ichrakieh, Layal, 2020. "The impact of Israeli Geopolitical Risks on the Lebanese Financial Market: A Destabilizer Multiplier," MPRA Paper 99376, University Library of Munich, Germany.
    5. Rui Yang & Xin An & Yingwen Chen & Xiuli Yang, 2023. "The Knowledge Analysis of Panel Vector Autoregression: A Systematic Review," SAGE Open, , vol. 13(4), pages 21582440231, December.
    6. Yulong Luo & Can Wang & Chen Chen & Kangle Ding & Weiliang Zeng, 2021. "Total Investment in Fixed Assets and the Later Stage of Urbanization: A Case Study of Shanghai," Sustainability, MDPI, vol. 13(7), pages 1-29, March.
    7. Ghouse, Ghulam & Khan, Saud Ahmed & Rehman, Atiq Ur, 2018. "ARDL model as a remedy for spurious regression: problems, performance and prospectus," MPRA Paper 83973, University Library of Munich, Germany.
    8. Rambeli, Norimah & Awang Marikan, Dayang Affizah & Hashim, Emilda & Mohd. Ariffin, Siti Zubaidah & Hashim, Asmawi & M. Podivinsky, Jan, 2021. "The Determinants of Carbon Dioxide Emissions in Malaysia and Singapore," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 55(2), pages 107-119.
    9. Mohammad Naim Azimi & Mohammad Musa Shafiq, 2022. "The J-curve phenomenon in Afghanistan and its major trading partners: evidence from a non-linear ARDL approach," SN Business & Economics, Springer, vol. 2(7), pages 1-28, July.
    10. Dimitrios DAPONTAS, 2019. "Relationship between mortality and financial crisis. The case of Greece," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(3(620), A), pages 171-178, Autumn.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gutierrez, Carlos Enrique Carrasco & Souza, Reinaldo Castro & Guillén, Osmani Teixeira de Carvalho, 2009. "Selection of Optimal Lag Length in Cointegrated VAR Models with Weak Form of Common Cyclical Features," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 29(1), May.
    2. Guillén, Osmani Teixeira & Hecq, Alain & Issler, João Victor & Saraiva, Diogo, 2015. "Forecasting multivariate time series under present-value model short- and long-run co-movement restrictions," International Journal of Forecasting, Elsevier, vol. 31(3), pages 862-875.
    3. Hecq, A.W. & Issler, J.V., 2012. "A common-feature approach for testing present-value restrictions with financial data," Research Memorandum 006, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    4. Athanasopoulos, George & Issler, João Victor & Guillen, Osmani Teixeira Carvalho, 2005. "Forecasting accuracy and estimation uncertainty using VAR models with short- and long-term economic restrictions: a Monte-Carlo study," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 589, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    5. Issler, João Victor & Rodrigues, Claudia & Burjack, Rafael, 2014. "Using common features to understand the behavior of metal-commodity prices and forecast them at different horizons," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 310-335.
    6. Mont'Alverne Duarte, Angelo & Gaglianone, Wagner Piazza & de Carvalho Guillén, Osmani Teixeira & Issler, João Victor, 2021. "Commodity prices and global economic activity: A derived-demand approach," Energy Economics, Elsevier, vol. 96(C).
    7. Elizabeth Wakerly & Byron Scott & James Nason, 2006. "Common trends and common cycles in Canada: who knew so much has been going on?," Canadian Journal of Economics, Canadian Economics Association, vol. 39(1), pages 320-347, February.
    8. Carlos Enrique Carrasco Gutierrez & Fábio Augusto Reis Gomes, 2006. "Evidence About Mercosur’S Business Cycle," Anais do XXXIV Encontro Nacional de Economia [Proceedings of the 34th Brazilian Economics Meeting] 179, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    9. Neri, Marcelo Côrtes, 2014. "Brazil's middle classes," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 759, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    10. Robert Dixon & David Shepherd, 2001. "Trends and Cycles in Australian State and Territory Unemployment Rates," The Economic Record, The Economic Society of Australia, vol. 77(238), pages 252-269, September.
    11. Norman J. Morin, 2006. "Likelihood ratio tests on cointegrating vectors, disequilibrium adjustment vectors, and their orthogonal complements," Finance and Economics Discussion Series 2006-21, Board of Governors of the Federal Reserve System (U.S.).
    12. Heather M Anderson & Farshid Vahid, 2010. "VARs, Cointegration and Common Cycle Restrictions," Monash Econometrics and Business Statistics Working Papers 14/10, Monash University, Department of Econometrics and Business Statistics.
    13. repec:fgv:epgewp:736 is not listed on IDEAS
    14. Balcilar, Mehmet & Gupta, Rangan & Wohar, Mark E., 2017. "Common cycles and common trends in the stock and oil markets: Evidence from more than 150years of data," Energy Economics, Elsevier, vol. 61(C), pages 72-86.
    15. Bicu, A.C. & Candelon, B., 2012. "Government bond market dynamics and sovereign risk: systemic or idiosyncratic?," Research Memorandum 032, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    16. Li, Xiao-Lin & Chang, Tsangyao & Miller, Stephen M. & Balcilar, Mehmet & Gupta, Rangan, 2015. "The co-movement and causality between the U.S. housing and stock markets in the time and frequency domains," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 220-233.
    17. Osmani Teixeira de Carvalho de Guillén & Carlos Hamilton Vasconcelos Araújo, 2005. "O Mecanismo De Transmissão Da Taxa De Câmbio Para Índices De Preços: Uma Análise Vecm Para O Brasil," Anais do XXXIII Encontro Nacional de Economia [Proceedings of the 33rd Brazilian Economics Meeting] 034, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    18. Dilip M. Nachane & Amlendu Dubey, 2021. "The Spectral Envelope: An Application to the Decoupling Problem in Economics," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 287-308, December.
    19. Willie Lahari, 2011. "Assessing Business Cycle Synchronisation - Prospects for a Pacific Islands Currency Union," Working Papers 1110, University of Otago, Department of Economics, revised Oct 2011.
    20. Corradi, Valentina & Swanson, Norman R., 2006. "The effect of data transformation on common cycle, cointegration, and unit root tests: Monte Carlo results and a simple test," Journal of Econometrics, Elsevier, vol. 132(1), pages 195-229, May.
    21. Paresh Kumar Narayan & Seema Narayan, 2008. "The role of permanent and transitory shocks in explaining international health expenditures," Health Economics, John Wiley & Sons, Ltd., vol. 17(10), pages 1171-1186, October.

    More about this item

    Keywords

    C32; C53;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:22550. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.