IDEAS home Printed from https://ideas.repec.org/p/cte/werepe/27484.html
   My bibliography  Save this paper

Seasonal Quasi-Vector Autoregressive Models with an Application to Crude Oil Production and Economic Activity in the United States and Canada

Author

Listed:
  • Escribano, Álvaro
  • Licht, Adrian
  • Blazsek, Szabolcs

Abstract

We introduce the Seasonal-QVAR (quasi-vector autoregressive) model that we apply to study the relationship between oil production and economic activity. Seasonal-QVAR is a score-driven nonlinear model for the multivariate t distribution. It is an alternative to the basic structural model that disentangles local level and stochastic seasonality. Seasonal-QVAR is robust to extreme observations and it is an extension of Seasonal-VARMA (VAR moving average). We use monthly data from world crude oil production growth, global real economic activity growth and the industrial production growths of the United States and Canada. We address an important economic question about the influence of world crude oil production on the industrial productions of the United States and Canada. We find that the effects of industrial production growth of the United States on world crude oil production growth are about six times higher for the basic structural model and Seasonal-VARMA than for Seasonal-QVAR. We also find that the effects of world crude oil production growth on the industrial production growth of Canada are positive for Seasonal-QVAR, but those effects are negative for Seasonal-VARMA. Likelihood-based performance metrics and transitivity arguments support the estimates of Seasonal- QVAR, as opposed to the basic structural model and Seasonal-VARMA.

Suggested Citation

  • Escribano, Álvaro & Licht, Adrian & Blazsek, Szabolcs, 2018. "Seasonal Quasi-Vector Autoregressive Models with an Application to Crude Oil Production and Economic Activity in the United States and Canada," UC3M Working papers. Economics 27484, Universidad Carlos III de Madrid. Departamento de Economía.
    • Handle: RePEc:cte:werepe:27484
    as

    Download full text from publisher

    File URL: https://e-archivo.uc3m.es/rest/api/core/bitstreams/34ffc2e1-f157-40bd-b7c4-59c33c14b985/content
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jeffrey M. Wooldridge, 2005. "Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(1), pages 39-54, January.
    2. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024.
    3. Escanciano, J. Carlos & Lobato, Ignacio N., 2009. "An automatic Portmanteau test for serial correlation," Journal of Econometrics, Elsevier, vol. 151(2), pages 140-149, August.
    4. Andrew Harvey & Alessandra Luati, 2014. "Filtering With Heavy Tails," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1112-1122, September.
    5. Blazsek, Szabolcs & Escribano, Alvaro, 2016. "Score-driven dynamic patent count panel data models," Economics Letters, Elsevier, vol. 149(C), pages 116-119.
    6. Ayala, Astrid & Escribano, Álvaro & Blazsek, Szabolcs, 2017. "Dynamic conditional score models with time-varying location, scale and shape parameters," UC3M Working papers. Economics 25043, Universidad Carlos III de Madrid. Departamento de Economía.
    7. Gert Peersman & Ine van Robays, 2009. "Oil and the Euro area economy [Labour market implications of EU product market integration]," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 24(60), pages 603-651.
    8. Hamilton, James D., 2003. "What is an oil shock?," Journal of Econometrics, Elsevier, vol. 113(2), pages 363-398, April.
    9. Lutz Kilian, 2008. "A Comparison of the Effects of Exogenous Oil Supply Shocks on Output and Inflation in the G7 Countries," Journal of the European Economic Association, MIT Press, vol. 6(1), pages 78-121, March.
    10. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    11. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    12. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Theory," Econometrica, Econometric Society, vol. 52(3), pages 681-700, May.
    13. Christiane Baumeister & Gert Peersman, 2013. "Time-Varying Effects of Oil Supply Shocks on the US Economy," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(4), pages 1-28, October.
    14. Szabolcs Blazsek & Hector Hernández, 2018. "Analysis of electricity prices for Central American countries using dynamic conditional score models," Empirical Economics, Springer, vol. 55(4), pages 1807-1848, December.
    15. Hindrayanto, Irma & Jacobs, Jan P.A.M. & Osborn, Denise R. & Tian, Jing, 2019. "Trend–Cycle–Seasonal Interactions: Identification And Estimation," Macroeconomic Dynamics, Cambridge University Press, vol. 23(8), pages 3163-3188, December.
    16. Marianne Baxter & Robert G. King, 1999. "Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 575-593, November.
    17. Christiane Baumeister & James D. Hamilton, 2019. "Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks," American Economic Review, American Economic Association, vol. 109(5), pages 1873-1910, May.
    18. Szabolcs Blazsek & Luis Antonio Monteros, 2017. "Event-study analysis by using dynamic conditional score models," Applied Economics, Taylor & Francis Journals, vol. 49(45), pages 4530-4541, September.
    19. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    20. James H. Stock & Mark W. Watson, 2001. "Vector Autoregressions," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 101-115, Fall.
    21. Drew Creal & Siem Jan Koopman & André Lucas, 2013. "Generalized Autoregressive Score Models With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 777-795, August.
    22. Olivier J. Blanchard & Mark W. Watson, 1986. "Are Business Cycles All Alike?," NBER Chapters, in: The American Business Cycle: Continuity and Change, pages 123-180, National Bureau of Economic Research, Inc.
    23. Christopher A. Sims, 1982. "Policy Analysis with Econometric Models," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 13(1), pages 107-164.
    24. Blundell, Richard & Griffith, Rachel & Windmeijer, Frank, 2002. "Individual effects and dynamics in count data models," Journal of Econometrics, Elsevier, vol. 108(1), pages 113-131, May.
    25. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    26. Christopher A. Sims, 1986. "Are forecasting models usable for policy analysis?," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 10(Win), pages 2-16.
    27. Baumeister, Christiane & Hamilton, James, 2017. "Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Deman," CEPR Discussion Papers 12532, C.E.P.R. Discussion Papers.
    28. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    29. William D. Nordhaus, 1980. "Oil and Economic Performance in industrial Countries," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 11(2), pages 341-400.
    30. Francisco Blasques & Paolo Gorgi & Siem Jan Koopman & Olivier Wintenberger, 2016. "Feasible Invertibility Conditions and Maximum Likelihood Estimation for Observation-Driven Models," Tinbergen Institute Discussion Papers 16-082/III, Tinbergen Institute.
    31. Ye, Michael & Zyren, John & Shore, Joanne, 2006. "Forecasting short-run crude oil price using high- and low-inventory variables," Energy Policy, Elsevier, vol. 34(17), pages 2736-2743, November.
    32. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Applications to Poisson Models," Econometrica, Econometric Society, vol. 52(3), pages 701-720, May.
    33. Michele Caivano & Andrew Harvey & Alessandra Luati, 2016. "Robust time series models with trend and seasonal components," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(1), pages 99-120, March.
    34. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575.
    35. Escribano, Álvaro & Licht, Adrian & Blazsek, Szabolcs, 2017. "Score-driven non-linear multivariate dynamic location models," UC3M Working papers. Economics 25739, Universidad Carlos III de Madrid. Departamento de Economía.
    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. Blazsek, Szabolcs & Escribano, Álvaro & Licht, Adrian, 2019. "Co-integration and common trends analysis with score-driven models : an application to the federal funds effective rate and US inflation rate," UC3M Working papers. Economics 28451, Universidad Carlos III de Madrid. Departamento de Economía.

    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. Escribano, Álvaro & Licht, Adrian & Blazsek, Szabolcs, 2018. "Seasonal quasi-vector autoregressive models for macroeconomic data," UC3M Working papers. Economics 26316, Universidad Carlos III de Madrid. Departamento de Economía.
    2. Blazsek, Szabolcs & Escribano, Álvaro & Licht, Adrian, 2019. "Markov-switching score-driven multivariate models: outlier-robust measurement of the relationships between world crude oil production and US industrial production," UC3M Working papers. Economics 29030, Universidad Carlos III de Madrid. Departamento de Economía.
    3. Blazsek, Szabolcs & Escribano, Álvaro & Licht, Adrian, 2018. "Seasonality Detection in Small Samples using Score-Driven Nonlinear Multivariate Dynamic Location Models," UC3M Working papers. Economics 27483, Universidad Carlos III de Madrid. Departamento de Economía.
    4. Astrid Ayala & Szabolcs Blazsek, 2019. "Score-driven currency exchange rate seasonality as applied to the Guatemalan Quetzal/US Dollar," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 10(1), pages 65-92, March.
    5. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    6. Blazsek Szabolcs & Licht Adrian & Escribano Alvaro, 2021. "Identification of Seasonal Effects in Impulse Responses Using Score-Driven Multivariate Location Models," Journal of Econometric Methods, De Gruyter, vol. 10(1), pages 53-66, January.
    7. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    8. Blazsek, Szabolcs & Ayala, Astrid & Escribano, Álvaro, 2019. "Score-driven time series models with dynamic shape : an application to the Standard & Poor's 500 index," UC3M Working papers. Economics 28133, Universidad Carlos III de Madrid. Departamento de Economía.
    9. Blasques, Francisco & van Brummelen, Janneke & Koopman, Siem Jan & Lucas, André, 2022. "Maximum likelihood estimation for score-driven models," Journal of Econometrics, Elsevier, vol. 227(2), pages 325-346.
    10. Caldara, Dario & Cavallo, Michele & Iacoviello, Matteo, 2019. "Oil price elasticities and oil price fluctuations," Journal of Monetary Economics, Elsevier, vol. 103(C), pages 1-20.
    11. Christian Francq & Genaro Sucarrat, 2018. "An Exponential Chi-Squared QMLE for Log-GARCH Models Via the ARMA Representation," Journal of Financial Econometrics, Oxford University Press, vol. 16(1), pages 129-154.
    12. Gong, Xu & Chen, Liqiang & Lin, Boqiang, 2020. "Analyzing dynamic impacts of different oil shocks on oil price," Energy, Elsevier, vol. 198(C).
    13. F Blasques & P Gorgi & S J Koopman & O Wintenberger, 2016. "Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models ," Working Papers hal-01377971, HAL.
    14. Aastveit, Knut Are, 2014. "Oil price shocks in a data-rich environment," Energy Economics, Elsevier, vol. 45(C), pages 268-279.
    15. F Blasques & P Gorgi & S Koopman & O Wintenberger, 2016. "Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models," Papers 1610.02863, arXiv.org.
    16. Francisco Blasques & Paolo Gorgi & Siem Jan Koopman & Olivier Wintenberger, 2016. "Feasible Invertibility Conditions and Maximum Likelihood Estimation for Observation-Driven Models," Tinbergen Institute Discussion Papers 16-082/III, Tinbergen Institute.
    17. Astrid Ayala & Szabolcs Blazsek, 2018. "Equity market neutral hedge funds and the stock market: an application of score-driven copula models," Applied Economics, Taylor & Francis Journals, vol. 50(37), pages 4005-4023, August.
    18. Rodrigo da Silva Souza & Leonardo Bornacki Mattos, 2023. "Macroeconomic effects of oil price shocks on an emerging market economy," Economic Change and Restructuring, Springer, vol. 56(2), pages 803-824, April.
    19. Blasques, F. & Gorgi, P. & Koopman, S.J., 2021. "Missing observations in observation-driven time series models," Journal of Econometrics, Elsevier, vol. 221(2), pages 542-568.
    20. Liu, Li & Wang, Yudong & Yang, Li, 2018. "Predictability of crude oil prices: An investor perspective," Energy Economics, Elsevier, vol. 75(C), pages 193-205.

    More about this item

    Keywords

    Dynamic conditional score (DCS);

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:cte:werepe:27484. 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: Ana Poveda (email available below). General contact details of provider: http://www.eco.uc3m.es/ .

    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.