IDEAS home Printed from https://ideas.repec.org/p/rtv/ceisrp/184.html
   My bibliography  Save this paper

An Alternative Solution to the Autoregressivity Paradox in Time Series Analysis

Author

Listed:

Abstract

This note concerns with the marginal models associated with a given vector autoregressive model. In particular, it is shown that a reduction in the orders of the univariate ARMA marginal models can be determined by the presence of variables integrated with different orders. The concepts and methods of the paper are illustrated via an empirical investigation of the low-frequency properties of hours worked in the US.

Suggested Citation

  • Gianluca Cubadda & Umberto Triacca, 2011. "An Alternative Solution to the Autoregressivity Paradox in Time Series Analysis," CEIS Research Paper 184, Tor Vergata University, CEIS, revised 24 Jan 2011.
  • Handle: RePEc:rtv:ceisrp:184
    as

    Download full text from publisher

    File URL: https://ceistorvergata.it/RePEc/rpaper/RP184.pdf
    File Function: Main text
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. King, Robert G. & Plosser, Charles I. & Stock, James H. & Watson, Mark W., 1991. "Stochastic Trends and Economic Fluctuations," American Economic Review, American Economic Association, vol. 81(4), pages 819-840, September.
    2. Cubadda, Gianluca & Hecq, Alain & Palm, Franz C., 2008. "Macro-panels and reality," Economics Letters, Elsevier, vol. 99(3), pages 537-540, June.
    3. Zellner, Arnold & Palm, Franz, 1974. "Time series analysis and simultaneous equation econometric models," Journal of Econometrics, Elsevier, vol. 2(1), pages 17-54, May.
    4. Cubadda, Gianluca & Hecq, Alain & Palm, Franz C., 2009. "Studying co-movements in large multivariate data prior to multivariate modelling," Journal of Econometrics, Elsevier, vol. 148(1), pages 25-35, January.
    5. Palm, Franz, 1977. "On univariate time series methods and simultaneous equation econometric models," Journal of Econometrics, Elsevier, vol. 5(3), pages 379-388, May.
    6. Wallis, Kenneth F, 1977. "Multiple Time Series Analysis and the Final Form of Econometric Models," Econometrica, Econometric Society, vol. 45(6), pages 1481-1497, September.
    7. Rose, Andrew K., 1986. "Four paradoxes in GNP," Economics Letters, Elsevier, vol. 22(2-3), pages 137-141.
    8. Maravall, Agustin & Mathis, Alexandre, 1994. "Encompassing univariate models in multivariate time series : A case study," Journal of Econometrics, Elsevier, vol. 61(2), pages 197-233, April.
    9. Cubadda, G. & Hecq, A.W. & Palm, F.C., 2007. "Studying co-movements in large multivariate models without multivariate modelling," Research Memorandum 032, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    10. Unknown, 1986. "Letters," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 1(4), pages 1-9.
    11. Atella, Vincenzo & Centoni, Marco & Cubadda, Gianluca, 2008. "Technology shocks, structural breaks and the effects on the business cycle," Economics Letters, Elsevier, vol. 100(3), pages 392-395, September.
    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. repec:gam:jecnmx:v:4:y:2016:i:2:p:21:d:67747 is not listed on IDEAS
    2. Nunzio Cappuccio & Diego Lubian, 2016. "Unit Root Tests: The Role of the Univariate Models Implied by Multivariate Time Series," Econometrics, MDPI, vol. 4(2), pages 1-11, April.

    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. Cubadda, Gianluca & Hecq, Alain & Palm, Franz C., 2009. "Studying co-movements in large multivariate data prior to multivariate modelling," Journal of Econometrics, Elsevier, vol. 148(1), pages 25-35, January.
    2. Cubadda, G. & Hecq, A.W. & Palm, F.C., 2007. "Studying co-movements in large multivariate models without multivariate modelling," Research Memorandum 032, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    3. Gianluca Cubadda & Alain Hecq & Antonio Riccardo, 2018. "Forecasting Realized Volatility Measures with Multivariate and Univariate Models: The Case of The US Banking Sector," CEIS Research Paper 445, Tor Vergata University, CEIS, revised 30 Oct 2018.
    4. Marco Centoni & Gianluca Cubadda, 2011. "Modelling comovements of economic time series: a selective survey," Statistica, Department of Statistics, University of Bologna, vol. 71(2), pages 267-294.
    5. Franchi, Massimo & Paruolo, Paolo, 2011. "A characterization of vector autoregressive processes with common cyclical features," Journal of Econometrics, Elsevier, vol. 163(1), pages 105-117, July.
    6. Hecq Alain & Palm Franz C. & Laurent Sébastien, 2016. "On the Univariate Representation of BEKK Models with Common Factors," Journal of Time Series Econometrics, De Gruyter, vol. 8(2), pages 91-113, July.
    7. Nunzio Cappuccio & Diego Lubian, 2016. "Unit Root Tests: The Role of the Univariate Models Implied by Multivariate Time Series," Econometrics, MDPI, vol. 4(2), pages 1-11, April.
    8. Stephan Smeekes & Jean-Pierre Urbain, 2014. "On the Applicability of the Sieve Bootstrap in Time Series Panels," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 139-151, February.
    9. repec:gam:jecnmx:v:4:y:2016:i:2:p:21:d:67747 is not listed on IDEAS
    10. Huh, Hyeon-seung & Kim, David, 2013. "An empirical test of exogenous versus endogenous growth models for the G-7 countries," Economic Modelling, Elsevier, vol. 32(C), pages 262-272.
    11. Kapetanios, G. & Pagan, A. & Scott, A., 2007. "Making a match: Combining theory and evidence in policy-oriented macroeconomic modeling," Journal of Econometrics, Elsevier, vol. 136(2), pages 565-594, February.
    12. Chevillon, Guillaume & Hecq, Alain & Laurent, Sébastien, 2018. "Generating univariate fractional integration within a large VAR(1)," Journal of Econometrics, Elsevier, vol. 204(1), pages 54-65.
    13. Mala Raghavan & George Athanasopoulos & Param Silvapulle, 2009. "VARMA models for Malaysian Monetary Policy Analysis," Monash Econometrics and Business Statistics Working Papers 6/09, Monash University, Department of Econometrics and Business Statistics.
    14. E. Philip Howrey, 1980. "The Role of Time Series Analysis in Econometric Model Evaluation," NBER Chapters, in: Evaluation of Econometric Models, pages 275-307, National Bureau of Economic Research, Inc.
    15. Emre Kahraman & Gazanfer Unal, 2016. "Multiple Wavelet Coherency Analysis and Forecasting of Metal Prices," Papers 1602.01960, arXiv.org.
    16. Lutkepohl, Helmut, 2007. "General-to-specific or specific-to-general modelling? An opinion on current econometric terminology," Journal of Econometrics, Elsevier, vol. 136(1), pages 319-324, January.
    17. David Hendry & Grayham E. Mizon, 2012. "Forecasting from Structural Econometric Models," Economics Series Working Papers 597, University of Oxford, Department of Economics.
    18. Mala Raghavan & George Athanasopoulos & Param Silvapulle, 2016. "Canadian monetary policy analysis using a structural VARMA model," Canadian Journal of Economics, Canadian Economics Association, vol. 49(1), pages 347-373, February.
    19. Lutkepohl, Helmut & Poskitt, D S, 1996. "Specification of Echelon-Form VARMA Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 69-79, January.
    20. Alain Hecq & Luca Margaritella & Stephan Smeekes, 2019. "Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure," Papers 1902.10991, arXiv.org, revised Dec 2020.
    21. Renée Fry & Adrian Pagan, 2011. "Sign Restrictions in Structural Vector Autoregressions: A Critical Review," Journal of Economic Literature, American Economic Association, vol. 49(4), pages 938-960, December.

    More about this item

    Keywords

    VAR Models; ARIMA Models; Final Equations;
    All these keywords.

    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

    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:rtv:ceisrp:184. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/csrotit.html .

    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: Barbara Piazzi (email available below). General contact details of provider: https://edirc.repec.org/data/csrotit.html .

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.