IDEAS home Printed from https://ideas.repec.org/a/bla/obuest/v83y2021i3p641-662.html
   My bibliography  Save this article

Discovering Specific Common Trends in a Large Set of Disaggregates: Statistical Procedures, their Properties and an Empirical Application

Author

Listed:
  • Guillermo Carlomagno
  • Antoni Espasa

Abstract

Macroeconomic variables are weighted averages of a large number of components. Our objective is to model and forecast all of the N components of a macro variable. The main feature of our proposal consists of discovering subsets of components that share single common trends while neither assuming pervasiveness nor imposing special restrictions on the serial or cross‐sectional idiosyncratic correlation. We adopt a pairwise approach and study its statistical properties. Our asymptotic theory works both with fixed N and T→∞ and with [T,N]→∞. We show that the pairwise approach can be implemented using three alternative strategies, which take into account alternative characteristics of the data generating process. The paper includes an application to the US CPI broken down into 159 components.

Suggested Citation

  • Guillermo Carlomagno & Antoni Espasa, 2021. "Discovering Specific Common Trends in a Large Set of Disaggregates: Statistical Procedures, their Properties and an Empirical Application," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(3), pages 641-662, June.
    • Handle: RePEc:bla:obuest:v:83:y:2021:i:3:p:641-662
    DOI: 10.1111/obes.12412
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/obes.12412
    Download Restriction: no
    File URL: https://libkey.io/10.1111/obes.12412?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Joseph P. Romano & Michael Wolf, 2005. "Stepwise Multiple Testing as Formalized Data Snooping," Econometrica, Econometric Society, vol. 73(4), pages 1237-1282, July.
    2. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    3. Osborn, Denise R, et al, 1988. "Seasonality and the Order of Integration for Consumption," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 50(4), pages 361-377, November.
    4. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1014-1024, November.
    5. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2012. "Model selection when there are multiple breaks," Journal of Econometrics, Elsevier, vol. 169(2), pages 239-246.
    6. Clements, Michael P & Hendry, David F, 1995. "Forecasting in Cointegration Systems," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 127-146, April-Jun.
    7. Saikkonen, Pentti & Lutkepohl, Helmut, 2000. "Testing for the Cointegrating Rank of a VAR Process with Structural Shifts," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(4), pages 451-464, October.
    8. Cavaliere, Giuseppe & De Angelis, Luca & Rahbek, Anders & Robert Taylor, A.M., 2018. "Determining The Cointegration Rank In Heteroskedastic Var Models Of Unknown Order," Econometric Theory, Cambridge University Press, vol. 34(2), pages 349-382, April.
    9. Hendry, David F. & Hubrich, Kirstin, 2011. "Combining Disaggregate Forecasts or Combining Disaggregate Information to Forecast an Aggregate," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(2), pages 216-227.
    10. Saikkonen, Pentti & Lütkepohl, Helmut, 2000. "Testing For The Cointegrating Rank Of A Var Process With An Intercept," Econometric Theory, Cambridge University Press, vol. 16(3), pages 373-406, June.
    11. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    12. Giuseppe Cavaliere & Luca De Angelis & Luca Fanelli, 2018. "Co†integration Rank Determination in Partial Systems Using Information Criteria," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(1), pages 65-89, February.
    13. Carlos Santos & David Hendry & Soren Johansen, 2008. "Automatic selection of indicators in a fully saturated regression," Computational Statistics, Springer, vol. 23(2), pages 317-335, April.
    14. Helmut Lütkepohl & Pentti Saikkonen & Carsten Trenkler, 2004. "Testing for the Cointegrating Rank of a VAR Process with Level Shift at Unknown Time," Econometrica, Econometric Society, vol. 72(2), pages 647-662, March.
    15. Juselius, Katarina, 2006. "The Cointegrated VAR Model: Methodology and Applications," OUP Catalogue, Oxford University Press, number 9780199285679.
    16. Lutkepohl, Helmut & Saikkonen, Pentti, 2000. "Testing for the cointegrating rank of a VAR process with a time trend," Journal of Econometrics, Elsevier, vol. 95(1), pages 177-198, March.
    17. Jennifer L. Castle & David F. Hendry, 2010. "Nowcasting from disaggregates in the face of location shifts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 200-214.
    18. Marianne Sensier & Dick van Dijk, 2004. "Testing for Volatility Changes in U.S. Macroeconomic Time Series," The Review of Economics and Statistics, MIT Press, vol. 86(3), pages 833-839, August.
    19. Espasa, Antoni & Mayo-Burgos, Iván, 2013. "Forecasting aggregates and disaggregates with common features," International Journal of Forecasting, Elsevier, vol. 29(4), pages 718-732.
    20. Saikkonen, Pentti, 1992. "Estimation and Testing of Cointegrated Systems by an Autoregressive Approximation," Econometric Theory, Cambridge University Press, vol. 8(1), pages 1-27, March.
    21. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    22. Jurgen A. Doornik & David F. Hendry & Bent Nielsen, 1998. "Inference in Cointegrating Models: UK M1 Revisited," Journal of Economic Surveys, Wiley Blackwell, vol. 12(5), pages 533-572, December.
    23. Bent Nielsen & Anders Rahbek, 2000. "Similarity Issues in Cointegration Analysis," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 62(1), pages 5-22, February.
    24. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    25. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
    26. Castle Jennifer L. & Doornik Jurgen A & Hendry David F., 2011. "Evaluating Automatic Model Selection," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-33, February.
    27. Poskitt, Don S, 2000. "Strongly Consistent Determination of Cointegrating Rank via Canonical Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(1), pages 77-90, January.
    28. Saikkonen, Pentti & Lütkepohl, HELMUT, 1996. "Infinite-Order Cointegrated Vector Autoregressive Processes," Econometric Theory, Cambridge University Press, vol. 12(5), pages 814-844, December.
    29. Johansen, Søren & Juselius, Katarina, 2014. "An asymptotic invariance property of the common trends under linear transformations of the data," Journal of Econometrics, Elsevier, vol. 178(P2), pages 310-315.
    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. Guillermo Carlomagno & Nicolas Eterovic & L. G. Hernández-Román, 2023. "Disentangling Demand and Supply Inflation Shocks from Chilean Electronic Payment Data," Working Papers Central Bank of Chile 986, Central Bank of Chile.
    2. Carlomagno, Guillermo & Fornero, Jorge & Sansone, Andrés, 2023. "A proposal for constructing and evaluating core inflation measures," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(3).
    3. Guillermo Carlomagno & Jorge Fornero & Andrés Sansone, 2021. "Toward a general framework for constructing and evaluating core inflation measures," Working Papers Central Bank of Chile 913, Central Bank of Chile.

    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. Carlomagno, Guillermo, 2015. "Forecasting a large set of disaggregates with common trends and outliers," DES - Working Papers. Statistics and Econometrics. WS ws1518, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Carlomagno, Guillermo, 2014. "The pairwise approach to model a large set of disaggregates with common trends," DES - Working Papers. Statistics and Econometrics. WS ws141309, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Carlomagno, Guillermo, 2016. "Discovering common trends in a large set of disaggregates: statistical procedures and their properties," DES - Working Papers. Statistics and Econometrics. WS ws1519, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Neil R. Ericsson, 2021. "Dynamic Econometrics in Action: A Biography of David F. Hendry," International Finance Discussion Papers 1311, Board of Governors of the Federal Reserve System (U.S.).
    5. Johansen, Søren & Nielsen, Morten Ørregaard, 2018. "The cointegrated vector autoregressive model with general deterministic terms," Journal of Econometrics, Elsevier, vol. 202(2), pages 214-229.
    6. Kirstin Hubrich & Helmut Lutkepohl & Pentti Saikkonen, 2001. "A Review Of Systems Cointegration Tests," Econometric Reviews, Taylor & Francis Journals, vol. 20(3), pages 247-318.
    7. Matei Demetrescu & Helmut Lütkepohl & Pentti Saikkonen, 2009. "Testing for the cointegrating rank of a vector autoregressive process with uncertain deterministic trend term," Econometrics Journal, Royal Economic Society, vol. 12(3), pages 414-435, November.
    8. Walter Krämer, 2019. "Interview mit Helmut Lütkepohl," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 13(1), pages 87-94, April.
    9. Zhaoxing Gao & Ruey S. Tsay, 2020. "Modeling High-Dimensional Unit-Root Time Series," Papers 2005.03496, arXiv.org, revised Aug 2020.
    10. Takamitsu Kurita & B. Nielsen, 2018. "Partial cointegrated vector autoregressive models with structural breaks in deterministic terms," Economics Papers 2018-W03, Economics Group, Nuffield College, University of Oxford.
    11. Skrobotov, Anton, 2021. "Structural breaks in cointegration models: Multivariate case," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 64, pages 83-106.
    12. Dimitrios Sideris, 2009. "Do the new EU member states form an Optimum Currency Area with the eurozone? Evidence from six Central and Eastern European Countries," SEEMHN papers 15, National Bank of Serbia.
    13. Hallin, M. & van den Akker, R. & Werker, B.J.M., 2012. "Rank-based Tests of the Cointegrating Rank in Semiparametric Error Correction Models," Other publications TiSEM bc68a2f2-3ca3-443c-b3ac-f, Tilburg University, School of Economics and Management.
    14. Dimitrios Sideris, 2011. "Optimum currency areas, structural changes and the endogeneity of the OCA criteria: evidence from six new EU member states," Applied Financial Economics, Taylor & Francis Journals, vol. 21(4), pages 195-206.
    15. repec:cte:wsrepe:25392 is not listed on IDEAS
    16. Takamitsu Kurita & Bent Nielsen, 2019. "Partial Cointegrated Vector Autoregressive Models with Structural Breaks in Deterministic Terms," Econometrics, MDPI, vol. 7(4), pages 1-35, October.
    17. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2013. "Model Selection in Equations with Many ‘Small’ Effects," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(1), pages 6-22, February.
    18. John D. Levendis, 2018. "Time Series Econometrics," Springer Texts in Business and Economics, Springer, number 978-3-319-98282-3, April.
    19. Beckmann, Joscha & Czudaj, Robert, 2013. "Gold as an inflation hedge in a time-varying coefficient framework," The North American Journal of Economics and Finance, Elsevier, vol. 24(C), pages 208-222.
    20. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry & Ragnar Nymoen, 2014. "Misspecification Testing: Non-Invariance of Expectations Models of Inflation," Econometric Reviews, Taylor & Francis Journals, vol. 33(5-6), pages 553-574, August.
    21. repec:nsb:seemhn:17 is not listed on IDEAS
    22. Harris, David & Leybourne, Stephen J. & Taylor, A.M. Robert, 2016. "Tests of the co-integration rank in VAR models in the presence of a possible break in trend at an unknown point," Journal of Econometrics, Elsevier, vol. 192(2), pages 451-467.

    More about this item

    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:bla:obuest:v:83:y:2021:i:3:p:641-662. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/sfeixuk.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.