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Aggregation in Large Dynamic Panels

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  • Pesaran, M. Hashem

    ()
    (University of Cambridge)

  • Chudik, Alexander

    ()
    (University of Cambridge)

Abstract

This paper considers the problem of aggregation in the case of large linear dynamic panels, where each micro unit is potentially related to all other micro units, and where micro innovations are allowed to be cross sectionally dependent. Following Pesaran (2003), an optimal aggregate function is derived, and the limiting behavior of the aggregation error is investigated as N (the number of cross section units) increases. Certain distributional features of micro parameters are also identified from the aggregate function. The paper then establishes Granger's (1980) conjecture regarding the long memory properties of aggregate variables from 'a very large scale dynamic, econometric model', and considers the time profiles of the effects of macro and micro shocks on the aggregate and disaggregate variables. Some of these findings are illustrated in Monte Carlo experiments, where we also study the estimation of the aggregate effects of micro and macro shocks. The paper concludes with an empirical application to consumer price inflation in Germany, France and Italy, and re-examines the extent to which ‘observed’ inflation persistence at the aggregate level is due to aggregation and/or common unobserved factors. Our findings suggest that dynamic heterogeneity as well as persistent common factors are needed for explaining the observed persistence of the aggregate inflation.

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Bibliographic Info

Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 5478.

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Length: 56 pages
Date of creation: Feb 2011
Date of revision:
Publication status: forthcoming in: Journal of Econometrics
Handle: RePEc:iza:izadps:dp5478

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Keywords: weak and strong cross section dependence; long memory; large dynamic panels; aggregation; VAR models; impulse responses; factor models; inflation persistence;

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References

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  1. Yan Shen & Cheng Hsiao & Hiroshi Fujiki, 2005. "Aggregate vs. disaggregate data analysis-a paradox in the estimation of a money demand function of Japan under the low interest rate policy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(5), pages 579-601.
  2. Van Garderen, K. J. & Lee, K. & Pesaran M., 1998. "Cross-sectional Aggregation of Non-linear Models," Cambridge Working Papers in Economics 9803, Faculty of Economics, University of Cambridge.
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  5. Bailey, Natalia & Kapetanios, George & Pesaran, M. Hashem, 2012. "Exponent of Cross-sectional Dependence: Estimation and Inference," IZA Discussion Papers 6318, Institute for the Study of Labor (IZA).
  6. Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October.
  7. Alexander Chudik & M. Hashem Pesaran, 2007. "Infinite Dimensional VARs and Factor Models," CESifo Working Paper Series 2176, CESifo Group Munich.
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  11. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
  12. Alexander Chudik & M. Hashem Pesaran & Elisa Tosetti, 2011. "Weak and strong cross‐section dependence and estimation of large panels," Econometrics Journal, Royal Economic Society, vol. 14(1), pages C45-C90, February.
  13. Pesaran, Hashem & Chudik, Alexander, 2010. "Econometric analysis of high dimensional VARs featuring a dominant unit," Working Paper Series 1194, European Central Bank.
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  22. Harry H. Kelejian, 1980. "Aggregation and Disaggregation of Nonlinear Equations," NBER Chapters, in: Evaluation of Econometric Models, pages 135-152 National Bureau of Economic Research, Inc.
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  24. Altissimo, Filippo & Mojon, Benoit & Zaffaroni, Paolo, 2009. "Can aggregation explain the persistence of inflation?," Journal of Monetary Economics, Elsevier, vol. 56(2), pages 231-241, March.
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Citations

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Cited by:
  1. Alexander Chudik & Hashem Pesaran, 2014. "Theory and Practice of GVAR Modeling," Cambridge Working Papers in Economics 1408, Faculty of Economics, University of Cambridge.
  2. Bussière, Matthieu & Chudik, Alexander & Sestieri, Giulia, 2009. "Modelling global trade flows: results from a GVAR model," Working Paper Series 1087, European Central Bank.
  3. Fabio Bacchini & Cristina Brandimarte & Piero Crivelli & Roberta De Santis & Marco Fioramanti & Alessandro Girardi & Roberto Golinelli & Cecilia Jona-Lasinio & Massimo Mancini & Carmine Pappalardo & D, 2013. "Building the core of the Istat system of models for forecasting the Italian economy: MeMo-It," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 15(1), pages 17-45.
  4. Hashem M. Pesaran & Ron P. Smith, 2011. "Beyond the DSGE Straitjacket," CESifo Working Paper Series 3447, CESifo Group Munich.
  5. Pesaran, M.H. & Chudik, A., 2011. "Aggregation in Large Dynamic Panels," Cambridge Working Papers in Economics 1118, Faculty of Economics, University of Cambridge.

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