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Aggregation in large dynamic panels

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  • Pesaran, M. Hashem
  • Chudik, Alexander

Abstract

This paper investigates 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 used (i) to establish conditions under which Granger’s (1980) conjecture regarding the long memory properties of aggregate variables from ‘a very large scale dynamic, econometric model’ holds, and (ii) to show which distributional features of micro parameters can be identified from the aggregate model. The paper also derives impulse response functions for the aggregate variables, distinguishing between the effects of composite macro and aggregated idiosyncratic shocks. Some of the findings of the paper are illustrated by Monte Carlo experiments. The paper also contains 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.

Suggested Citation

  • Pesaran, M. Hashem & Chudik, Alexander, 2014. "Aggregation in large dynamic panels," Journal of Econometrics, Elsevier, vol. 178(P2), pages 273-285.
  • Handle: RePEc:eee:econom:v:178:y:2014:i:p2:p:273-285
    DOI: 10.1016/j.jeconom.2013.08.027
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    Cited by:

    1. Bernard Candelpergher & Michel Miniconi & Florian Pelgrin, 2015. "Long-memory process and aggregation of AR(1) stochastic processes: A new characterization," Working Papers hal-01166527, HAL.
    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. Pesaran, M. Hashem & Chudik, Alexander, 2014. "Aggregation in large dynamic panels," Journal of Econometrics, Elsevier, vol. 178(P2), pages 273-285.
    4. Chudik, Alexander & Pesaran, M. Hashem, 2015. "Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors," Journal of Econometrics, Elsevier, vol. 188(2), pages 393-420.
    5. M. Hashem Pesaran & Cynthia Fan Yang, 2016. "Econometric Analysis of Production Networks with Dominant Units," CESifo Working Paper Series 6141, CESifo Group Munich.
    6. Alexander Chudik & M. Hashem Pesaran, 2016. "Theory And Practice Of Gvar Modelling," Journal of Economic Surveys, Wiley Blackwell, vol. 30(1), pages 165-197, February.
    7. 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.
    8. Alexander Chudik & Kamiar Mohaddes & M. Hashem Pesaran & Mehdi Raissi, 2016. "Long-Run Effects in Large Heterogeneous Panel Data Models with Cross-Sectionally Correlated Errors," Advances in Econometrics,in: Essays in Honor of Aman Ullah, volume 36, pages 85-135 Emerald Publishing Ltd.
    9. Balaguer, Jacint & Ripollés, Jordi, 2016. "Asymmetric fuel price responses under heterogeneity," Energy Economics, Elsevier, vol. 54(C), pages 281-290.
    10. Chudik, Alexander & Grossman, Valerie & Pesaran, M. Hashem, 2016. "A multi-country approach to forecasting output growth using PMIs," Journal of Econometrics, Elsevier, vol. 192(2), pages 349-365.
    11. Anamaria Illes & Marco Lombardi & Paul Mizen, 2015. "Why Did Bank Lending Rates Diverge from Policy Rates After the Financial Crisis?," Discussion Papers 2015/05, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    12. Pesaran, M. Hashem & Smith, Ron P., 2011. "Beyond the DSGE Straitjacket," IZA Discussion Papers 5661, Institute for the Study of Labor (IZA).
    13. Jondeau, Eric & Pelgrin, Florian, 2014. "Estimating aggregate autoregressive processes when only macro data are available," Economics Letters, Elsevier, vol. 124(3), pages 341-347.
    14. Cesa-Bianchi, Ambrogio & Pesaran, M Hashem & Rebucci, Alessandro, 2018. "Uncertainty and Economic Activity: A Multi-Country Perspective," CEPR Discussion Papers 12713, C.E.P.R. Discussion Papers.
    15. Yunus Emre Ergemen, 2016. "System Estimation of Panel Data Models under Long-Range Dependence," CREATES Research Papers 2016-02, Department of Economics and Business Economics, Aarhus University.
    16. Rodríguez Caballero, Carlos Vladimir & Ergemen, Yunus Emre, 2017. "Estimation of a Dynamic Multilevel Factor Model with possible long-range dependence," DES - Working Papers. Statistics and Econometrics. WS 24614, Universidad Carlos III de Madrid. Departamento de Estadística.

    More about this item

    Keywords

    Aggregation; Large dynamic panels; Long memory; Weak and strong cross section dependence; VAR models; Impulse responses; Factor models; Inflation persistence;

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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