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Forecasting with Approximate Dynamic Factor Models: the Role of Non-Pervasive Shocks

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  • Matteo Luciani

Abstract

This paper studies the role of non-pervasive shocks when forecasting with factor models. To this end, we first introduce a new model that incorporates the effects of non-pervasive shocks, an Approximate Dynamic Factor Model with a sparse model for the idiosyncratic component. Then, we test the forecasting performance of this model both in simulations, and on a large panel of US quarterly data. We find that, when the goal is to forecast a disaggregated variable, which is usually affected by regional or sectorial shocks, it is useful to capture the dynamics generated by non-pervasive shocks; however, when the goal is to forecast an aggregate variable, which responds primarily to macroeconomic, i.e. pervasive, shocks, accounting for non-pervasive shocks is not useful.

Suggested Citation

  • Matteo Luciani, 2011. "Forecasting with Approximate Dynamic Factor Models: the Role of Non-Pervasive Shocks," Working Papers ECARES ECARES 2011‐022, ULB -- Universite Libre de Bruxelles.
  • Handle: RePEc:eca:wpaper:2013/97308
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    Cited by:

    1. Jack Fosten, 2017. "Model selection with estimated factors and idiosyncratic components," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(6), pages 1087-1106, September.
    2. repec:eee:intfor:v:33:y:2017:i:4:p:786-800 is not listed on IDEAS
    3. Pablo Duarte & Bernd Süssmuth, 2018. "Implementing an Approximate Dynamic Factor Model to Nowcast GDP Using Sensitivity Analysis," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 127-141, April.
    4. Hindrayanto, Irma & Koopman, Siem Jan & de Winter, Jasper, 2016. "Forecasting and nowcasting economic growth in the euro area using factor models," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1284-1305.
    5. Bragoli, Daniela & Modugno, Michele, 2017. "A now-casting model for Canada: Do U.S. variables matter?," International Journal of Forecasting, Elsevier, vol. 33(4), pages 786-800.
    6. Smeekes, Stephan & Wijler, Etienne, 2018. "Macroeconomic forecasting using penalized regression methods," International Journal of Forecasting, Elsevier, vol. 34(3), pages 408-430.
    7. Bragoli, Daniela, 2017. "Now-casting the Japanese economy," International Journal of Forecasting, Elsevier, vol. 33(2), pages 390-402.
    8. Alessandro Girardi & Roberto Golinelli & Carmine Pappalardo, 2017. "The role of indicator selection in nowcasting euro-area GDP in pseudo-real time," Empirical Economics, Springer, vol. 53(1), pages 79-99, August.
    9. Dahlhaus, Tatjana & Guénette, Justin-Damien & Vasishtha, Garima, 2017. "Nowcasting BRIC+M in real time," International Journal of Forecasting, Elsevier, vol. 33(4), pages 915-935.
    10. Matteo Luciani & David Veredas, "undated". "A simple model for vast panels of volatilities," ULB Institutional Repository 2013/136239, ULB -- Universite Libre de Bruxelles.
    11. Matteo Luciani & Lorenzo Ricci, 2014. "Nowcasting Norway," International Journal of Central Banking, International Journal of Central Banking, vol. 10(4), pages 215-248, December.
    12. S. Delle Chiaie & L. Ferrara & D. Giannone, 2017. "Common Factors of Commodity Prices," Working papers 645, Banque de France.
    13. Luciani, Matteo & Pundit, Madhavi & Ramayandi, Arief & Veronese, Giovanni, 2015. "Nowcasting Indonesia," Finance and Economics Discussion Series 2015-100, Board of Governors of the Federal Reserve System (U.S.).
    14. Barigozzi, Matteo & Lippi, Marco & Luciani, Matteo, 2016. "Non-Stationary Dynamic Factor Models for Large Datasets," Finance and Economics Discussion Series 2016-024, Board of Governors of the Federal Reserve System (U.S.), revised 18 Jul 2017.
    15. Matteo Barigozzi & Marco Lippi & Matteo Luciani, 2014. "Dynamic Factor Models, Cointegration and Error Correction Mechanisms," Working Papers ECARES ECARES 2014-14, ULB -- Universite Libre de Bruxelles.
    16. Bai, Jushan & Liao, Yuan, 2016. "Efficient estimation of approximate factor models via penalized maximum likelihood," Journal of Econometrics, Elsevier, vol. 191(1), pages 1-18.
    17. Hyun Hak Kim, 2013. "Forecasting Macroeconomic Variables Using Data Dimension Reduction Methods: The Case of Korea," Working Papers 2013-26, Economic Research Institute, Bank of Korea.
    18. Li, Jiahan & Chen, Weiye, 2014. "Forecasting macroeconomic time series: LASSO-based approaches and their forecast combinations with dynamic factor models," International Journal of Forecasting, Elsevier, vol. 30(4), pages 996-1015.
    19. Barigozzi, Matteo & Hallin, Marc, 2017. "Generalized dynamic factor models and volatilities: estimation and forecasting," Journal of Econometrics, Elsevier, vol. 201(2), pages 307-321.
    20. David de Antonio Liedo, 2014. "Nowcasting Belgium," Working Paper Research 256, National Bank of Belgium.
    21. repec:eee:ecmode:v:68:y:2018:i:c:p:127-144 is not listed on IDEAS

    More about this item

    Keywords

    Penalized regressions; Local factors; Bayesian shrinkage; Forecasting;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • 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
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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