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Nowcasting the Czech Trade Balance

Author

Listed:
  • Oxana Babecka Kucharcukova
  • Jan Bruha

Abstract

In this paper we are interested in nowcasting and short-run forecasting of the main external trade variables. We consider four empirical methods: principal component regression, elastic net regression, the dynamic factor model and partial least squares. We discuss the adaptation of those methods to asynchronous data releases and to the mixed-frequency set-up. We contrast them with a set of univariate benchmarks. We find that for variables in value terms (both nominal and real), elastic net regression typically yields the most accurate predictions, followed by the dynamic factor model and then by principal components. For export and import prices, univariate techniques seem to have the higher precision for backcasting and nowcasting, but for short-run forecasting the more sophisticated methods tend to produce more accurate forecasts. Here again, elastic net regression dominates the other methods.

Suggested Citation

  • Oxana Babecka Kucharcukova & Jan Bruha, 2016. "Nowcasting the Czech Trade Balance," Working Papers 2016/11, Czech National Bank.
  • Handle: RePEc:cnb:wpaper:2016/11
    as

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    File URL: https://www.cnb.cz/export/sites/cnb/en/economic-research/.galleries/research_publications/cnb_wp/cnbwp_2016_11.pdf
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    References listed on IDEAS

    as
    1. Marine Carrasco & Barbara Rossi, 2016. "In-Sample Inference and Forecasting in Misspecified Factor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 313-338, 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. Katerina Arnostova & David Havrlant & Luboš Rùžièka & Peter Tóth, 2011. "Short-Term Forecasting of Czech Quarterly GDP Using Monthly Indicators," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(6), pages 566-583, December.
    4. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    5. Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2011. "A two-step estimator for large approximate dynamic factor models based on Kalman filtering," Journal of Econometrics, Elsevier, vol. 164(1), pages 188-205, September.
    6. repec:hal:journl:peer-00844811 is not listed on IDEAS
    7. James H. Stock & Mark W. Watson, 2012. "Generalized Shrinkage Methods for Forecasting Using Many Predictors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 481-493, June.
    8. Frantisek Brazdik & Jan Bruha & Michal Franta & David Havrlant & Tibor Hledik & Tomas Holub & Zuzana Humplova & Frantisek Kopriva & Jiri Polansky & Marek Rusnak & Jaromir Tonner, 2015. "Forecasting," Occasional Publications - Edited Volumes, Czech National Bank, edition 1, volume 13, number rb13/1 edited by Jan Babecky & Kamil Galuscak, March.
    9. Smeekes, Stephan & Wijler, Etienne, 2018. "Macroeconomic forecasting using penalized regression methods," International Journal of Forecasting, Elsevier, vol. 34(3), pages 408-430.
    10. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    11. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
    12. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    13. Jushan Bai & Peng Wang, 2015. "Identification and Bayesian Estimation of Dynamic Factor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(2), pages 221-240, April.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Dynamic factor models; elastic net regression; mixed-frequency data; nowcasting; principal component analysis; state space models; trade balance;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • F17 - International Economics - - Trade - - - Trade Forecasting and Simulation

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