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Forecasting Czech GDP Using Mixed-Frequency Data Models

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Abstract

In this paper we use a battery of various mixed-frequency data models to forecast Czech GDP growth. The models employed are mixed-frequency vector autoregressions, mixed-data sampling models, and the dynamic factor model. Using a dataset of historical vintages of unrevised macroeconomic and financial data, we evaluate the performance of these models over the 2005–2014 period and compare them with the Czech National Bank’s macroeconomic forecasts. The results suggest that for shorter forecasting horizons the CNB forecasts outperform forecasts based on the mixed-frequency data models. At longer horizons, mixed-frequency vector autoregressions and the dynamic factor model are able to perform similarly or slightly better than the CNB forecasts. Furthermore, moving away from point forecasts, we also explore the potential of density forecasts from Bayesian mixed-frequency vector autoregressions.

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Notes

  1. Recently developed time-series models employing mixed-frequency data also include factor-MIDAS (Marcellino and Schumacher 2010) and mixed sampling frequency VAR models (Ghysels 2016). For a recent overview of methods that deal with mixed-frequency data see Foroni and Marcellino (2013).

  2. Arnoštová et al. (2010) consider a naïve MA(4) model of GDP growth, the model based on prediction of the expenditure components of GDP used in the CNB for near-term output forecasting, a simple bivariate VAR model of quarterly GDP growth and an aggregated monthly indicator, and a bridge equations model that starts with forecasting of monthly indicators, which are then combined with the GDP series in a VAR model for quarterly data. Apart from these four models, Arnoštová et al. (2010) also examine several factor models based on static principal components and dynamic factors.

  3. Regarding the relative forecasting performance of MIDAS and MF-VAR in output forecasting, Kuzin et al. (2011) show that in the case of nowcasting and forecasting of euro area GDP growth, MF-VAR performs better for longer horizons (5–9 months) while MIDAS outperforms MF-VAR at shorter horizons (1–4 months).

  4. Since nowcasts are computed for several points in a quarter only, the MSE is computed at the same point in the previous four quarters. Next, note that for forecasts the mean squared errors used to compute the weights for quarter \( t_{q} \) are taken for quarters \( t_{q - 2} , \ldots ,t_{q - 5} \) to account for the GDP publication lag.

  5. Two additional schemes for coefficients are considered: a normalized beta polynomial scheme with a zero last lag and a normalized exponential Almon lag polynomial scheme. The results are available upon request.

  6. In our case, using the last vintage data instead of the data available at the time of the forecast does not consistently lower the forecast errors. There is some evidence of a decline in errors for the DFM at short horizons and also for MF-BVAR at medium horizons. Often, however, using last vintage data worsens the forecasting performance, although the differences in the RMSE are mostly rather small. The detailed results are available upon request.

  7. As another robustness check, some coincident indicators—industrial production, construction, and the unemployment rate—were excluded from the set of monthly indicators for forecasting. While coincident indicators are important for nowcasting, their usefulness for forecasting could be questioned. The results of the forecasting performance exercise with the reduced set of monthly indicators are almost unchanged. Implicit weighting of the DFM or explicit weighting based on the MSE seems to be sufficient to deal with the different importance of different monthly indicators for nowcasting and forecasting.

References

  • Aastveit, K. A., Gerdrup, K. R., Jore, A. S., & Thorsrud, L. A. (2014). Nowcasting GDP in real-time: A density combination approach. Journal of Business & Economic Statistics, 32(1), 48–68.

    Article  Google Scholar 

  • Andreou, E., Ghysels, E., & Kourtellos, A. (2011). Forecasting with mixed-frequency data. In Clements, M., & Hendry, F. (Eds.), The Oxford handbook of economic forecasting. Oxford University Press, pp 225–245.

  • Andrle, M., Hlédik, T., Kameník, O., & Vlček, K. (2009). Implementing the new structural model of the Czech National Bank. Czech National Bank Working Paper 2/2009, Czech National Bank, Prague

  • Armesto, M., Engemann, K., & Owyang, M. (2010). Forecasting with mixed frequencies. The Federal Reserve Bank of St. Louis Review, 92(6), 521–536.

    Google Scholar 

  • Arnoštová, K., Havrlant, D., Růžička, L., & Tóth, P. (2010). Short-term forecasting of Czech quarterly GDP using monthly indicators. Czech National Bank Working Papers, No. 12/2010, Czech National Bank, Prague

  • Bache, I. W., Brubakk, L., Jore, A. S., Maih, J., & Nicolaisen, J. (2010). Monetary policy analysis in practice—a conditional forecasting approach. Norges Bank Monetary Policy Staff Memo, No. 8/2010.

  • Banbura, M., Giannone, D., & Lenza, M. (2015). Conditional forecasts and scenario analysis with vector autoregressive models for large cross-sections. International Journal of Forecasting, 31(3), 739–756.

    Article  Google Scholar 

  • Banbura, M., & Modugno, M. (2014). Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data. Journal of Applied Econometrics, 29(1), 133–160.

    Article  Google Scholar 

  • Brůha, J., Hlédik, T., Holub, T., Polanský, J., & Tonner, J. (2013). Incorporating judgments and dealing with data uncertainty in forecasting at the Czech National Bank. Czech National Bank Research and Policy Note, No. 2/2013, Czech National Bank, Prague

  • Camacho, M., & Perez-Quiros, G. (2010). Introducing the Euro-sting: Short-term indicator of Euro area growth. Journal of Applied Econometrics, 25(4), 663–694.

    Article  Google Scholar 

  • Canova, F. (2007). Methods for applied macroeconomic research. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Chiu, C. W., Eraker, B., Foerster, A. T., Kim, T. B., & Seoane, H. D. (2012). Estimating VAR’s sampled at mixed or irregular spaced frequencies: A Bayesian approach. The Federal Reserve Bank of Kansas City Research Working Paper, No. 11-11, Federal Reserve Bank of Kansas City, Kansas City

  • Clements, M. P., & Galvão, A. B. (2008). Macroeconomic forecasting with mixed-frequency data: Forecasting output growth in the United States. Journal of Business & Economic Statistics, 26(4), 546–554.

    Article  Google Scholar 

  • Clements, M. P., & Galvão, A. B. (2009). Forecasting US output growth using leading indicators: An appraisal using MIDAS models. Journal of Applied Econometrics, 24, 1187–1206.

    Article  Google Scholar 

  • Coats, W., Laxton, D., & Rose, D. (2003). The Czech National Bank’s forecasting and policy analysis system. Prague: Czech National Bank.

  • Foroni, C., & Marcellino, M. (2013). A survey of econometrics methods for mixed-frequency data. Norges Bank Working Paper, No. 2013/06, Norges Bank, Oslo

  • Foroni, C., & Marcellino, M. (2014). A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates. International Journal of Forecasting, 30, 554–568.

    Article  Google Scholar 

  • Foroni, C., Schumacher, C., & Marcellino, M. (2015). Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials. Journal of the Royal Statistical Society: Series A (Statistics in Society), 178(1), 57–82.

  • Ghysels, E. (2016). Macroeconomics and the reality of mixed frequency data. Journal of Econometrics, 193(2), 294–314.

    Article  Google Scholar 

  • Ghysels, E., Santa-Clara, P., & Valkanov, R. (2004). The MIDAS touch: Mixed data sampling regressions. CIRANO Working Paper, No. 2004s-20, CIRANO, Montreal

  • Ghysels, E., Sinko, A., & Valkanov, R. (2007). MIDAS regressions: Further results and new directions. Econometric Reviews, 26(1), 53–90.

    Article  Google Scholar 

  • Giannone, D., Reichlin, L., & Small, D. (2008). Nowcasting: the real-time informational content of macroeconomic data. Journal of Monetary Economics, 55(4), 665–676.

    Article  Google Scholar 

  • Havranek, T., Horvath, R., & Mateju, J. (2012). Monetary transmission and the financial sector in the Czech Republic. Economic Change and Restructuring, 45, 135–155.

    Article  Google Scholar 

  • Kascha, C., & Ravazzolo, F. (2010). Combining inflation density forecasts. Journal of Forecasting, 29(1–2), 231–250.

    Article  Google Scholar 

  • Kuzin, V., Marcellino, M., & Schumacher, C. (2011). MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro area. International Journal of Forecasting, 27, 529–542.

    Article  Google Scholar 

  • Marcellino, M., Porqueddu, M., & Vendetti, F. (2013). Short-term GDP forecasting with a mixed frequency dynamic factor model with stochastic volatility. Journal of Business and Economic Statistics, 34(1), 118–127.

    Article  Google Scholar 

  • Marcellino, M., & Schumacher, C. (2010). Factor-MIDAS for now—and forecasting with ragged-edge data: A model comparison for German GDP. Oxford Bulletin of Economics and Statistics, 72, 518–550.

    Article  Google Scholar 

  • Mariano, R. S., & Murasawa, Y. (2003). A new coincident index of business cycles based on monthly and quarterly series. Journal of Applied Econometrics, 18, 427–443.

    Article  Google Scholar 

  • Mariano, R. S., & Murasawa, Y. (2010). A coincident index, common factors, and monthly real GDP. Oxford Bulletin of Economics and Statistics, 72(1), 27–46.

    Article  Google Scholar 

  • Mitchell, J., & Hall, S. G. (2005). Evaluating, comparing, and combining density forecasts using the KLIC with an application to the Bank of England and NIESR “fan” charts of inflation. Oxford Bulletin of Economics and Statistics, 67, 995–1033.

    Article  Google Scholar 

  • Rusnák, M. (2013a). Nowcasting Czech GDP in real time, Czech National Bank Working Papers, No. 6/13, Czech National Bank, Prague

  • Rusnák, M. (2013b). Revisions to the Czech National Accounts: Properties and predictability. Czech Journal of Economics and Finance (Finance a uver), 63(3), 244–261.

    Google Scholar 

  • Schorfheide, F., & Song, D. (2013). Real-time forecasting with a mixed frequency VAR. NBER Working Paper, No. 19712.

  • Sims, C. A. (2002). The role of models and probabilities in the monetary policy process. Brookings Papers on Economic Activity, 33(2), 1–62.

    Article  Google Scholar 

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Acknowledgments

We acknowledge support from the Czech National Bank (Project No. B6/13), Rusnák acknowledges support from Grant Agency of Charles University (#888413) and the Grant Agency of Czech Republic (Grant p402/12/G097). We thank Marta Bańbura and Eric Ghysels for sharing parts of their Matlab codes. We also thank Oxana Babecká Kucharčuková, Claudia Foroni, Ana Beatriz Galvão, seminar participants at the Czech National Bank and two anonymous referees for their helpful comments. The views expressed here are those of authors and not necessarily those of the Czech National Bank.

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Authors and Affiliations

  1. Czech National Bank, Prague, Czech Republic

    Michal Franta, David Havrlant & Marek Rusnák

  2. Institute of Economic Studies, Charles University, Prague, Czech Republic

    Marek Rusnák

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  1. Michal Franta
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  2. David Havrlant
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  3. Marek Rusnák
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Correspondence to Michal Franta.

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Franta, M., Havrlant, D. & Rusnák, M. Forecasting Czech GDP Using Mixed-Frequency Data Models. J Bus Cycle Res 12, 165–185 (2016). https://doi.org/10.1007/s41549-016-0008-z

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