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Nowcasting Russian GDP using forecast combination approach. (2021). Zhemkov, Michael.
In: International Economics.
RePEc:eee:inteco:v:168:y:2021:i:c:p:10-24.

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Cited: 11

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Cites: 36

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  1. Forecasting the Asian stock market volatility: Evidence from WTI and INE oil futures. (2024). Huang, Dengshi ; Ma, Feng ; Ghani, Maria.
    In: International Journal of Finance & Economics.
    RePEc:wly:ijfiec:v:29:y:2024:i:2:p:1496-1512.

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  2. The Information Content of Conflict, Social Unrest and Policy Uncertainty Measures for Macroeconomic Forecasting. (2024). Rauh, C ; Prez, J J ; Mueller, H ; Molina, L ; Diakonova, M.
    In: Cambridge Working Papers in Economics.
    RePEc:cam:camdae:2418.

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  3. Forecasting Key Macroeconomic Indicators Using DMA and DMS Methods. (2024). Pankratova, Anastasiia.
    In: Russian Journal of Money and Finance.
    RePEc:bkr:journl:v:83:y:2024:i:1:p:32-52.

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  4. .

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  5. Application of Markov-Switching MIDAS models to nowcasting of GDP and its components. (2023). Stankevich, Ivan.
    In: Applied Econometrics.
    RePEc:ris:apltrx:0474.

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  6. The economic impact of conflict-related and policy uncertainty shocks: The case of Russia. (2023). Perez, Javier J ; Molina, Luis ; Ghirelli, Corinna ; Diakonova, Marina.
    In: International Economics.
    RePEc:eee:inteco:v:174:y:2023:i:c:p:69-90.

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  7. Estimation of the Impact of Global Shocks on the Russian Economy and GDP Nowcasting Using a Factor Model. (2022). Lomonosov, Daniil ; Zubarev, Andrey ; Rybak, Konstantin.
    In: Russian Journal of Money and Finance.
    RePEc:bkr:journl:v:81:y:2022:i:2:p:49-78.

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  8. The information content of conflict, social unrest and policy uncertainty measures for macroeconomic forecasting. (2022). Pérez, Javier ; Mueller, Hannes ; Molina Sánchez, Luis ; Diakonova, Marina ; Rauh, Cristopher ; Perez, Javier J.
    In: Working Papers.
    RePEc:bde:wpaper:2232.

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References

References cited by this document

    References contributed by pko254-2043824

  1. Achkasov, Yu, 2016. Nowcasting of the Russian GDP Using Current Statistics: Approach Modification, 8. The Bank of Russia. Working Paper Series. Andreev, A., 2016. Inflation Forecasting in the Bank of Russia by Combining Forecasts, vol. 14. The Bank of Russia, pp. 2–11. Working Paper Series.

  2. Andreou, E., Ghysels, E., Kourtellos, A., 2010. Regression models with mixed sampling frequencies. J. Econom. 158 (2), 246–261.

  3. Baffigi, A., Golinelli, R., Parigi, G., 2004. Bridge models to forecast the euro area GDP. Int. J. Forecast. 20 (3), 447–460.

  4. Bernanke, B.S., Boivin, J., Eliasz, P., 2005. Measuring the effects of monetary policy: a factor-augmented vector autoregressive (FAVAR) approach//The Quarterly. J. Econ. (1), 387–422, 120.

  5. Boivin, J., Ng, S., 2006. Are more data always better for factor analysis?//. J. Econom. 132 (1), 169–194.

  6. Cepni, O., Güney, I.E., Swanson, N.R., 2019. Nowcasting and forecasting GDP in emerging markets using global financial and macroeconomic diffusion indexes. Int. J. Forecast. 35 (2), 555–572. С.

  7. Clark, T.E., et al., 2021. Tail Forecasting with Multivariate Bayesian Additive Regression Trees.

  8. Clements, M.P., Galv~ao, A.B., 2008. Macroeconomic forecasting with mixed-frequency data: forecasting output growth in the United States. J. Bus. Econ. Stat. 26 (4), 546–554.

  9. Coulombe, P.G., et al., 2020. How Is Machine Learning Useful for Macroeconomic Forecasting? Working Paper.

  10. Coulombe, P.G., Marcellino, M., Stevanovic, D., 2021. Can Machine Learning Catch the COVID-19 Recession? Working Paper.

  11. Diebold, F.X., Mariano, R., 1995. Comparing predictive accuracy. J. Bus. Econ. Stat. 13, 253–265.

  12. Doz, C., Giannone, D., Reichlin, L., 2011. A two-step estimator for large approximate dynamic factor models based on Kalman filtering. J. Econom. 164 (1), 188–205.

  13. Fair, R.C., Shiller, R.J., 1990. Comparing information in forecasts from econometric models. Am. Econ. Rev. 375–389.

  14. Ferrara, L., Marsilli, C., 2019. Nowcasting global economic growth: a factor-augmented mixed-frequency approach. World Econ. 42 (3), 846–875. С.

  15. Foroni, C., Marcellino, M., Stevanovic, D., 2020. Forecasting the Covid-19 recession and recovery: lessons from the financial crisis. Int. J. Forecast. https://doi.org/10.1016/j.ijforecast.2020.12.005. ISSN 0169-2070.

  16. Ghysels, E., Santa-Clara, P., Valkanov, R., 2004. The MIDAS Touch: Mixed Data Sampling Regression Models.

  17. Ghysels, E., Santa-Clara, P., Valkanov, R., 2006. Predicting volatility: getting the most out of return data sampled at different frequencies. J. Econom. 131 (1–2), 59–95.

  18. Giannone, D., Reichlin, L., 2008. Small D. Nowcasting: the real-time informational content of macroeconomic data. J. Monetary Econ. 55 (4), 665–676.

  19. Hendry, D.F., Hubrich, K., 2011. Combining disaggregate forecasts or combining disaggregate information to forecast an aggregate. J. Bus. Econ. Stat. 29 (2), 216–227.

  20. Huber, F., et al., 2020. Nowcasting in a pandemic using non-parametric mixed frequency VARs. J. Econom. https://doi.org/10.1016/j.jeconom.2020.11.006. ISSN 0304-4076.

  21. Hubrich, K., 2005. Forecasting euro area inflation: does aggregating forecasts by HICP component improve forecast accuracy? Int. J. Forecast. 21 (1), 119–136.

  22. Koop, G., Korobilis, D., 2011. UK macroeconomic forecasting with many predictors: which models forecast best and when do they do so? Econ. Modell. 28 (5), 2307–2318.

  23. Kuzin, V., Marcellino, M., Schumacher, C., 2011. MIDAS vs. mixed-frequency VAR: nowcasting GDP in the euro area. Int. J. Forecast. 27 (2), 529–542.

  24. Marcellino, M., Schumacher, C., 2010. Factor MIDAS for nowcasting and forecasting with ragged-edge data: a model comparison for German GDP. Oxf. Bull. Econ. Stat. 72 (4), 518–550.

  25. Mikosch, H., Solanko, L., 2019. Forecasting quarterly Russian GDP growth with mixed-frequency data. Russian Journal of Money and Finance 78 (1), 19–35.

  26. Mikosch, H., Zhang, Y., 2014. Forecasting Chinese GDP Growth with Mixed Frequency Data: Which Indicators to Look at?.

  27. Pinkwart, N., 2018. Short-term Forecasting Economic Activity in Germany: A Supply and Demand Side System of Bridge Equations.

  28. Porshakov, A., Ponomarenko, A., Sinyakov, A., 2016. Nowcasting and short-term forecasting of Russian GDP with a dynamic factor model. Journal of the New Economic Association 60.

  29. Raftery, A.E., Karný, M., Ettler, P., 2010. Online prediction under model uncertainty via dynamic model averaging: application to a cold rolling mill. Technometrics 52 (1), 52–66.
    Paper not yet in RePEc: Add citation now
  30. Schumacher, C., Breitung, J., 2008. Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data. Int. J. Forecast. 24 (3), 386–398.

  31. Stock, J.H., Watson, M.W., 2005. Implications of dynamic factor models for VAR analysis. National Bureau of Economic Research w11467.

  32. Styrin, K., 2019. Forecasting inflation in Russia using dynamic model averaging. Russian Journal of Money and Finance 78 (1), 3–18.

  33. Timmermann, A., 2006. Forecast combinations. Handb. Econ. Forecast. 1, 135–196.

  34. Wallis, K.F., 1986. Forecasting with an econometric model: the ‘ragged edge’ problem. J. Forecast. 5 (1), 1–13. M. Zhemkov International Economics 168 (2021) 10–24.
    Paper not yet in RePEc: Add citation now
  35. Т Kim, H.H., Swanson, N.R., 2018. Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods, С Int. J. Forecast. 34 (2), 339–354.

  36. Т Poncela, P., et al., 2011. Forecast combination through dimension reduction techniques, С Int. J. Forecast. 27 (2), 224–237.

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  1. Nowcasting Russian GDP using forecast combination approach. (2021). Zhemkov, Michael.
    In: International Economics.
    RePEc:eee:inteco:v:168:y:2021:i:c:p:10-24.

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  4. Monetary Policy Shocks in the Russian Economy and Their Macroeconomic Effects. (2019). Rostova, Natalia A ; Mamonov, Mikhail E ; Pestova, Anna A.
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  7. On the use of high frequency measures of volatility in MIDAS regressions. (2016). Andreou, Elena.
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  14. The estimation of continuous time models with mixed frequency data. (2016). Chambers, Marcus.
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  15. On the use of high frequency measures of volatility in MIDAS regressions. (2016). Andreou, Elena.
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  54. MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area. (2009). Schumacher, Christian ; Marcellino, Massimiliano ; Kuzin, Vladimir .
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