Does the high-frequency data is helpful for forecasting Russian inflation?

Authors

  • Dmitriy Tretyakov Russian Academy of National Economy and Public Administration, Moscow
  • Nikita Fokin Russian Academy of National Economy and Public Administration,Moscow https://orcid.org/0000-0002-4058-7331

DOI:

https://doi.org/10.21638/spbu05.2021.206

Abstract

Due to the fact that at the end of 2014 the Central Bank made the transition to a new monetary policy regime for Russia — the inflation targeting regime, the problem of forecasting inflation rates became more relevant than ever. In the new monetary policy regime, it is important for the Bank of Russia to estimate the future inflation rate as quickly as possible in order to take measures to return inflation to the target level. In addition, for effective monetary policy, the households must trust the actions of monetary authorities and they must be aware of the future dynamics of inflation. Thus, to manage inflationary expectations of economic agents, the Central Bank should actively use the information channel, publish accurate forecasts of consumer price growth. The aim of this work is to build a model for nowcasting, as well as short-term forecasting of the rate of Russian inflation using high-frequency data. Using this type of data in models for forecasting is very promising, since this approach allows to use more information about the dynamics of macroeconomic indicators. The paper shows that using MIDAS model with weekly frequency series (RUB/USD exchange rate, the interbank rate MIACR, oil prices) has more accurate forecast of monthly inflation compared to several basic models, which only use low-frequency data.

Keywords:

inflation, nowcasting, forecasting, high-frequency data, MIDAS model

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References

Литература

Андреев А. (2016) Прогнозирование инфляции методом комбинирования прогнозов в Банке России. Банк России. Серия докладов об экономических исследованиях. Т. 14. С. 2–11.

Байбуза И. (2018) Прогнозирование инфляции с помощью методов машинного обучения. Деньги и кредит. Т. 77, № 4. С. 42–59.

Гафаров Б. Н. (2011) Кривая Филлипса и становление рынка труда в России. Экономический журнал Высшей школы экономики. Т. 15, № 2. С. 155–176.

Зубарев А. В. (2018) Об оценке кривой Филлипса для российской экономики. Экономический журнал Высшей школы экономики. Т. 22, № 1. C. 40–58.

Микош Х., Соланко Л. (2019) Прогнозирование роста российского ВВП с использованием данных со смешанной периодичностью. Деньги и кредит. Т. 78, № 1. С. 19–35.

Павлов Е. (2020) Прогнозирование инфляции в России с помощью нейронных сетей. Деньги и кредит. Т. 79, № 1. С. 57–73.

Перевышин Ю., Перевышина Е. (2015) Эффект переноса процентных ставок в России в 2010–2014 годах. Экономическая политика. Т. 10, № 5. С. 38 -52.

Пестова А. А. (2018) Об оценке эффектов монетарной политики в России: роль пространства шоков и изменений режимов политики. Вопросы экономики. № 2. С. 33–55.

Пономарев Ю., Трунин П., Улюкаев А. (2014) Эффект переноса динамики обменного курса на цены в России. Вопросы экономики. № 3. С. 21–35.

Стырин К. (2019) Прогнозирование инфляции в России методом динамического усреднения моделей. Деньги и кредит. Т. 78, № 1. С. 3–18.

Тишин А. (2019) Неожиданные шоки ДКП в России. Деньги и кредит. Т. 78, № 4. С. 48–70.

Angelini E., Bańbura M., Rünstler G. (2010). Estimating and forecasting the euro area monthly national accounts from a dynamic factor model. OECD Journal: Journal of Business Cycle Measurement and Analysis, no. 1, pp. 1–22.

Baffigi A., Golinelli R., Parigi G. (2004). Bridge models to forecast the euro area GDP. International Journal of forecasting, vol. 20, no. 3, pp. 447–460.

Baumeister C., Guérin P., Kilian L. (2015) Do high-frequency financial data help forecast oil prices? The MIDAS touch at work. International Journal of Forecasting, vol. 31, no. 2, pp. 238–252.

Breitung J., Roling C. (2015) Forecasting inflation rates using daily data: A nonparametric MIDAS approach.Journal of Forecasting, vol. 34, no. 7, pp. 588–603.

Delle Monache D., Petrella I. (2017) Adaptive models and heavy tails with an application to inflation forecasting. International Journal of Forecasting, vol. 33, no. 2, pp. 482–501.

Galí J., Gertler M. (1999) Inflation dynamics: A structural econometric analysis. Journal of monetary Economics, vol. 44, no. 2, pp. 195–222.

Ghysels E., Santa-Clara P., Valkanov R. (2004) The MIDAS touch: Mixed data sampling regression models. Série Scientifique, Mai. Montréal. 34 p.

Ghysels E., Sinko A., Valkanov R. (2007) MIDAS regressions: Further results and new directions. Econometric Reviews, vol. 26, no. 1, pp. 53–90.

Ghysels E., Kvedaras V., Zemlys V. (2016) Mixed frequency data sampling regression models: the R package midasr. Journal of statistical software, vol. 72, no. 4, pp. 1–35.

Lucas R. E. (1973) Some international evidence on output-inflation tradeoffs. The American Economic Review, vol. 63, no. 3, pp. 326–334.

McKnight S., Mihailov A., Rumler F. (2020) Inflation forecasting using the New Keynesian Phillips Curve with a time-varying trend. Economic Modelling, vol. 87, pp. 383–393.

Medeiros M. C., Vasconcelos G. F., Veiga A., Zilberman E. (2021) Forecasting inflation in a data-rich environment: the benefits of machine learning methods. Journal of Business & Economic Statistics,vol. 39, no. 1, pp. 98–119.

Parigi G., Schlitzer G. (1995) Quarterly forecasts of the Italian business cycle by means of monthly economic indicators. Journal of Forecasting, vol. 14, no. 2, pp. 117–141.

Ribon S., Suhoy T. (2011) Forecasting short run inflation using MIDAS. Research Department, Bank of Israel.31 p.

Sargent T. J., Wallace N. (1975) “Rational” Expectations, the Optimal Monetary Instrument, and the Optimal Money Supply Rule. Journal of Political Economy, vol. 83, no. 2, pp. 241–254.

Sargent T. J., Wallace N. (1976) Rational expectations and the theory of economic policy. Journal of Monetary economics, vol. 2, no. 2, pp. 169–183.

Stock J. H., Watson M. W. (2007) Why has US inflation become harder to forecast? Journal of Money, Credit and banking, vol. 39, pp. 3–33.

Trehan B. (1989) Forecasting growth in current quarter real GNP. Economic Review-Federal Reserve Bank of San Francisco, no. 1, p. 39.


References

Andreev A. (2016) Forecasting inflation by combining forecasts at the Bank of Russia. Bank of Russia. Economic Research Report Series, vol. 14, pp. 2–11. (In Russian)

Angelini E., Bańbura M., Rünstler G. (2010) Estimating and forecasting the euro area monthly national accounts from a dynamic factor model. OECD Journal: Journal of Business Cycle Measurement and Analysis, no. 1, pp. 1–22.

Baffigi A., Golinelli R., Parigi G. (2004). Bridge models to forecast the euro area GDP. International Journal of forecasting, vol. 20, no. 3, pp. 447–460.

Baumeister C., Guérin P., Kilian L. (2015) Do high-frequency financial data help forecast oil prices? The MIDAS touch at work. International Journal of Forecasting, vol. 31, no. 2, pp. 238–252.

Baybuza I. (2018) Inflation Forecasting Using Machine Learning Methods. Russian Journal of Money and Finance, vol. 77, no. 4, pp. 42–59. (In Russian)

Breitung J., Roling C. (2015) Forecasting inflation rates using daily data: A nonparametric MIDAS approach. Journal of Forecasting, vol. 34, no. 7, pp. 588–603.

Delle Monache, D., Petrella, I. (2017). Adaptive models and heavy tails with an application to inflation forecasting. International Journal of Forecasting, vol. 33, no. 2, pp. 482–501.

Galí J., Gertler M. (1999) Inflation dynamics: A structural econometric analysis. Journal of monetary Economics, vol. 44, no. 2, pp. 195–222.

Gafarov B. N. (2011) Phillips curve and the formation of the labor market in Russia. Economic Journal of the Higher School of Economics, vol. 15, no. 2, pp. 155 176. (In Russian)

Ghysels E., Santa-Clara P., Valkanov R. (2004) The MIDAS touch: Mixed data sampling regression models.Série Scientifique, Mai. Montréal. 34 p.

Ghysels E., Sinko A., Valkanov R. (2007) MIDAS regressions: Further results and new directions. Econometric Reviews, vol. 26, no. 1, pp. 53–90.

Ghysels E., Kvedaras V., Zemlys V. (2016) Mixed frequency data sampling regression models: the R package midasr. Journal of statistical software, vol. 72, no. 4, pp. 1–35.

Lucas R. E. (1973) Some international evidence on output-inflation tradeoffs. The American Economic Review, vol. 63, no. 3, pp. 326–334.

McKnight S., Mihailov A., Rumler F. (2020) Inflation forecasting using the New Keynesian Phillips Curve with a time-varying trend. Economic Modelling, vol. 87, pp. 383–393.

Medeiros M. C., Vasconcelos G. F., Veiga, A., Zilberman E. (2021). Forecasting inflation in a data-rich environment: the benefits of machine learning methods. Journal of Business & Economic Statistics,vol. 39, no. 1, pp. 98–119.

Mikosch H., Solanko L. (2019) Forecasting Quarterly Russian GDP Growth with Mixed-Frequency Data. Russian Journal of Money and Finance, vol. 78, no. 1, pp. 19–35. (In Russian)

Pavlov E. (2020) Forecasting Inflation in Russia Using Neural Networks. Russian Journal of Money and Finance, vol. 79, no. 1, pp. 57–73. (In Russian)

Parigi G., Schlitzer G. (1995) Quarterly forecasts of the Italian business cycle by means of monthly economic indicators. Journal of Forecasting, vol. 14, no. 2, pp. 117–141.

Perevyshin Yu., Perevyshina E. (2015) The effect of the transfer of interest rates in Russia in 2010-2014. Economic Policy, vol. 10, no. 5, pp. 38–52. (In Russian)

Pestova A. A. (2018) On the effects of monetary policy in Russia: The role of the space of spanned and the policy regime shifts. Voprosy Ekonomiki, no. 2, pp. 33 55. (In Russian)

Ponomarev Y., Trunin P., Ulyukaev A. (2014) Exchange Rate Pass-through in Russia. Voprosy Ekonomiki, no. 3, pp. 21–55. (In Russian)

Ribon S., Suhoy T. (2011) Forecasting short run inflation using MIDAS. Research Department, Bank of Israel.31 p.

Sargent T. J., Wallace N. (1975) “Rational” Expectations, the Optimal Monetary Instrument, and the Optimal Money Supply Rule. Journal of Political Economy, vol. 83, no. 2, pp. 241–254.

Sargent T. J., Wallace N. (1976) Rational expectations and the theory of economic policy. Journal of Monetary economics, vol. 2, no. 2, pp. 169–183.

Stock J. H., Watson M. W. (2007) Why has US inflation become harder to forecast? Journal of Money, Credit and banking, vol. 39, pp. 3–33.

Styrin K. (2019) Forecasting Inflation in Russia Using Dynamic Model Averaging. Russian Journal of Money and Finance, vol. 78, no. 1, pp. 3–18. (In Russian)

Tishin A. (2019) Monetary Policy Surprises in Russia. Russian Journal of Money and Finance, vol. 78, no. 4, pp. 48–70. (In Russian)

Trehan B. (1989) Forecasting growth in current quarter real GNP. Economic Review-Federal Reserve Bank of San Francisco, no. 1, p. 39.

Zubarev A. (2018) On the Estimation of the Phillips Curve for the Russian Economy. HSE Economic Journal, vol. 22, no. 1, pp. 40–58. (In Russian)

Published

2021-09-14

How to Cite

Tretyakov, D., & Fokin, N. (2021). Does the high-frequency data is helpful for forecasting Russian inflation?. St Petersburg University Journal of Economic Studies, 37(2), 318–343. https://doi.org/10.21638/spbu05.2021.206

Issue

Section

Mathematical and instrumental methods of economics