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Estimating Sustainable Output Growth in Emerging Market Economies


  • Anna Krupkina

    () (1] Bank of Russia, 107016, Neglinnaya 12, Moscow, Russia. E-mails:;[2] NRU-HSE, Myasnitskaya 20, 101000, Moscow, Russia.)

  • Elena Deryugina

    (Bank of Russia, 107016, Neglinnaya 12, Moscow, Russia. E-mails:;

  • Alexey Ponomarenko

    (Bank of Russia, 107016, Neglinnaya 12, Moscow, Russia. E-mails:;


We present a model that incorporates the information contained in diverse variables when estimating sustainable output growth. For this purpose, we specify a state-space model representing a multivariate HP filter that links cyclical fluctuation in GDP with several indicators of macroeconomic imbalances. We obtain the parameterizetion of the model by estimating it over a cross-section of emerging market economies. We show that the trend output growth rates estimated by using this model are more stable than those obtained with a univariate version of the filter and thus are more consistent with the notion of sustainable output.

Suggested Citation

  • Anna Krupkina & Elena Deryugina & Alexey Ponomarenko, 2015. "Estimating Sustainable Output Growth in Emerging Market Economies," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 57(1), pages 168-182, March.
  • Handle: RePEc:pal:compes:v:57:y:2015:i:1:p:168-182

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    Cited by:

    1. Grintzalis, Ioannis & Lodge, David & Manu, Ana-Simona, 2017. "The implications of global and domestic credit cycles for emerging market economies: measures of finance-adjusted output gaps," Working Paper Series 2034, European Central Bank.

    More about this item

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models


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