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Unobserved Components Methods To Estimate Potential Gdp (The Case Of Romania)

Author

Listed:
  • Stanica, Cristian Nicolae

    (Institute for Economic Forecasting, Romanian Academy, Bucharest)

Abstract

The estimation of potential output gap is useful for the identification of a sustainable growth rate without inflationary pressures. In order to derive the potential output decomposition statistical methods and structural relationships estimation methods are used. The former tries to separate a series into a permanent and a cyclical component, and the latter tries to isolate the structural and cyclical influences upon the aggregate output using the economic theory. In order to estimate the potential GDP for the Romanian economy I applied only models with unobserved components.

Suggested Citation

  • Stanica, Cristian Nicolae, 2005. "Unobserved Components Methods To Estimate Potential Gdp (The Case Of Romania)," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 2(4), pages 44-63.
    • Handle: RePEc:rjr:romjef:v:2:y:2005:i:4:p:44-63
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    Citations

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    Cited by:

    1. Moisa Altar & Ciprian Necula & Gabriel Bobeica, 2009. "A Robust Assessment of the Romanian Business Cycle," Advances in Economic and Financial Research - DOFIN Working Paper Series 28, Bucharest University of Economics, Center for Advanced Research in Finance and Banking - CARFIB.
    2. Moisa, Altar & Necula, Ciprian & Bobeica, Gabriel, 2010. "Estimating Potential GDP for the Romanian Economy. An Eclectic Approach," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 5-25, September.

    More about this item

    Keywords

    potential GDP; unobserved components methods; univariate models;
    All these keywords.

    JEL classification:

    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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