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Ölkə iqtisadiyyatı üzrə göstəricilərin modelləşdirilməsi və proqnozlaşdırılması: problemlər və praktiki çətinliklər
[Modeling and forecasting of macroeconomic variables of the national economy: problems and practical issues]

Author

Listed:
  • Mehdiyev, Mehdi
  • Ahmadov, Vugar
  • Huseynov, Salman
  • Mammadov, Fuad

Abstract

In this paper, we study main problems and practical issues of modeling and forecasting of macroeconomic variables in the national economy. For that, we employ astructural VAR models and estimate interdependencies among different economic variables. Initial data analysis of macroeconomic variables incorporated into the model show that there exist certain anomalies and high volatility in their dynamics. Only that fact alone can be considered among important factors which strongly contribute to weak interdependencies among variables. It is not surprising that researchers using different macroeconomic variables on the national economy cannot find statistically significant relations among them. Here, we use different statistical methods to reduce volatilities and anomalies present in their dynamics. We estimate VAR models with different number of variables and employ two forecasting methodologies (Waggoner and Zha (1999), Banbura, Giannone and Lenza (2014)) to construct our conditional and unconditional forecasts on several variables. In the end, we demonstrate practical problems of modeling and forecasting of macroeconomic variables and provide some policy recommendations to improve quality of statistical data on the national economy.

Suggested Citation

  • Mehdiyev, Mehdi & Ahmadov, Vugar & Huseynov, Salman & Mammadov, Fuad, 2015. "Ölkə iqtisadiyyatı üzrə göstəricilərin modelləşdirilməsi və proqnozlaşdırılması: problemlər və praktiki çətinliklər [Modeling and forecasting of macroeconomic variables of the national economy: pro," MPRA Paper 63517, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:63517
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    References listed on IDEAS

    as
    1. Helmut Lütkepohl, 2005. "New Introduction to Multiple Time Series Analysis," Springer Books, Springer, number 978-3-540-27752-1, April.
    2. Daniel F. Waggoner & Tao Zha, 1999. "Conditional Forecasts In Dynamic Multivariate Models," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 639-651, November.
    3. Günter Coenen, 2005. "Asymptotic confidence bands for the estimated autocovariance and autocorrelation functions of vector autoregressive models," Empirical Economics, Springer, vol. 30(1), pages 65-75, January.
    4. Bańbura, Marta & Giannone, Domenico & Lenza, Michele, 2015. "Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections," International Journal of Forecasting, Elsevier, vol. 31(3), pages 739-756.
    5. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    6. Bénédicte Vidaillet & V. d'Estaintot & P. Abécassis, 2005. "Introduction," Post-Print hal-00287137, HAL.
    7. Huseynov, Salman & Ahmadov, Vugar & Adigozalov, Shaig, 2014. "Beating a Random Walk: “Hard Times” for Forecasting Inflation in Post-Oil Boom Years?," MPRA Paper 63515, University Library of Munich, Germany.
    8. Salman Huseynov & Vugar Ahmadov, 2013. "Oil Windfalls, Fiscal Policy and Money Market Disequilibrium," William Davidson Institute Working Papers Series wp1051, William Davidson Institute at the University of Michigan.
    9. Marta Bańbura, 2008. "Large Bayesian VARs," 2008 Meeting Papers 334, Society for Economic Dynamics.
    10. Jaromír Beneš & Andrew Binning & Kirdan Lees, 2008. "Incorporating judgement with DSGE models," Reserve Bank of New Zealand Discussion Paper Series DP2008/10, Reserve Bank of New Zealand.
    11. Lutz Kilian, 1998. "Small-Sample Confidence Intervals For Impulse Response Functions," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 218-230, May.
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    More about this item

    Keywords

    Forecasting; Time series methods; Bayesian methods;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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