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DSGE Models: Problem of Trends

Author

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  • Sergey M. Ivashchenko

    (Institute of Regional Economy Problems of the Russian Academy of Sciences, St Petersburg 190013, Russia; Financial Research Institute, Moscow 127006, Russia; St Petersburg University, St Petersburg 199034, Russia)

Abstract

There are trends (deterministic and stochastic) in the most macroeconomic time series. Dynamic Stochastic General Equilibrium (DSGE) models have to take into account these data features. Data detrending is one of the popular approaches that imply exogenous (to the model) decomposition of time series into cycle and trend components, and dropping of the last one. The aim of the paper is to analyze the consequences of such approach. This paper shows that the methods described above distort the model, save some specific conditions. If one of the following conditions remains, then detrending disturbs the model unsystematically. Trend is eliminated from each time series separately. One variable has different nonlinear transformation than the other (example: one variable is in-logs while the other in-levels). Correlation of trend divergence (i.e. difference between trends of one and another variable) with exogenous shocks is incorrect (correct correlation can be nonzero). If trends are dropped from the model, then detrending distorts the model systematically. The author presents numerical results of detrending analysis and creates DSGE model. Then the model was estimated on multiple arrays of simulated data with different detrending schemes including the absence of detrending. Data detrending leads to 1.5–3 times higher errors of parameters estimation. More flexible detrending scheme leads to worse results (HP filter produces the worst result). However, if trend is eliminated from the data and DSGE model without trend is used then estimation errors increases additionally by 4–10 times.

Suggested Citation

  • Sergey M. Ivashchenko, 2019. "DSGE Models: Problem of Trends," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 127006, Russia, issue 2, pages 81-95, April.
  • Handle: RePEc:fru:finjrn:190206:p:81-95
    DOI: 10.31107/2075-1990-2019-2-81-95
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    Cited by:

    1. Ivashchenko, S., 2020. "Long-term growth sources for sectors of Russian economy," Journal of the New Economic Association, New Economic Association, vol. 48(4), pages 86-112.

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    More about this item

    Keywords

    DSGE; trend; detrending; HP-filter; estimation accuracy; RMSE;
    All these keywords.

    JEL classification:

    • 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
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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