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Short-term inflation forecasting models for Turkey and a forecast combination analysis

机译:土耳其的短期通胀预测模型和预测组合分析

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摘要

In this paper, we produce short term forecasts for the inflation in Turkey, using a large number of econometric models. In particular, we employ univariate models, decomposition based approaches (both in frequency and time domain), a Phillips curve motivated time varying parameter model, a suite of VAR and Bayesian VAR models and dynamic factor models. Our findings suggest that the models which incorporate more economic information outperform the benchmark random walk, and the relative performance of forecasts are on average 30% better for the first two quarters ahead. We further combine our forecasts by means of several weighting schemes. Results reveal that, the forecast combination leads to a reduction in forecast error compared to most of the models, although some of the individual models perform alike in certain horizons.
机译:在本文中,我们使用大量计量经济学模型得出了土耳其通货膨胀的短期预测。特别是,我们采用单变量模型,基于分解的方法(在频域和时域均采用),以Phillips曲线为动力的时变参数模型,一组VAR和贝叶斯VAR模型以及动态因子模型。我们的发现表明,包含更多经济信息的模型优于基准随机游走模型,并且在未来的前两个季度中,预测的相对性能平均要好30%。我们通过几种加权方案进一步组合我们的预测。结果表明,与大多数模型相比,预测组合可减少预测误差,尽管某些模型在某些情况下表现相似。

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