Do Unobserved Components Models Forecast Inflation in Russia?

Bulat Gafarov
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Abstract

I apply the model with unobserved components and stochastic volatility (UC-SV) to forecast the Russian consumer price index. I extend the model which was previously suggested as a model for inflation forecasting in the USA to take into account a possible difference in model parameters and seasonal factor. Comparison of the out-of-sample forecasting performance of the linear AR model and the UC-SV model by mean squared error of prediction shows better results for the latter model. Relatively small absolute value of the standard error of the forecasts calculated by the UC-SV model makes it a reasonable candidate for a real time forecasting method for the Russian CPI.
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不可观测成分模型能预测俄罗斯的通货膨胀吗?
本文运用无观测分量和随机波动率(UC-SV)模型对俄罗斯消费者价格指数进行预测。我扩展了之前被建议作为美国通货膨胀预测模型的模型,以考虑模型参数和季节因素可能存在的差异。通过预测均方误差比较线性AR模型和UC-SV模型的样本外预测性能,后者模型的预测效果更好。UC-SV模型计算的预测值的标准误差绝对值相对较小,使其成为俄罗斯CPI实时预测方法的合理候选。
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