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Forecasting Approaches for Forecasting Inflation

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The CPI is very important macroeconomic indicator for the policymaking process. For this reason, in this paper a new forecasting approach was examined for forecasting inflation.

Using monthly data ranging between January 2008 and March 2018, whether the application of the DMA approach is appropriate to forecast the Kyrgyz inflation it has been analyzed. More specifically, I want to observe whether the DMA methods allow for both the evolution of model and time-varying parameters.

The result reveals the following. First, that models and predictors evolved over time. Second, some interesting evidence was found that of the net inflow of remittances and the policy rate of the National Bank performed particularly well in forecasting CPI. Next, it has been found that the DMA and DMS approaches outperform any other comparative models. To be more specific, model selection comparatively forecasts better than model averaging, even though it should be emphasized that the spread of deviation is small enough. Finally, the result of this study especially for the medium and long-term, are consistent with the result of Koop and Korobilis (2012) who found that the model selection approach forecast better than the model averaging.

As Table 1 indicates, DMS with forgetting factor α = λ = 0.95 and their combinations (α = .99 λ = 0.95) and (α = 0.95 λ = 0.99) were the best model to forecast inflation. This implies that the models and determinants changed quite rapidly over time. Depending on time horizon, the best drivers influencing inflation evolved significantly.

In addition, in the future, the models can be replicated with a longer sample period and for longer forecast horizon.

In broad terms, no considerable drawback was found. From the technical part of the study, in my particular case, as I increased the number of independent variables, the computation time increased as well. More powerful computing hardware is required when dealing with Km models.

There are several other areas future researchers can explore. As an independent variable, only two external indicators were used. The models can be improved by adding a few more external variables, such as leading macroeconomic variables of main economic and trade partners (Russia Federation and the Kazakhstan Republic). Moreover, adding new domestic predictors instead of the one proposed in this model could also provide better performance in predicting and analyzing inflation. I leave these extensions for my future research.

In conclusion, these developments could inform an alternative approach for forecasting inflation in the Kyrgyz Republic.

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Forecasting Approaches for Forecasting Inflation. (2019, April 10). GradesFixer. Retrieved November 29, 2021, from
“Forecasting Approaches for Forecasting Inflation.” GradesFixer, 10 Apr. 2019,
Forecasting Approaches for Forecasting Inflation. [online]. Available at: <> [Accessed 29 Nov. 2021].
Forecasting Approaches for Forecasting Inflation [Internet]. GradesFixer. 2019 Apr 10 [cited 2021 Nov 29]. Available from:
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