Browsing by Subject "dynamic factor models"

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  • Amstad, Marlene; Ye, Huan; Ma, Guonan (2018)
    BOFIT Discussion Papers 11/2018
    Inflation in emerging markets is often driven by large, persistent changes in food and energy prices. Core inflation measures that neglect or under-weight volatile CPI subcomponents such as food and energy risk excluding information helpful in assessing current and future inflation trends. This paper develops an underlying inflation gauge (UIG) for China, extracting the persistent part of the common component in a broad dataset of price and non-price variables. Our proposed UIG for China avoids the excess volatility reduction that plagues traditional Chinese core inflation measures. When forecasting headline CPI, the proposed UIG outperforms traditional core inflation measures over a variety of samples.
  • Porshakov, Alexey; Deryugina, Elena; Ponomarenko, Alexey; Sinyakov, Andrey (2015)
    BOFIT Discussion Papers 19/2015
    Published in Zhournal Novoi Ekonomicheskoi Associacii, Volume 2, Issue 30, 2016: 60-76
    Real-time assessment of quarterly GDP growth rates is crucial for evaluation of economy’s current perspectives given the fact that respective data is normally subject to substantial publication delays by national statistical agencies. Large information sets of real-time indicators which could be used to approximate GDP growth rates in the quarter of interest are in practice characterized by unbalanced data, mixed frequencies, systematic data revisions, as well as a more general curse of dimensionality problem. The latter issues could, however, be practically resolved by means of dynamic factor modeling that has recently been recognized as a helpful tool to evaluate current economic conditions by means of higher frequency indicators. Our major results show that the performance of dynamic factor models in predicting Russian GDP dynamics appears to be superior as compared to other common alternative specifications. At the same time, we empirically show that the arrival of new data seems to consistently improve DFM’s predictive accuracy throughout sequential nowcast vintages. We also introduce the analysis of nowcast evolution resulting from the gradual expansion of the dataset of explanatory variables, as well as the framework for estimating contributions of different blocks of predictors into now-casts of Russian GDP.
  • Mäkinen, Mikko (2016)
    BOFIT Policy Brief 4/2016
    Statistical agencies release their preliminary estimates of quarterly GDP growth with a publication delay that ranges from four to eight weeks. Given this lack of timeliness, nowcasting methods have been developed to produce early estimates of GDP growth during the ongoing quarter. As a practical illustration of these methods, I apply several small-scale nowcasting models, including a dynamic factor model, to produce estimates of Russian GDP growth for the first quarter of 2016. I then compare the nowcasting performance of the dynamic factor model against naïve AR- and ADLmodels using pseudo out-of-sample forecasting errors. The results indicate Russia’s GDP contraction slowed in the first quarter of 2016. The dynamic factor model outperforms the naïve models,displaying better nowcasting prediction accuracy for Russian GDP.