Abstract
We estimate a dynamic factor model for the cross section of monetary and price indicators. We extract the common part of the dataset’s fluctuations and decompose it into structural shocks. We argue that one of the shocks identified has empirical properties (in terms of impulse response functions) that are fully in line with the theoretically expected relationship between money growth and inflation, confirming that the process identified has the capacity for economic interpretation. Based on this finding, we decompose recent inflationary developments in Russia into those that are associated with changes in monetary stance and other shorter-lived shocks.
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Notes
In fact considering the relative insignificance of interbank money markets (particularly domestic) in Russia and the high volatility of short-term interest rates, it is doubtful that any money market interest rate or any of the Bank of Russia’s interest rates per se could safely be regarded as a policy rate over the last decade. In fact during that period the monetary policy regime in Russia had undergone a transformation from exchange rate management to more conventional reliance on steering interest rates [see Lainela and Ponomarenko (2012) for review]. This precludes making use of short-term interest for identification of money supply shocks as suggested in Benati (2008) or Chadha et al. (2010).
The money holdings of other financial institutions in Russia are insignificant. We therefore saw no reason to include these as a separate component.
As in Ponomarenko et al. (2014), the foreign currency denominated aggregates were adjusted for a re-evaluation effect caused by exchange rate fluctuations.
Although this indicator is not part of conventional monetary aggregates and could presumably be subject to significant measurement error, it may be regarded as an important monetary instrument in the Russian economy (at least in the early 2000s). The estimate was constructed as in Ponomarenko et al. (2014).
The two principal “classical” testing methodologies for unit roots are the Dicky–Fuller and Phillips–Perron. However, the tests are well known to have low power to reject unit roots. Also, the non-stationarity results with these tests are often an artifact of a short estimation period, which would apply to the data for Russia, and may reflect economic disequilibrium. We therefore apply KPSS unit root test with Bartlett kernel as recommended by Jönsson (2006) for the short time samples.
As pointed out in Forni and Gambetti (2010), a large number of static factors are needed to allow for cross-sectional dynamic heterogeneity among variables (i.e., leading or lagging relationship). We obviously expect to find such a relationship between money and prices and therefore are willing to include many static factors in the model. However, we found that adding more than three static factors leads to non-stationarity of the VAR. As discussed in Section 4.2, this number of static factors allows us to obtain a reasonable share of explained cross-sectional variance.
This lag length choice is supported by both Akaike and Schwartz criteria if two-step estimation method is applied (i.e., the unobserved factors are estimated by principal components and are treated as data). The model seems to be robust in relation to the lag length choice: setting \(L=3\) or \(L=4\) does not change the results significantly.
The fact that this effect predominantly shows up in non-food inflation seems plausible as this category of goods is most sensitive to import price fluctuations.
See, e.g., the Bank of Russia Quarterly Inflation Review (2007 Q4, 2011 Q1 and 2012 Q1) for a more detailed review of these episodes.
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Acknowledgments
We are grateful to anonymous referees, the participants of the joint ECB/Bank of Russia Seminar on Monetary Analysis and would like to thank Haroon Mumtaz from CCBS, Bank of England, for his assistance. Charlotte Dendy provided excellent research assistance, and Maria Brady provided valuable administrative support.
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The views expressed in this paper are those of the authors. They do not necessarily represent the position of the Bank of Russia.
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Deryugina, E., Ponomarenko, A. Money-based underlying inflation measure for Russia: a structural dynamic factor model approach. Empir Econ 53, 441–457 (2017). https://doi.org/10.1007/s00181-016-1125-1
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DOI: https://doi.org/10.1007/s00181-016-1125-1