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Armine Khachatryan is an Advisor to the Executive Director for the constituency of countries including Armenia, the World Bank; Grigor Sargsyan was a graduate student at the University of Michigan at the time this paper was written. The authors would like to thank Christian Beddies, John Matovu, two anonymous referees, and AIPRG conference participants at the World Bank for their comments, and Karina Totah for editorial assistance. Remaining errors are their own. This paper is forthcoming in the Armenian Journal of Public Policy.
See Grigorian (2003) for an overview of problems facing the sector. The results of factor analysis of the interest rate presented in Poghosyan and Khachatryan (2001) suggest that a large portion of the interest rate spread is due to a high level of nonperforming loans, followed by excessive overheads.
An appendix to this paper contains a chronology of key events in the monetary and financial sectors in Armenia.
Throughout recent years, the CBA has made a number of analytical and practical attempts to develop more sophisticated policy channels. As more data become available with the years, the CBA has tried to refine its estimates of the transmission mechanism and the impact of monetary policy on aggregate demand and prices. Since 1996, the CBA expanded its own financial programming framework, which has significantly improved the quality of estimates. Since 1997, the policy implementation has been widely tested through the practical implications of monetary impulse functions. With signs of development in the secondary market for treasury bills and interbank markets, the CBA started looking into the possibility of estimating monetary policy impact through the use of the Monetary Condition Index (Poghosyan, Mkrtchyan, and Khachatryan, 2001).
This “neutrality” feature of money holds with the exception of cases where excessive money growth can lead to external imbalances (such as capital flights and sharp deteriorations in the current account) and, therefore, have redistributive effects on domestic agents.
For general transition cases see, for example, Calvo and Reinhart (2000), Kuijs (2002), and Billmeier and Bonato (2002). For studies on Armenia, see Vardanyan, Mkrtchyan, and Khachatryan (1998), Poghosyan, Mkrtchyan, and Khachatryan (2001), and Poghosyan and Khachatryan (2001).
To the extent that prices on dram-denominated assets yield higher returns than foreign currency-denominated holdings of the CBA, the latter bears a cost of having to “sterilize” the capital inflows described above. These costs can be substantial given the interest rate differential between the dram- and foreign currency-denominated assets.
This exercise was accompanied by the introduction of the monetary base corridor, which was aimed at smoothing the fluctuations in the operational target and increasing the efficiency of its impact on price management. Should the monetary base leave the corridor in either direction, the CBA would intervene in money and foreign exchange markets to bring the monetary base back into the band.
It is important to note at this stage the data limitations in Armenian (and more generally, in transition economies’) context. These are largely due to less-than-perfect methodologies of data collection and aggregation, and structural changes that took place within the decade of transition. Even though in recent years the quality of monetary statistics in Armenia has improved dramatically, there remain considerable problems in the external and real sector coverage. Although Armenia recently joined the IMF’s Special Data Dissemination Standard (SDDS), the timeliness, internal consistency, width of coverage, and periodicity of data requires additional efforts to reach developed country data quality standards.
The series for terms of trade includes actual data compiled by the CBA for January 1996 to December 1999 (series is discontinued) and an inverted price-of-oil index thereafter. Using the price of oil as a proxy for the inverse of the terms of trade reflects Armenia’s heavy reliance on oil imports. The results do not change significantly when the inverse of the oil price index is used to proxy terms of trade for the entire sample period, i.e., January 1996-June 2003.
In the absence of reliable measures of labor and capital stock in Armenia, the paper uses the deviation from output trend (calculated by a Hodrick-Prescott filter) as a proxy for deviations from the potential level and/or (short-run) productivity movements.
Here, because the own interest rate of money is virtually zero (since demand deposits do not generally earn any interest in Armenia), the net opportunity cost of holding cash would be equal to the yield of the alternative assets, that is, time deposits. Using the treasury bill rate as a measure of yield for alternative assets leads to qualitatively similar results. Due to the limited access of the general population to treasury bill market, time deposits are viewed as a better alternative to holding money than treasury bills.
Unofficial estimates place the unemployment rate in Armenia well in excess of 20 percent of the labor force.
As this dummy variable proved insignificant, it is dropped from subsequent discussions.
After estimating the model, we tested whether the residuals normally distributed. We ran a Jarque-Bera (JB) normality test, which provided the comfort level necessary to proceed with the discussion of the findings. The results of the test are reported below.
|Short-Run Eq. of Interest||JB-stat value||p-value|
|Short-Run Eq. of Interest||JB-stat value||p-value|
The residuals in all four equations of interest appear to be normally distributed at 5 percent confidence level. (Note, that the null hypothesis in this case that residuals are normally distributed, so the higher p-value indicates the normality of residuals).
Granger causality, which tests for a specific relationship between variables and their lags, is stronger than weak exogeneity.
This was suggested by one of the referees. Estimates are available from the authors upon request.