The Interest Rate Channel

Many developments prior to the crisis point toward a strengthening of the interest rate channel. First, in January 2006, the CBA announced a move to an inflation targeting framework. The policy rate was chosen as the operational target, with an interest rate corridor around the target based on the overnight deposit and Lombard facilities. Previously, the CBA targeted monetary aggregates, guided by liquidity forecasts. This, however, became untenable as the CBA struggled with sterilizing liquidity injections from foreign exchange intervention to slow the pace of appreciation brought about by rising remittances and capital inflows. As a result, the CBA frequently missed its target. In addition, favorable macroeconomic conditions and more confidence in the domestic currency increased the demand for money. This made it difficult to pin down the relationship between money growth and inflation. Under the inflation targeting regime, the CBA has aimed to keep short-term interest rates close to the announced policy rate and signal the monetary stance. One of the foreign banks has even started offering loans with floating interest rates linked to the central bank’s policy rate. Evidence from other countries also shows that inflation targeting and the greater use of interest rate instruments have strengthened the interest rate channel, as seen in Leiderman, Maino, and Parrado (2006) for Peru and Bolivia.

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Excess Reserves

(in mln drams)

Citation: IMF Working Papers 2010, 270; 10.5089/9781455210770.001.A001

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Second, hand in hand with adopting inflation targeting, the CBA moved to mop up the excess liquidity that had previously weakened the monetary transmission mechanism through sale of its own securities. With structural liquidity then in excess, the CBA’s operations did not have much traction on banks’ marginal cost of funding. Banks had very little need to actively manage their liquidity positions and access the secondary market, which therefore remained shallow. With a shallow money market, the link from the policy rate to short-term and long-term market interest rates is impaired. Data show that the CBA was somewhat successful and excess liquidity dropped in 2007 and turnover in the secondary market increased from an average of 437 million drams in 2005 to 1.5 billion drams in 2009. Short-term money market rates appear to broadly track the policy rate.

Third, the government and the central bank made efforts to develop the market for local securities. The treasury increased its issuance of government securities, moving toward the establishment of benchmark rates for a longer yield curve. In addition, the CBA reached an agreement with the Ministry of Finance (MoF) to restrict repo transactions to the shorter end of the yield curve. Previously, the CBA used its own 6-month to 1-year securities to mop up excess structural liquidity, as the government was reluctant to offer its own treasury bills in excess of the needed budgetary financing. Splitting the yield curve between the MoF and CBA has helped avoid confusion in the market on monetary policy operations. This is especially true when the MoF occasionally applies cut-off rates in auctions, creating differences in the yields of securities with the same maturity. With market rates not prevailing in some parts of the yield curve, the interest rate channel is distorted. Currently, the CBA issues repos of up to 12 weeks maturity only and the MoF issues securities at 13 weeks maturity and longer.

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Daily Interbank Market Volume

(mln AMD)

Citation: IMF Working Papers 2010, 270; 10.5089/9781455210770.001.A001

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Fourth, the appreciating trend of the dram and the period of macroeconomic stability increased confidence in the domestic currency, fostering dedollarization. The ratio of foreign currency deposits to total deposits dropped from a peak of more than 80 percent in 2001 to a low of less than 40 percent in 2008. Domestic currency loans also increased as a proportion of total loans. Previously, changes in domestic currency interest rates had very little impact on activity as many agents borrowed and saved in foreign currency. Dedollarization is expected to increase the link between interest rates and economic activity, strengthening the interest rate channel.

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Dollarization

(In percent)

Citation: IMF Working Papers 2010, 270; 10.5089/9781455210770.001.A001

Source: Armenian authorities.

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More recent developments during and after the crisis point toward a weakening of the interest rate channel. First, the transition to full-fledged inflation targeting was sidetracked, as the credibility of the framework may have weakened, as a result of the crisis. The framework, still in its infancy when the crisis struck, is best described as inflation targeting lite (Stone, 2003), given its relatively low credibility owing to the country’s shallow financial system and vulnerability to economic shocks. However, when inflation targeting was announced in 2006, the economic environment bolstered the credibility of the framework: low inflation, high growth, strong external inflows, a strong fiscal position, low debt, and a trend toward dedollarization. All these were reversed during the crisis. The large and rapid decline in current and capital inflows put enormous pressure on the exchange rate. A nominal depreciation of more than 20 percent in March 2009 increased inflation above the target band, and the significant decline in output, a deterioration of the fiscal position, and an increase in government debt complicated monetary policy, by placing pressures on the multiple objectives of the central bank.

Second, meeting these multiple objectives presented challenges to liquidity management. The central bank injected liquidity to stem the slowdown in credit growth. Banks’ lending, however, remained cautious during the crisis, given the contracting economy, rising non-performing loans, and pervasive uncertainty. The resulting excess liquidity contributed to occasional turmoil in the foreign currency market, forcing the central bank to sometimes tighten liquidity conditions. This resulted in high short-term interest rate volatility, as market rates either dropped to the overnight deposit rates or rose to the overnight lending rates.

Third, the trend toward dedollarization reversed. An expectation of depreciation associated with the crisis triggered a move from dram to dollar deposits. The ratio of foreign currency deposits to total deposits increased to 67 percent by the time of the March 2009 depreciation. To close their positions, banks also increased dollar-denominated lending. Loan dollarization has risen to around 55 percent from a pre-crisis low of 39 percent.

The Credit Channel

The same factors that strengthened the interest rate channel may also have boosted the credit channel. Under inflation targeting, the central bank’s monetary stance should become more transparent and easier to understand. The policy rate drives monetary operations that influence the supply of funds. Lower excess liquidity also gave the CBA more control of the supply of funds, as banks become more responsive to the policy rate. Dedollarization increased the supply of dram funding, as the population is more willing to hold dram assets. In addition, the CBA made progress in strengthening its banking sector regulation and supervision, contributing to raising financial intermediation. The public’s trust in banks appears to have improved markedly, as reflected in the steady increase in deposits even during the crisis. The total loan portfolio of the banking system also did not contract. With more economic activity transacted through banks, the impact of monetary policy through the credit channel should strengthen. However, there are still weaknesses in the institutional infrastructure that inhibit lending, including in the areas of creditor protection and corporate governance. Moreover, many enterprises and individuals continue to rely on remittances or capital inflows as alternative sources of financing and/or remain in the shadow economy.

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Foreign currency depostits to total deposits, 2009

Citation: IMF Working Papers 2010, 270; 10.5089/9781455210770.001.A001

Source: Staff estimates and Database used in Eduardo Levy Yeyati, Financial Dollarization: Evaluating the Consequences, Econonmic Policy, January 2006, pp. 61-118 updated August 2010*Central and Eastern Europe**Caucasus and Central Asia

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The Exchange Rate Channel

The impact of recent developments on the exchange rate channel is less obvious. The period between 2006 and the global financial crisis was characterized by dedollarization and the move to inflation targeting, both of which are expected to weaken the exchange rate channel. With lower dollar holdings, the public is less impacted by changes in the exchange rate. In addition, the experience of Peru and Bolivia shows that the adoption of inflation targeting should also have reduced the exchange rate pass through by anchoring inflation expectations on the target (Armas and Grippa, 2005). However, the pre-crisis period was also characterized by sizable interventions by the central bank, as it initially leaned heavily against appreciation pressures and eventually resisted against depreciation pressures when the trend reversed. This may have increased the significance of the exchange rate among its instruments and enhanced the effectiveness of the exchange rate channel. In addition, to the extent that the dollarization level even at its lowest point was still relatively high and the episode of relatively low dollarization was not long enough to overcome the public’s sensitivity to the exchange rate, the exchange rate channel has likely remained strong.

Meanwhile, the period during the crisis up to the present has been characterized by increased dollarization and reduced interventions.

While previous work concluded that the monetary transmission mechanism in Armenia is weak, the recent developments described above and summarized in Tables 1 and 2 suggest that updating the analysis would be important to not only check if the old conclusions still hold but also to test if the nature of the different channels have changed and, if so, when. The next section describes the approaches that we will take to answer these questions.

Table 1.

Effects of Recent Pre-crisis Developments on the MTM

Table 1.

Effects of Recent Pre-crisis Developments on the MTM

	Interest rate channel	Credit channel	Exchange rate channel
Move to inflation targeting	↑	↑	↓
Better liquidity management	↑	↑
Development of local currency market	↑	↑
Dedollarization	↑	↑	↓
Stronger banking sector regulation and supervision	↑	↑
Increased foreign currency intervention			↑

View Table

Table 1.

Effects of Recent Pre-crisis Developments on the MTM

	Interest rate channel	Credit channel	Exchange rate channel
Move to inflation targeting	↑	↑	↓
Better liquidity management	↑	↑
Development of local currency market	↑	↑
Dedollarization	↑	↑	↓
Stronger banking sector regulation and supervision	↑	↑
Increased foreign currency intervention			↑

Table 2.

Effects of Recent Crisis and Post-crisis Developments on the MTM

Table 2.

Effects of Recent Crisis and Post-crisis Developments on the MTM

	Interest rate channel	Credit channel	Exchange rate channel
Slow transition to full- fledged inflation targeting	↓	↓	↑
Less consistent liquidity management	↓	↓
Continued development of local currency market	↑	↑
Redollarization	↓	↓	↑
Stronger banking sector regulation and supervision	↑	↑
Reduced foreign currency intervention			↓

View Table

Table 2.

Effects of Recent Crisis and Post-crisis Developments on the MTM

	Interest rate channel	Credit channel	Exchange rate channel
Slow transition to full- fledged inflation targeting	↓	↓	↑
Less consistent liquidity management	↓	↓
Continued development of local currency market	↑	↑
Redollarization	↓	↓	↑
Stronger banking sector regulation and supervision	↑	↑
Reduced foreign currency intervention			↓

Methodology and Data

The baseline unrestricted VAR specification can be written in matrix form as

where Y_t is the vector of endogenous variables, Z_t is the vector of exogenous variables and ε_t is the vector of serially uncorrelated disturbances that have a zero mean and a time invariant covariance matrix. The coefficient matrices are denoted as A and B whereas L denotes the lag operator.

The vector of endogenous variables consists of output as measured by real GDP (y_t), the consumer price index (p_t), the repo rate (s_t) which is the key short-term interest rate used by the CBA to signal its monetary stance, domestic narrow money or M1 (m_t) and the nominal effective exchange rate or NEER (x_t):

Thus, we follow Dabla-Norris and Floerkemeier (2006) and order output before prices on the assumption that it adjusts more sluggishly². Furthermore, the money supply is ordered before the exchange rate. This reflects the underlying assumption that the nominal effective exchange rate is immediately affected by all type of shocks, whereas the money supply is affected by shocks to monetary policy, output and prices and the interest rate in the short term responds contemporaneously to shocks in output and prices, but not to changes in financial variables.

The vector of exogenous variables contains an index of world oil prices (oilp_t), and the U.S. Federal Funds Rate $\left(s_t^{US}\right)$. As in Dabla-Norris and Floerkemeier (2006), the world oil price index is included as a proxy for the development of remittances since it is highly correlated with gas prices and economic growth in Russia, the main source of remittances to Armenia. The U.S. Federal Funds Rate is included to capture interest rate parity. Thus:

The endogenous and exogenous variables are all seasonally adjusted and are expressed in natural logarithms with the exception of the repo rate and U.S. Federal Funds Rate, which are in levels and not seasonally adjusted. We use data for the period of 2000M1-2010M5. A detailed overview of data sources is provided in Table A.1 in the Appendix.

Standard information criteria are used to determine the lag length of the VAR. Following Weber et al. (2009), we use the Hannan-Quinn (HQ) and Schwartz Criterion (SC). The Akaike criterion is disregarded since it overestimates the order with some probability as shown by Luetkepohl (2005). We follow a number of papers (e.g. Weber et al. 2009, Al-Mashat and Billmeier, 2008 and Dabla-Norris and Floerkemeier, 2006) and estimate the VAR in levels, although the null hypothesis that some of the variables are integrated of order one and therefore follow a unit root process cannot be rejected. We do this for two reasons. First, we would like to be able to compare our results to the earlier findings on the MTM in Armenia by Dabla-Norris and Floerkemeier (2006). Second, while imposing cointegration restrictions improves the efficiency of the estimation, given the short data series, it may result in inconsistencies. In addition, as demonstrated by Sims, Stock and Watson (1990) even if the system includes non-stationary variables, the OLS estimators are still consistent when the model is estimated in levels.

Results

Table 3 presents the results of the bivariate and multivariate Granger causality tests as preliminary evidence of the causal links between the repo rate and real economic activity and inflation. The results suggest that the repo rate and domestic narrow money both have a significant effect on output while only the NEER has a significant effect on inflation.³

Table 3.

Multivariate and Bivariate Block Granger Causality Tests

Note: *, **, and *** denote rejection of the null hypothesis at the 10, 5, and 1 percent significance levels, respectively.

Table 3.

Multivariate and Bivariate Block Granger Causality Tests

	p-values
Effect on output
Interest Rate (s)	0.00***
Money Supply (m)	0.00***
Exchange Rate (x)	0.05**
Block (p, s, x, m)	0.00***
Effect on prices
Interest Rate (s)	0.96
Money Supply (m)	0.15
Exchange Rate (x)	0.03**
Block (p, s, x, m)	0.14
Effect on NEER
Interest Rate (s)	0.03**
Money Supply (m)	0.06*
Block (p, s, x, m)	0.02**

Note: *, **, and *** denote rejection of the null hypothesis at the 10, 5, and 1 percent significance levels, respectively.

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Table 3.

Multivariate and Bivariate Block Granger Causality Tests

	p-values
Effect on output
Interest Rate (s)	0.00***
Money Supply (m)	0.00***
Exchange Rate (x)	0.05**
Block (p, s, x, m)	0.00***
Effect on prices
Interest Rate (s)	0.96
Money Supply (m)	0.15
Exchange Rate (x)	0.03**
Block (p, s, x, m)	0.14
Effect on NEER
Interest Rate (s)	0.03**
Money Supply (m)	0.06*
Block (p, s, x, m)	0.02**

Note: *, **, and *** denote rejection of the null hypothesis at the 10, 5, and 1 percent significance levels, respectively.

Evidence from the Granger causality tests thus points to a very weak transmission mechanism. The link between policy rates and inflation is indirect in that the repo rate can influence the exchange rate, which in turns significantly affects inflation. The standard VAR is then estimated and the impulse response functions for interest and money supply shocks to output and prices are depicted in Figure 2.⁴

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Impulse Responses for the Standard VAR Model

(Response to One S.D. Innovations ±2 S.E.)

Citation: IMF Working Papers 2010, 270; 10.5089/9781455210770.001.A001

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The results of the VAR model confirm the results from the Granger causality tests. Both interest and money shocks have persistent effects on output but small and insignificant effects on prices. These results are also in line with the findings by Dabla-Norris and Floerkemeier (2006) who use a shorter sample period with data only up to the end of 2005. We also confirm the findings of a study on inflation targeting in Armenia and Georgia by Dabla-Norris et al. (2007), which shows that the exchange rate pass-through to domestic prices is rapid and statistically significant in Armenia. However, in contrast to our findings and those of Dabla-Norris and Floerkemeier (2006), Dabla-Norris et al. (2007) find the interest rate channel to be effective in influencing prices. The difference in results is likely due to the fact that Dabla-Norris et al. (2007) estimate a slightly different VAR specification with only the U.S. Federal funds rate as an exogenous variable and currency in circulation as an additional endogenous variable. They also use a different sample period: January 2001-December 2005.

The different results across these studies highlight a significant shortcoming of the simple VAR analysis. Armenia is a country that has undergone significant structural changes in the past years. Most significantly, there was the introduction of inflation targeting in 2006 and decline of dollarization levels between 2006 and 2008. Therefore, it is likely that the transmission mechanism has changed and that the parameters that are estimated in the standard VAR have not remained constant over time. This is a likely explanation of why results differ between VAR estimations with different sample periods. The next section will estimate a regime-switching VAR, without any prior assumption as to when the transmission mechanism in Armenia is likely to have undergone significant changes.

Methodology

The aim of this section is to examine whether the effects of monetary policy on the real economy have been subject to a structural break without making prior assumptions about its timing. We therefore employ a technique known as Markov Switching Vector Autoregression (MSVAR). This method allows us to model structural breaks as Markov regime switches and to compare the monetary transmission mechanism before and after a regime switch.

We examine the most general MSVAR model in which K endogenous variables are explained by a K dimensional intercept vector, v, autoregressive terms of order l, a residual vector of normally distributed fundamental disturbances uncorrelated at all leads and lags, U_t, which is pre-multiplied by a regime-dependent matrix A. In this specification all parameters may switch between m regimes:

where r_t = 1, …,m

and U_t ~ N (0,I_k).

According to the above specification, the variance of each fundamental disturbance is normalized to unity. However, because the residuals are pre-multiplied by a regime dependent matrix, the variance-covariance matrix of the residuals A(r_t)U_t is also regime dependent:

We assume that the regime r_t follows a hidden m-state Markov chain and that the probability of regime i occurring next period given that the current regime j is exogenous and constant. Since this is a regime switching model, the number of regimes needs to be fixed before the estimation. Given the short sample period, we fix this number to two regimes. The transition matrix can then be expressed as follows:

where

The elements of the matrix R show the probability of being in regime 1 in the next period given that the current regime is regime 1, that is r_11, as well as the probability of being in regime 2 in the next period given that the current regime is regime 1, r₁₂, and vice versa if the current regime is regime 2.

As shown by Hamilton (1990) all parameters of the above hidden Markov chain can be estimated jointly by applying the Expectations-Maximization (EM) algorithm. Under the EM algorithm, the hidden Markov chain is first inferred for a given set of parameters and subsequently the parameters for this hidden Markov chain are estimated in the maximization step. This procedure is repeated until convergence. The application of this algorithm allows us to find estimates of the coefficients and variance-covariance matrix for each regime as well as the transition matrix and an optimal inference of the hidden Markov chain, that is the probability that the current regime is i=1,2 for t=1,…, T.

The endogenous variables used for the estimation of the MSVAR model are real GDP (y_t), the consumer price index (p_t), the repo rate (s_t), M2 (m_t) and NEER (x_t) and the sample period continues to be 2006M1-2010M5. Again, the optimal number of lags is determined using the Hannan-Quinn (HQ) and Schwartz Criterion (SC). Following Fujiwara (2006) the model is estimated in levels and the Choleski decomposition is used to identify the system for contemporaneous relationships between macroeconomic variables.

The model is estimated in Oxmetrics using the MSVAR package, which was developed by Krolzig (1998). In general, impulse response functions can be derived within this MSVAR framework as shown by Krolzig and Toro (1999) and Ehrmann et al (2003), who derive regime dependent impulse response functions. However, the code provided by Ehrmann et al. (2003) currently does not allow models to have more than four endogenous variables. We therefore provide coefficients of the VAR model under the two regimes, which also facilitates an analysis of how the transmission mechanism has changed.

Results

Table 4 shows the estimation results for the five-variable MSVAR estimated in this paper. It can be seen that the coefficients differ between regimes, and that according to the transition matrix each regime is highly persistent. Furthermore, according to the linearity test statistic, the model is highly non-linear and coefficients switch significantly between regimes, which suggests that the MSVAR is an appropriate method to estimate the model. Diagnostic tests furthermore confirm that the errors can be considered normally and independently distributed as shown in Figures A2-A4.

(4) Linearity test
LR linearity test: 388.5780	Chi(60) =[0.0000] **		Chi(62)=[0.0000] **	DAVIES=[0.0000]**

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(4) Linearity test
LR linearity test: 388.5780	Chi(60) =[0.0000] **		Chi(62)=[0.0000] **	DAVIES=[0.0000]**

An important advantage of the MSVAR model is that it estimates the timing of the regime switch without any prior assumptions on when this switch is taking place. Figure 3 shows smoothed regime probabilities, which suggest that there was a regime switch in mid-2006. The results in Table 4 also show that the transmission mechanism was strengthened after the regime switch in 2006. In regime 2, the repo rate has a significant effect on inflation and the exchange rate, which indirectly significantly affects inflation. On the contrary, in regime 1, a change in the repo rate or exchange rate causes no significant effect on inflation or output. Therefore, these results point to a less effective monetary policy in regime 1.

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Regime Probabilities for MSVAR(2).

Citation: IMF Working Papers 2010, 270; 10.5089/9781455210770.001.A001

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Given the timing of the regime switch, it is likely that several factors -- the introduction of inflation targeting, reduced levels of dollarization, and the several measures to develop the market for dram instruments and improve liquidity management -- played a role. However, the model is unable to tell us why a regime switch took place; it merely estimates its most likely timing. The next section will therefore introduce dollarization in the model, which is one of the underlying factors explaining the regime switch in the Armenian MTM.

Methodology and Data

The analysis in the previous section suggests that there was a regime switch in mid-2006. Several factors can explain this regime switch, including the shift to inflation targeting, the improvement in the central bank’s liquidity management, as well as the dedollarization process occurring in this period. In this section, we will consider one of these factors, namely the level of dollarization, and formally evaluate whether it affects the strength of the MTM and therefore explains the regime switch in mid-2006. If it does have an impact, we will then analyze its effect on the MTM by looking at the response of the economy to an exogenous monetary policy shock within a structural VAR framework.

As in the previous section, we consider an MSVAR with 2 regimes, with the regimes determined by the level of dollarization:

where

and

The coefficients and covariance matrix are all regime-dependent and d_t is the threshold variable whose value relative to the threshold D determines the regime. The threshold variable, which in this paper is the level of dollarization, is assumed to be exogenous. This means that shocks to the system do not affect the level of dollarization and, thus, the prevailing regime. The assumption is restrictive, given that many shocks that affect the endogenous variables also affect the level of dollarization. In addition, the relationship between dollarization and the MTM does not just go in one direction. While it is accepted that dollarization weakens the MTM, Ize and Yeyati (2005) also suggest that low credibility of the monetary authorities, which weakens the MTM, can create conditions – specifically, high inflation – such that the optimal portfolio of the public leans toward a higher share of foreign currency assets. Dollarization in this case is endogenous.

The assumption therefore does not allow us to analyze the complete response of the economy to changes in monetary policy. However, it does allow us to estimate regime-dependent coefficients and impulse response functions. Since the purpose of the paper is not to analyze the overall effect but to look closely at differences in the MTM across the two regimes, regime-dependent impulse responses will satisfy our purpose.

We use the same set and ordering of endogenous variables as in the previous section. The variables are transformed in logs and then in first differences to ensure stationarity. The ratio of foreign currency loans to total loans is used to measure the level of dollarization. In Armenia, this measure tends to lag behind other measures such as the ratio of foreign currency deposits to total deposits since banks cannot immediately react to shifts in the composition of liabilities. However, it captures a more complete shift in the regime since loan dollarization must be preceded by deposit dollarization.

Results

The software MSVAR by Krolzig (1998) is used, which estimates the critical value, D, that maximizes the conditional log-likelihood of the model. Estimation results are presented in Table 5. The threshold level is 51 percent. This divides the sample into two regimes. Periods where the ratio of foreign currency loans to total loans is below 51 percent belong to the low dollarization regime and the rest to the high dollarization regime. The estimated critical value for the threshold suggests that the Armenian economy moved from a high dollarization regime in late 2006, stayed in a low dollarization regime until mid-2009, and moved back to a high dollarization afterwards. According to these results, the economy is currently in a high dollarization regime. This result is broadly consistent with the results in the MSVAR in the previous section which showed a similar regime switch in 2006. However, the second regime switch shows up only in this approach. This can be explained by other factors in addition to dedollarization that have affected the nature of the MTM. Another reason could be that the time series after the second regime switch in the MSVAR with threshold variables (11 months) is too short to identify another regime switch when applying the MSVAR in the previous section.

Table 5.

Estimation Results for Threshold MSVAR

Table 5.

Estimation Results for Threshold MSVAR

Estimated threshold	51
Period of high dollarization	2000:3 – 2006:11 2009:7 – 2010:5
Period of low dollarization	2006:12 – 2009:6
Log likelihood	1238.35
LR Linearity test	329.84
p-values (adjusted chi-squared)	[0.0000]

View Table

Table 5.

Estimation Results for Threshold MSVAR

Estimated threshold	51
Period of high dollarization	2000:3 – 2006:11 2009:7 – 2010:5
Period of low dollarization	2006:12 – 2009:6
Log likelihood	1238.35
LR Linearity test	329.84
p-values (adjusted chi-squared)	[0.0000]

To test the null hypothesis of the linear model explaining the data better than the nonlinear threshold VAR model, the likelihood ratio between the two models was computed. The p-values suggest that the threshold VAR model explains the data better. Figure 4 also presents the smoothed regime probabilities.

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Regime Probabilities for Threshold MSVAR

Citation: IMF Working Papers 2010, 270; 10.5089/9781455210770.001.A001

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We then proceed to analyze the difference between the two regimes. To do so, we compute regime-dependent impulse response functions and plot the response of growth and inflation to a one unit shock to the repo rate, money, and the nominal effective exchange rate. Since we assume that the threshold variable is exogenous, we implicitly assume that shocks to the system do not change the regime. This allows us to divide the sample between the two regimes, run a reduced form VAR on each regime, and perform a Cholesky decomposition to compute the structural parameters for each regime.

Figures 5 to 7 present the regime-dependent impulse response functions with 95 percent standard error bands. Figure 5 shows the impact of an increase in the repo rate under the two regimes. The impact of a 100 basis point increase in the repo rate on GDP growth is negative and significant under both low and high dollarization. However, the impact under low dollarization is nearly 10 times stronger. Turning to inflation, the repo rate has a negative impact on inflation. Under low dollarization, the response of inflation to the repo shock is much larger but remains insignificant suggesting that the interest rate channel has strengthened with the drop in dollarization but remains weak.

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Threshold MSVAR: Response to a Repo Shock

Citation: IMF Working Papers 2010, 270; 10.5089/9781455210770.001.A001

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Threshold MSVAR: Response to a Money Shock

Citation: IMF Working Papers 2010, 270; 10.5089/9781455210770.001.A001

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Threshold MSVAR: Response to a NEER Shock

Citation: IMF Working Papers 2010, 270; 10.5089/9781455210770.001.A001

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Figure 6 presents the response of GDP growth and inflation to a 1 percentage point increase in money growth under both regimes. The response of GDP growth to a shock on money has the expected sign and is stronger under the low dollarization regime but does not appear to be significant under both regimes. The overall impact on inflation is significant under both regimes and, again, we find that the impact is stronger under low dollarization. The significant impact of money growth as opposed to the policy rate on inflation during the low dollarization regime attests to the early stages of the CBA’s inflation targeting framework. As mentioned above, despite the announced move, the central bank does not always strictly adhere to the framework, suggesting that the framework is inflation targeting lite. The experience of other emerging market inflation targeters shows an increase in the interest rate pass through immediately after the adoption of inflation targeting (see Armas and Grippa, 2005, for example). In these countries, the volatility of short-term market interest rates was reduced quickly, enhancing the credibility of their policy rate. In contrast, Armenia’s short-term interest rates are volatile and frequently diverge from the policy rate, diminishing the relevance of the latter. The central bank can make more effort to actively manage liquidity and reduce short-term interest rate volatility.

Figure 7 presents the response of GDP growth and inflation to a 1 percentage point increase in the nominal effective exchange rate under both regimes. The exchange rate pass through during the high dollarization period appears to be weaker than in the low dollarization regime in Armenia. This result differs from empirical evidence (for example, Reinhart, Rogoff, and Savastano, 2003) associating high dollarization with higher exchange rate pass through. It is not however unusual. There has been evidence of lower exchange rate pass through in highly dollarized settings during recessions, due to balance sheet effects and the offsetting impact of lower demand (for example, Carranza, Galdon-Sanchez, and Gomez Biscarri, 2004). Depreciation in a dollarized economy could be contractionary, lowering demand, and offsetting the standard effect of the nominal exchange rate on output and prices. This could explain the low pass through in the high dollarization period that started in 2009. In Armenia, the global crisis which started in 2008 and contracted the economy in 2009 came with a significant depreciation in the first quarter of 2009. The subsequent drop in demand kept inflation below 5 percent during the year, despite a 20 percent drop in the value of the local currency and deposit dollarization rising above 65 percent.

Another explanation for the pattern of exchange rate pass through in the periods of high and low dollarization in Armenia could be the increasing use of exchange rate interventions during the low dollarization period. During the low dollarization period, Armenia experienced substantial inflows of remittances and foreign direct investment. As a result, the local currency appreciated and the central bank became a net purchaser of foreign exchange to slow the appreciation. This reversed at the end of 2008, when the global crisis started. Depreciation pressures increased, and the central bank became a net seller. In both episodes, interventions were frequent, contributing to the enhanced role of the exchange rate in managing inflation expectations and thus increasing the pass through in the low dollarization period. Meanwhile, in the high dollarization episodes before 2006 and after the crisis, the annual cumulative interventions were significantly lower. Before 2006, inflows were not substantial and the central bank intervened less. After the large depreciation in 2009, the central bank moved to a flexible exchange rate regime, adopting an intervention strategy that minimized interventions to smoothing excessive volatility and rebuilding reserves.

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Armenia: FX Intervention

Citation: IMF Working Papers 2010, 270; 10.5089/9781455210770.001.A001

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The extent of exchange rate pass through in Armenia is often used to justify frequent interventions as the central bank relies on the exchange rate channel to influence economic activity and inflation. However, higher exchange rate pass through often signifies the lack of monetary policy credibility and many countries with longer experiences in inflation targeting have seen a drop in exchange rate pass through, even with dollarization remaining high. In Armenia, a more consistent and credible monetary and exchange rate policy, in addition to dedollarization, will be necessary to reduce the exchange rate pass through.