Abstract
Selected Issues
Assessing the Monetary Stance in Georgia1
This paper assesses the adequacy of monetary policy stance in Georgia using two approaches. We first construct financial condition indexes (FCIs) for Georgia to explore the links between financial conditions and real economic activity. This is complemented with an estimation of the natural interest rate, which is higher than the policy rate. The analysis suggests slightly loose monetary conditions. However, in the absence of price and wage pressures, the monetary policy stance is considered broadly adequate. The National Bank of Georgia should stand ready to tighten if inflationary pressures emerge. In parallel, credit should be closely monitored to prevent a build-up of financial vulnerabilities.
A. Introduction
1. Financial conditions suggest that the monetary stance is slightly loose, but there is no evidence of price pressures. This paper provides an assessment of the monetary stance in Georgia using two complementary methods: (i) a financial conditions index which provides a historical perspective and allows assessment of relative tightness or looseness of financial conditions; and (ii) natural interest rate for Georgia and comparing it to central banks’ policy rate to assess the stance of monetary policy. In the context of rapid credit growth, the National bank of Georgia (NBG) should continue to monitor inflation and credit dynamics closely to support that the monetary policy stance remains adequate.
2. Financial conditions are found to have predictive power for GDP growth. FCI serves as a useful tool for the conduct of monetary policy since it encompasses variables capturing important channels of monetary policy transmission in a single indicator. Further, since financial indicators are known to have predictive power, FCI can be used as an input for econometric models for GDP growth forecasting.2
3. The paper is organized as follows. Section B provides an overview of the methodologies and results from calculating FCI’s. Section C assesses the predictive power of FCIs. Natural interest rate is estimated in section D, and section E concludes.
B. Building a Financial Conditions Index for Georgia
4. We construct the FCI using two complementary methods. Following the literature, a regression-based FCI and a factor based FCI’s are two parametric approaches used to estimate FCIs.3
VAR-Based FCI
The FCI is constructed based on vector auto-regression (VAR):
Wherein, FCI in each period is calculated as a weighted average of n different financial variables (xit), where ωi is the weight attached to the variable and
5. The variables in the FCI are selected based on various channels of monetary policy transmission and their impact on GDP growth. Financial variables reflecting various channels of monetary policy transmission (interest rate, exchange rate, credit, and asset price channel) are selected based on theory. The final variables included in the model are chosen based on an exploratory process like Swiston (2008). Further, the weight of each variable, ωi, included in the model is estimated as the cumulative 5-quarter impulse response of GDP growth to a unit shock of xit. The impulse responses are estimated from a recursive VAR including all financial variables, real GDP growth and the GDP deflator. Finally, the identification of structural shocks is based on a Cholesky decomposition.
6. Both external and domestic variables play a role in defining financial conditions. The initial dataset includes various measures of consumer and asset prices, interest rates, exchange rates, risk and banking sector indicators, and external sector indicators. The final variables in the FCI are included following this criterion: (i) the time span should cover at least the last 10 years, and (ii) the impulse response of GDP growth to a unit shock in financial variable should be economically meaningful. The final set of variables included in the estimation include deposit growth, REER, loan spread (domestic variables) and VIX (Chicago Board Options Exchange Volatility Index), EUR-OIS spread (spread between 3-month Euribor and Euro Overnight Index Average (EONIA)) (external variables).4 The FCI tracks GDP growth better than individual indicators (Table 1). FCI is strongly correlated with current and future GDP growth.
Correlation between Real GDP Growth and Financial Variables
p < 0.05,
p < 0.01,
p < 0.001
Correlation between Real GDP Growth and Financial Variables
| Real GDP Growth | |||||
|---|---|---|---|---|---|
| t | t+1 | t+2 | t+3 | t+4 | |
| FCI | 0.544*** | 0.644*** | 0.629*** | 0.608*** | 0.533*** |
| VIX | −0.363** | −0.425*** | −0.476*** | −0.320* | −0.305* |
| EUR-OIS spread | −0.0815 | −0.247 | −0.294* | −0.446*** | −0.479*** |
| Deposit growth | 0.787*** | 0.763*** | 0.607*** | 0.493*** | 0.187 |
| Loan spread | −0.0961 | −0.097 | −0.0834 | −0.0779 | −0.0907 |
| REER | −0.484*** | −0.553*** | −0.547*** | −0.505*** | −0.451*** |
| N | 60 | 60 | 60 | 60 | 60 |
p < 0.05,
p < 0.01,
p < 0.001
Correlation between Real GDP Growth and Financial Variables
| Real GDP Growth | |||||
|---|---|---|---|---|---|
| t | t+1 | t+2 | t+3 | t+4 | |
| FCI | 0.544*** | 0.644*** | 0.629*** | 0.608*** | 0.533*** |
| VIX | −0.363** | −0.425*** | −0.476*** | −0.320* | −0.305* |
| EUR-OIS spread | −0.0815 | −0.247 | −0.294* | −0.446*** | −0.479*** |
| Deposit growth | 0.787*** | 0.763*** | 0.607*** | 0.493*** | 0.187 |
| Loan spread | −0.0961 | −0.097 | −0.0834 | −0.0779 | −0.0907 |
| REER | −0.484*** | −0.553*** | −0.547*** | −0.505*** | −0.451*** |
| N | 60 | 60 | 60 | 60 | 60 |
p < 0.05,
p < 0.01,
p < 0.001
7. The impact of selected variables on GDP growth is in line with expectations (Figure 1). The structural shocks are identified using Cholesky decomposition using the ordering: VIX, EURIBOR – OIS, GDP growth, GDP deflator, deposit growth, loan spread, REER, following Ho and Lu (2013). This assumes that domestic financial conditions do not affect growth and inflation contemporaneously, and similarly domestic variables (both real and financial) do not have a contemporaneous effect on external variables. GDP growth falls in response to exchange rate appreciation, higher loan spread, and higher VIX and EUR-OIS spread reflecting tight global credit and liquidity conditions. In contrast, higher deposit growth is found to increase GDP growth.
Georgia: Response of GDP Growth to Financial Shocks
Citation: IMF Staff Country Reports 2018, 199; 10.5089/9781484364062.002.A004
Source: IMF staff calculations.Georgia: Response of GDP Growth to Financial Shocks
Citation: IMF Staff Country Reports 2018, 199; 10.5089/9781484364062.002.A004
Source: IMF staff calculations.Georgia: Response of GDP Growth to Financial Shocks
Citation: IMF Staff Country Reports 2018, 199; 10.5089/9781484364062.002.A004
Source: IMF staff calculations.Factor-Based FCI
8. The FCI is calculated based on the factor analysis, wherein an unobserved common factor is extracted that captures the greatest common variation in our chosen financial variables. The common factor is extracted by estimating an equation of the form:
Where Xt is a vector of financial variables, μ is a vector of variable means, and Ft is the common (unobserved) factor.
We further purge the common factor of any influence of past economic activity. This alleviates concerns about causality from economic activity to financial conditions. In particular, we regress Ft on current and lagged values of output growth:
Where B(L) is the lag operator, yt is the GDP growth and the error term, ϑt is our measure of factor-based FCI.
9. We use the same set of variables (as in VAR) for constructing the FCI using factor analysis. Except for loan-spread, the correlation of each individual financial variables with the common factor is greater than 50 percent, reflecting the importance of each variable. The financial variables have similar qualitative effect on the common factor as in VAR-based FCI: positive correlation for deposit growth and negative correlation for REER, VIX, EUR-OIS spread and loan spread (Figure 2).
Georgia: Financial Conditions Index
Citation: IMF Staff Country Reports 2018, 199; 10.5089/9781484364062.002.A004
Sources: National Bank of Georgia, and IMF staff calculations.Georgia: Financial Conditions Index
Citation: IMF Staff Country Reports 2018, 199; 10.5089/9781484364062.002.A004
Sources: National Bank of Georgia, and IMF staff calculations.Georgia: Financial Conditions Index
Citation: IMF Staff Country Reports 2018, 199; 10.5089/9781484364062.002.A004
Sources: National Bank of Georgia, and IMF staff calculations.Overview of the FCIs
10. The financial conditions are assessed to be slightly loose in recent period. This is driven largely by loose global monetary and liquidity conditions coupled with cyclical recovery in Georgia and relatively stable exchange rates.
11. The two measures of FCI are highly correlated with each other, and with GDP growth (Figure 3). An increasing index indicates easing financial conditions while a decreasing index reflects tightening.
Georgia: FCI Predictive Power
Citation: IMF Staff Country Reports 2018, 199; 10.5089/9781484364062.002.A004
Sources: National Bank of Georgia; and IMF staff calculationsGeorgia: FCI Predictive Power
Citation: IMF Staff Country Reports 2018, 199; 10.5089/9781484364062.002.A004
Sources: National Bank of Georgia; and IMF staff calculationsGeorgia: FCI Predictive Power
Citation: IMF Staff Country Reports 2018, 199; 10.5089/9781484364062.002.A004
Sources: National Bank of Georgia; and IMF staff calculations12. Historically, exchange rate has been the most important driver of financial conditions; and global factors are increasingly important. The weight of each variable is given by the cumulative five quarter response of GDP growth to a unit shock in the financial variable. Financial conditions were supportive prior to the global financial crisis (GFC). However, they tightened significantly in 2008 due to the dual effect of GFC and armed conflict with Russia. Financial conditions somewhat recovered before further deteriorating in 2011 coinciding with the period of rapid monetary contraction in response to food price shock. Among domestic variables, exchange rate and loan spread appear to be most important. Both VIX and EUR-OIS spread also have a significant impact, particularly after the GFC.
C. Forecast Evaluation Using FCI
13. The FCI may be used to assess future GDP developments. The forecasting properties of both VAR and factor-based FCI’s suggest that they both have meaningful predictive power. Adding FCI as an additional variable in equation 1 significantly increases the R-squared. FCI’s can explain up to 30 percent of the variation in GDP growth not explained by lagged GDP growth (Figure 1). Hence, adding FCI increases the predictive power of the model. Specifically, we estimate the following regression equation:
Where yt+h is the h quarter ahead forecast of the variable of interest (GDP growth) and Xt denotes the indicator being evaluated (FCI). The number of lags included in the model are chosen based on Akaike information criteria (AIC).
D. Estimating the Natural Interest Rate for Georgia
14. We estimate natural interest rate for Georgia to assess its monetary policy stance. The natural interest rate is defined as a rate at which an economy is in a stable price equilibrium. Natural interest rate is a useful tool to assess the appropriateness of monetary policy wherein a policy interest rate higher (lower) than the natural rate reflects tight (loose) monetary policy. A time varying parameter vector auto-regression (TVP-VAR) is estimated to extract the natural rate, since the natural interest rate is unobserved. We follow Lubik and Matthes (2015) to estimate TVP-VAR for three variables—the growth rate of real gross domestic product, the inflation rate, and a measure of real interest rate. The natural interest rate is then extracted by using a long-horizon forecast (5-year forecast) of the observed real rate as a measure of the natural rate of interest.
Natural Interest Rate
Citation: IMF Staff Country Reports 2018, 199; 10.5089/9781484364062.002.A004
Sources: National Bank of Georgia; and IMF staff calculations.Natural Interest Rate
Citation: IMF Staff Country Reports 2018, 199; 10.5089/9781484364062.002.A004
Sources: National Bank of Georgia; and IMF staff calculations.Natural Interest Rate
Citation: IMF Staff Country Reports 2018, 199; 10.5089/9781484364062.002.A004
Sources: National Bank of Georgia; and IMF staff calculations.15. Georgia’s real natural interest rate is estimated to be around 2 percent, and is found to be largely stable over the sample period (2013Q4 – 2018Q1). Using survey-based inflation expectations, Georgia’s real interest rate at 1.6 percent is lower than the natural rate, reflecting slightly loose monetary policy.
16. However, natural rate is estimated with uncertainty. While the estimated natural rate is lower than the real rate, uncertainty remains due to (i) estimation methodology wherein natural rates are usually estimated with large standard errors (Laubach and Williams, 2003; Lubik and Matthes, 2015) and (ii) difficulty in accurately measuring inflation expectations, particularly for emerging market economies.
E. Conclusion
17. Monetary stance is assessed to be broadly adequate. In this paper, we assess the monetary stance by estimating financial condition index and natural rate for Georgia. Both approaches suggest a slightly loose monetary stance. However, in the absence of price pressures (2.5 percent inflation in April 2018) despite cyclical upswing in the economy and narrowing output gap, monetary stance is assessed to be broadly adequate at this point.
References
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Hofman, D., 2011, “A Financial Conditions Index for Russia,” IMF Country Report 11/295 (Washington: International Monetary Fund).
Laubach, T., and J. C. Williams, 2003. “Measuring the Natural Rate of Interest,” The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 1063–1070, November.
Lubik, T. A., and C. Matthes, 2015, Calculating the Natural Rate of Interest: A Comparison of Two Alternative Approaches, Richmond Fed Economic Brief, issue Oct, p. 1–6.
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Swiston, A., 2008, “A U.S. Financial Conditions Index: Putting Credit Where Credit is Due,” IMF Working Paper 08/161 (Washington: International Monetary Fund).
Prepared by Umang Rawat (MCM).
English et al. (2005), Swiston (2008), and Hatzius et al. (2010) show that FCIs are highly correlated with GDP and have a strong predictive power for future economic activity.
See Beaton et al. (2009) and Hatzius et al (2010) for useful overviews of the FCI literature and existing FCIs.
Equity prices were dropped due to limited availability of data and low stock market capitalization in Georgia. House price index was dropped as we failed to find a significant and economically relevant relation with GDP growth.
