How well did the Russian Central Bank perform during the
financial crisis in 2014 according to the simple Taylor Rule?
Abstract
This thesis analysis the interest rate policy of the Russian Central Bank during the financial crisis in 2014. The interest rate policy is evaluated with the simple Taylor Rule. The Taylor Rule interest rate is compared with the interest rate of the Russian Central Bank by doing a regression and it showed that they are significantly different from each other. According to the Taylor Rule, the interest rate policy of the Russian Central bank did not fit the needs of the Russian economy.
Bachelor Thesis Economics and Finance Author: Thierry Wong
Student Number: 10775765 Supervisor: Ioana Neamtu Date: 25-06-2018
Statement of originality
This document is written by Student Thierry Wong who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and it references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Contents
1. Introduction 4
Financial Crisis Russia 5
2. Literature Review 6 3. Methodology 10 Data 10 Method 11 4. Results 12 5. Discussion 15 6. Conclusion 16 Appendices 17 References 18
1. Introduction
In times of economic distress, central banks have several tools they can use to target two different monetary instruments to act on this distress. Those instruments are the interest rate and the monetary base. In practice most central banks nowadays target the interest rate, because of its controllability and because it can be quickly observed and measured. The instrument also needs to have a predictable effect on the goals and therefore central banks prefer to target the short-term interest rates as a policy instrument. (
Mishkin F., K. Matthews
and M. Giuliodori, 2013).
On July 2014 the Russian economy experienced a financial crisis. The Russian Central Bank (RCB) reacted by raising the key rate as high as 17%. The purpose of the paper is to evaluate if the RCB reacted correctly to this financial crisis, by choosing the right interest rate. The interest rate decisions of the RCB will be evaluated by using the monetary policy model called the simple Taylor rule created by Taylor (1993). It is called simple because the model nowadays has many extensions, which incorporate more economic indicators. In the simple Taylor rule model the main economic indicators that are used to estimate the ideal interest rate are the inflation gap and the output gap. In this paper I will extensively explain what those economic indicators mean for Russia and how they are used in the Taylor rule. The dependent variable of the model is the optimal short-term nominal interest rate and this will be compared to the actual interest rate that the RCB used during the financial crisis. The simple Taylor rule is usually applied to developed economies and not to emerging market economies like Russia because of the economic instability. In this thesis, the assumption is made that Russia’s economy has developed over the years and that it is mature enough to be compared to more developed countries and therefore the Taylor rule is a valid model to evaluate the monetary policy measures of the RCB. The hypothesis of the thesis is formulated as: Did the interest rate policy of the RCB during the financial crisis in 2014 match the interest rate of the simple Taylor Rule.
The rest of this thesis will consist of a literature review section in which the Taylor model is explained, a methodology section where I explain what variables are used to test the model and the outcome of the analysis. In the final part I discuss the results and the conclusion of this research.
Financial crisis Russia 2014
The annual report for 2014 of the Russian Central Bank stated that the annual economic growth declined from 1.3% in 2013 to 0,6% in 2014. In a statement on this annual report, the governor of the Russian Central Bank (RCB) called 2014 a troubled year due to
two factors. The first factor is the Western sanctions on Russia and the second factor was the falling oil price(Russian Central Bank, 2015).
On the 6th of March the U.S. Department of state announced that, together with the EU, Japan, Switzerland and other countries, it would impose sanctions on Russia as a reaction on the Russian annexation of the Crimea. Goal of the sanctions was to send a message to the Russian government that it could not annex foreign land without consequences("Ukraine and Russia Sanctions", 2018).
The sanctions progressed from diplomatic sanctions (Faze 1) to sanctions on
individuals and entities (Faze 2) before finally adopting economic sanctions (Faze 3). The EU and US reinforced economic sanctions consisted of: restricted Russian access to EU capital markets, an arms embargo and restricted cooperation with the Russian energy sector. According to the European Parliamentary Research Service (2016) the sanctions affected Russian economy in various ways. The main short-term impact on the economy came from lending restrictions on Western lending and investment in Russia. The gas and oil production remained unaffected.
The second factor that caused the Russian finance crisis was the low oil price. In the second half of 2014, oil prices fell by approximately 50% between June 2014 and January 2015. According to the RCB this was due to stronger competition, new competitors in the oil market and a decrease in global demand for oil.
As a result of these two factors, the ruble almost halved in value compared to the dollar, inflation moved up too 11,4% by the end of 2014, which was higher than the 5% target inflation, and a drop in business confidence. This led to a slowdown in economic activity and reduction in fixed capital investment.
In the annual report of 2014 the RCB emphasizes that their main objective is price stability. This means that they want to achieve and maintain stable and low rates of inflation, which is according to them, essential to guarantee balanced and sustainable economic growth. Inflation rate targeting can only be achieved under a floating exchange rate regime. In a reaction to the depreciation of the Ruble and the high inflation rate the RCB increased the interest rate over 6 occasions starting in March 2014 with 6.5% to 17% in December 2014. Prior to the recession Russia inflation targets for 2014, 2015 and 2016 were
respectively 5.0%, 4.5% and 4.0%(Russian Central Bank, 2015). The policy instrument that the RCB used to achieve these inflation targets was by adjusting the key interest rate on its operations. Since 2014 the RCB is pursuing a floating exchange rate regime. Before this, the RCB used a dual band of around 9 rubles compared to a foreign exchange rate basket of which the weights consisted of 45% Euro and 55% Dollar price. This floating exchange rate regime means that the balance between the demand for foreign currency and its supply in the domestic exchange market shapes the exchange rate of the ruble. In the literature review
part I will elaborate more on how this floating exchange rate regime applies to the Taylor Rule. The expected GDP growth for 2014, 2015 and 2016 considering a baseline scenario were respectively 2.0%, 2.5% and 3.0%(Russian Central Bank, 2015). These growth
expectations were also in accordance with the projections of the IMF ("IMF World Economic Outlook (WEO) Update: Is the Tide Rising?, January 2014", 2018).
2. Literature review
This literature review will consist of three parts. First I explain the mechanism of the Taylor Rule and the assumptions that are made. Secondly I discuss how the model applies to emerging market economies in general and finally I discuss why I assume the Taylor Rule is suitable to evaluate the monetary policy rules of Russia.
The interest-based rule described by Taylor (1993) has been widely used in research on evaluating monetary policy measures. Taylor (1993) found that policy rules that focused on exchange rate or policies that focused on the money supply did not perform as well as policies that focused on price level and real output directly. He developed the Taylor rule to approximate how high the short interest rate of a country must be to reach its inflation or output target. The rule states that the short-term nominal interest rate should rise if the GDP is above its potential level or when inflation is above its target level. The equation is
formulated as:
𝒊 = 𝒓 ∗ + 𝒑𝒊 + 𝟎. 𝟓 (𝒑𝒊 − 𝒑𝒊 ∗) + 𝟎. 𝟓 ( 𝒚 − 𝒚 ∗)
Where 𝒊 is the nominal short-term interest rate, 𝒓 ∗ the real interest rate, 𝒑𝒊 is the rate of inflation, 𝒑𝒊 ∗ is the target inflation rate, 𝒚 is the logarithm of the real output and 𝒚 ∗ the logarithm of the potential output. Logarithms are used because this represents the percentage change per year rather than the absolute change.
In this model, several assumptions are made. The first one is that the short-term nominal interest rate should be equal to the real interest rate plus the inflation rate plus a weighted average of the inflation gap and the output gap. In his paper Taylor (1993) stated that there is consensus about the size of the coefficients on price and output, but that the equation above captures the spirit of a representative policy rule and is also consistent with recent research. The weights of 0,5 are also validated in later research on the United States and Europe. An example is the research done by Gerlach & Schnabel (2000). They applied the Taylor Rule to the EMU area between 1992 en 1999. According to them the Taylor Rule captured the interest policy rather well, except for the period 1992-1993 when there was exchange market turbulence. Later extensions of the Taylor Rule successfully added more
variables to the equations, which caused the weights to change. For simplicity I will not add any variables to the model in this paper. In Taylors (1993) view, a policy rule should not be just concluded with a single formula, but should rather be implemented and operated informally by a central bank that understands the instruments’ responses that underlie the policy rule and also who recognizes that operating the rule requires judgment. The second assumption is about how we determine the real interest rate 𝒓 ∗. In his paper, Taylor used a real interest rate of 2%, but this does not hold for Russia, because of different economic conditions. For simplicity we calculate the new real interest rate for every month by subtracting the inflation rate from the nominal interest rate.
Some of the assumptions that Taylor (1993) made in his model were questioned by McCallum (1993). McCallum argued that policymakers had to make assumptions about the equilibrium real interest rate and the potential output. These variables are known to be unreliable indicators for monetary policy. These unreliability’s were later quantified and proven in several researches.
Despite this proven unreliability, the Taylor Rule is still successful in predicting the monetary policy measures in several researches. The overall consensus is that the Taylor Rule predicts the monetary polices of developed countries rather well.
In another paper, Taylor (2000) argued why applying monetary policy rules to
emerging markets has the same benefits as for developed markets. He does this by pointing out 5 issues with respect to the monetary policy in emerging markets: “(i) the appropriate policy instrument in a monetary policy rule (ii) the appropriate degree of specificity of the rule, (iii) the relationship of a monetary policy rule to inflation targeting, (iv) the implications of underdeveloped long-term bond markets for the choice of a policy rule, and (v) the role of the exchange rate in a monetary policy rule.” (p. 3). The difference between developed
economies and emerging economies is that they might want to use different policy
instruments, If a country encounters a lot of uncertainty in determining the real interest rate or if there are big shocks in net exports than the money supply within the economy might be a better instrument compared to the interest rate. Also for both developed and developing economies, monetary policy rule should be used as a guideline rather than following the rule mechanically. The monetary policy should allow a certain degree of discretion, because if for example a liquidity crisis occurs, the central bank should be able to low its interest rate to counter this crisis. When a country pursues a floating exchange rate, an inflation rate target is essential. For emerging markets it is seen as an alternative for monetary aggregate targeting because for some of those countries the money supply is a better monetary instrument. Emerging markets should also set clear expectations through a monetary policy rule. Reifschneider and Williams (1999) have shown that the effects expectations can reduce the likelihood for an economy the run into a deep recession. These expectation effects exist
in any monetary policy rule where the use instruments are dependent on future events. In general emerging economies don’t have very liquid maturity markets, this means that they are less able to attract liquidity through for example issuing long-term bonds. This means that they are less effective in creating an expectations effect, but they should still communicate their intentions very clearly to the private sector, because expectations do not only affect the term structure, but also the exchange rate, domestic prices and wages. However, with underdeveloped long-term liquidity markets, policymakers should react more quickly to changes in the economy, because there is more weight on the short-term interest rate, because there is less expectation effect. Lastly, Taylor (2000) states that past research on the inclusion of the exchange rate in the simple Taylor Rule did not have any significant effect on its ability to predict the interest rate for developed economies. Also simple policy rules of emerging markets, which tend not to focus on the exchange rate work rater well. But Taylor (2000) argues that the effects of fluctuation in exchange rate might be understated for small open economies. The effects of exchange rate fluctuation can be very negative for an economy if there is a mismatch in assets and foreign denominated debt. Earlier research on Russia’s monetary policy by Esanov. et al, (2004) states that it is complicated to estimate the monetary behavior by a single rule because of the unstable economic environment in Russia. Williamson (2000) argues that policy makers in emerging markets are more concerned with exchange rates than policy makers in developed countries, because of the exchange rate pass-through to prices, which is the elasticity of import prices denoted in local currency with respect to the local currency price of foreign currency. Mishkin (2000) describes 5 elements of inflation targeting. “(i) The public announcement of a medium term numerical target inflation; (ii) an institutional commitment to price stability as the primary goal of monetary policy, to which other goals are subordinated; (iii) an information inclusive strategy in which many variables are used to decide the setting of policy instruments; (iv) increased
transparency of monetary-policy strategy through communication with the public and markets; (v) increased accountability of CB for maintaining its inflation objectives” (p. 105). With inflation targeting, the focus is more on the year ahead than the year of the
announcement of the numerical inflation target. This is important in the context of emerging market economies because many of them set numerical inflation expectations as part of a governments’ economic plan for the year ahead. According to Mishkin (2000) the first 4 elements do not hold for emerging markets, and thus also the 5th element is not attained. Emerging market economies cannot credibly commit to an inflation target strategy, but are rather focused on a monetary strategy. This is why many researchers think that the
exchange rate should be incorporated in a central banks’ reaction function. Vdovichenko & Voronina (2006) stated the interest rate policy has lacked efficiency in Russia. This is due to two factors. The first one is the absence of a well-developed financial market and the
shortage of monetary policy tools available to the RCB. So this raises the question if the Taylor Rule, which uses only the inflation gap and the output gap, is the right model to evaluate the monetary policy of Russia?
Between 2000 and 2012 Russia’s GDP per capita has almost doubled from $3900 to $7200 between (IMF, 2018). This raises the question if the growth was sustainable and if so, this was also a factor that played a role in the Russian financial crisis. In appendix 1 we see the quarterly output gap from the year 2000 until 2018. The output gap is seen as the excess output compared to the potential output level. If this gap is positive, its capital utilization rate is above its natural level. To explain the consequences this can have for an economy, we consider a country where the capital utilization level is at its natural rate. This level is corresponding with a certain inflation rate 𝜋. If we assume that the production factors are fixed in the short term, a positive demand shock will cause suppliers to increase their
capacity utilization rate. This, in turn, will also cause the inflation to rise. However, in the long run inflation expectations will increase and workers will demand higher wages and lenders will demand higher interest rate, which means that the factor costs will go up and according to the short-run Philips curve described by Phillips (1958), this means that for a given inflation level, suppliers will produce less and inflation will increase more(Oomes & Dynnikova, 2006). In appendix 2 we can see that the output gap has been positive at two moments. The first moment is before the global financial crisis in 2008 and the second moment is during the Russian financial crisis in 2014, although the gap was then
considerably lower and this was also for a shorter period than in 2008. Nonetheless this still might be an indication that the Russian economy was overheating, and thus might have been a factor that caused the crisis in 2014. However, I will not extend on this topic in the rest of this thesis.
In Appendix 2 we can see that the Russian GDP per capita in terms of purchasing power parity (PPP) is almost twice as high as it is for world average and other BRIC
countries. We can also see that the GDP per capita is almost as high as it was for the EMU countries in 2000. Another development that gives me a reason to believe that the Russian economy is getting more advanced is that the RCB has smoothened its managed floating exchange rate regime from 2010 to 2014 with the goal to improve the monetary policy transmission mechanism. In 2010 it started with a dual band floating exchange regime of 7 rubles compared to a basket of the dollar and euro. In 2010 they widened the band from 7 to 9 ruble and in 2014 they abolished the dual band floating exchange rate regime and made a transition to an exchange rate regime without borders. However this new approach did not mean a complete abandonment of FX interventions (Russian Central Bank, 2018). To gain more credibility from the public, the RCB also tries to make the monetary policy it conducts as transparent as possible. They do this by regularly informing the general public about the
banks’ vision of the current economic situation and development forecasts with the
corresponding decisions and their potential outcomes. Although the exchange rate regime is still managed, the transition that the mechanism has made and also the growth that the Russian economy has made in the last 15 years might indicate that the Russian economy has become more mature. I am aware that adding more variable to the equation of the policy rule like for example, the oil price or the exchange rate, the model will most likely give a better prediction of the interest rate policy that is best for Russia, but because of limited knowledge and time this paper will be kept simple. That is why we assume that the Taylor model is the right mechanism to evaluate the monetary policy that the RCB has conducted during the Russian financial crisis from 2014 to 2018.
3. Methodology and empirical analysis
Data
To evaluate the monetary policies of Russia, quarterly data is used for all the
variables in the Taylor Rule. The time frame stretches from the 1st of January 2014 to the 1st of January 2018. For the actual short-term interest rate, the yield of the one-day overnight interbank loans is used. The overnight interest rate, at which banks with excess reserves can lend to banks with a shortage in reserves, can be influenced by the central bank and is used to target monetary policy. This rate will be compared with the TR interest rate and was obtained through DataStream from the Thomson Reuters database. The real interest rate was calculated by subtracting the inflation rate from the nominal interest rate. For the nominal interest rate yields on 10-year interest rate swaps are used. Both the inflation and the yields of the interest rate swaps are obtained from DataStream, from the Thomson Reuters database. The Inflation target rate for 2012 and 2013 was obtained from the annual report by the RCB (2013). The RCB stated that they pursued a target rate between 5% and 7%, so for this paper an average of 6% was used. The inflation target rates for 2014, 2015, 2016 and 2017 were obtained from the annual report by the RCB (2015). These target inflation rate are subtracted from the actual inflation rate to calculate the inflation gap (𝒑𝒊 − 𝒑𝒊 ∗). A positive inflation gap means that the actual inflation rate is above the ideal inflation rate for the central bank. The central bank will therefore increase the interest to lower liquidity. Instead of obtaining the actual output and the output target separately, the output gap (𝒚 − 𝒚 ∗) was directly obtained from DataStream from the Oxford Economics database. It represents the deviation of the actual output from the potential output. A positive output gap means that demand within an economy is higher than what it can produce. This is not sustainable and can eventually lead to an overheating economy. The central bank must therefore react by increasing the interest rate and decrease the liquidity. The output gap was
directly obtained from DataStream from the Oxford Economics database. The oil price that is observed is from Crude Oil Western Texas Intermediate Futures, because this oil type is often used as a benchmark. The oil prices are obtained from Investing.com. The data of the oil price will be used to compare the course of the oil price with the TR and RCB interest rates.
Method
The research method used to test if the RCB has set the right interest rate during the financial crisis in 2014 according to the Taylor Rule, is based on earlier research by Moons and Van Poeck (2008). Their research focuses on EMU countries. The goal of their research is to test if the desired Taylor-based interest for each individual member country of the EMU is equal to the interest rate set by the ECB. For this interest rate the EONIA is used as a proxy. In this paper, the EONIA will be replaced by the overnight interbank interest rate in Russia. To test if Russia has pursued the appropriate interest rate, we first make a simple regression of the actual short-term interest rate that has been used 𝑖!"# and the interest rate
defined by the Taylor Rule 𝑖!". The regression equation is defined by the following formula:
𝑖!"# = 𝛽!+ 𝛽! 𝑖!"+ 𝜀
T-tests are used to test if the coefficient 𝛽! is not significantly different from 0 and if
the coefficient 𝛽! is not significantly different from 1.
Next the root mean squared interest rate gap is calculated, which is defined by:
𝑅𝑀𝑆𝐼𝐺 = !!"# !!!(!!",!!!!"#,!)! !
!
Where 𝑇 is the number of quarters, 𝑖!" is the interest rate defined by the Taylor Rule and 𝑖!"#
is the short-term overnight interbank rate in Russia. By calculating the RMSIG, all the
negative values of the interest rate gap are made positive and we calculate the total average interest rate gap. This RMSIG is than compared to the RMSIG’s of the euro countries that are used in the research of Moons and Van Poeck (2008) by doing a graphical analysis.
I also test if the interest rate set by the RCB has the tendency to get more in line with de interest rate that is ideal according to the Taylor Rule. We do this by defining the interest rate gap:
The interest rate gap for every month is set in a graph. This way we can check if the gap between in the Taylor interest rate and the actual interest rate changes over time. We can for example see if there is any difference in gap size before and after the crisis started and. We can also see if the interest rate gap is becoming smaller one the economic environment is stabilizing after the start of the crisis. If the output gap is positive, this means that the interest rate that is set by the RCB is too low. If the output gap is negative, this means that the interest rate that is set by the RCB is to high.
4. Results
Regression
In table 1 can see the regression output. Here the t-value for the constant is with respect to 0 and the t-value of the TR interest rate coefficient is with respect to 1.
B Std. deviation t-value p-value
Constant 2,713 0,587 4,622 < 0,00
Taylor Rule interest rate
0,598 0,056 -7,179 < 0,00
Table 1. regression output
The regression equation is defined as:
𝑖!"# = 2,713 + 0,598 𝑖!"
Given these results we firstly see that the constant 𝛽! is equal to 2.713, with a t value of 4,622, which means that the constant is significantly different from 0. This indicates that the Interest rate that is set by the RCB is systematically too low compared to the TR interest rate. Possible causes of this systematic deviation might be the lack of variables that are used in the simple TR equation or 1-day overnight interest rate does not represent the actual short-term interest rate. The coefficient of the TR interest rate is 0,598 with a t-value of -7,179. This coefficient shows that the RCB interest rate moves in the same direction as the TR interest rate, but it is less volatile, which means CBR is less responsive to changes in the inflation gap and the output gap. The high t-value indicates that the coefficient is significantly different from 1. Moons and Van Poeck (2008) also ran a regression for the EMU countries, which gave the following results:
The t-value for the constant was 2,87 and for the EMU interest rate coefficient was 6,17. This indicates that both coefficients are not significant, but because 1,11 is closer to 0 and 0,68 is closer to 1 this indicates that EMU n is better at tracking the TR interest rate compared to the
RCB.
So from the output in table 1 we can conclude that the RCB could have done a better job in setting their monetary policy, according to the Taylor Rule.
Graph 1. TR vs RCB Interest Rate vs Oil Price 2012-2018
In the graph above we see how the TR interest rate compares to the actual interest rate that was set by RCB. It moves more or less in the same direction, but as we saw in the regression output, RCB interest rate is systematically too low compared to the TR interest rate. The interest rate of the central bank already starts to increase at the beginning of 2014. From September 2014 we can see the increase in interest rate is almost vertical. There are a few events in this period that caused this increase. The first one is the reinforcement of economic sanction of both the EU and the US on Russia. After already announcing economic sanctions in July, both the EU and the US restricted Russian access to EU capital markets enforced an arms embargo and restricted cooperation with the Russian energy sector. The second event is that the oil price fell from $105 in June 2014 to 48$ in January 2015, this can also be seen in the graph. After January 2015 the oil price fell even lower, to 33$ per barrel in January 2016, but somehow both the TR and the CBR interest rate don’t seem to react to this drop in oil price.
$- $20,00 $40,00 $60,00 $80,00 $100,00 $120,00 0,0% 5,0% 10,0% 15,0% 20,0% 25,0% Actual Interest Rate Taylor Rule Interest Rate Oil Price
In the graph we can see that from September 2016, the TR interest rate is
approaching the interest rate levels prior to the financial crisis. The RCB interest rate is still above the interest rate prior the crisis and it continues to be so up to the end of 2017. It is interesting to see how the two interest rates cross each other only one time. This is in March 2016, after the start of the beginning of the financial crisis. A reason for this might be that the RCB is still cautious for new economic setbacks and therefore keeps the interest rate
relatively higher than before the crisis. The TR interest rate does not incorporate future events and is only concerned with current economic indicators.
Graph 2. Interest rate gap (2012-2018)
In this graph we see how the difference between the TR interest rate and the RCB interest rate moves over time. As stated earlier, we can see that the RCB interest rate is systematically to low before the crisis and as we can see in this graph, this gap becomes bigger during the crisis. From January 2016 the gap drops and becomes negative. As stated earlier, this describes the situation where the RCB is forward-looking and in this case
cautious for economic setbacks. The TR does not incorporate future events. This also indicates that the correlation between RCB and TR interest rate becomes smaller after the crisis in 2014.
If we compare this graph to what we saw in the output of table 1, we can see that the RCB interest rate is not systematically too low over the whole period. This makes it unlikely that the high constant is caused by the wrong interest rate, because al the short-term interest rates move more or less in the same direction.
-3,0% -2,0% -1,0% 0,0% 1,0% 2,0% 3,0% 4,0% 5,0% 6,0% 7,0% Interest rate gap
Graph 3. Root Mean Square Interest Rate Gap EMU countries vs Russia.
In graph 3 we see the RMSIG of several EMU countries that were by Moons and Van Poeck (2008) and the RMSIG of Russia. Something that should be taken in consideration is that the period in which the EMU countries are studied is between 1999, Q1 to 2003, Q3. They stated that the interest rate of the ECB was a better fit for the bigger countries in the EMU, because those also had a bigger weight on the total economy. Smaller economies like, Greece, Portugal, The Netherlands and especially Ireland were not served as good as the bigger countries by the ECB interest rate according to the TR equation. The RMSIG of Russia is somewhat the same as those small European countries. From this graph we can conclude that the RCB could have served the Russian economy better, because the RMSIG is too big.
5. Discussion
Methodological issues
The set up of this thesis had some limitations. In this thesis we assumed that Russia’s economy has developed over the years and that it was mature enough to use the simple TR to evaluate the monetary policy of the central bank. But the regression showed that the RCB interest rate was systematically too low compared to the TR interest rate. If more variables would have been included, the TR interest rate might have been different from the interest rate found in this thesis. If we had threated Russia as an emerging market, the exchange rate would have been an important indicator in the equation as explained by Mishkin (2000) and Taylor (2000). As we saw in graph 1, the shock in oil price also affected the interest rate. If we had added the price in the equation, the variance between the TR interest rate and the RCB interest rate might have been less.
0,0% 1,0% 2,0% 3,0% 4,0% 5,0% 6,0% 7,0% RMSIG
Results
The results of the regression indicate that the RCB did not implement the optimal monetary policy according to the Taylor Rule equation. The coefficient of the TR interest rate with respect to the RCB interest rate was 0,598, which indicates that they moved in the same direction, but RCB was less responsive to changes in the inflation gap and the output gap. A possible explanation is that the weight of at least one of these economic indicators was too high for Russia and therefore the TR model responded excessively compared to reality. Another explanation is that there is omitted variable bias. If another variable like oil or exchange rate had been added, this might have resulted in a lower TR interest rate. As stated earlier, the coefficient is significantly zero from 1, which means that the RCB could still improve its monetary policy with respect to the TR. The constant in the regression equation was 2,713 and was significantly different from 0. This indicates that the interest rate set by the RCB was systematically too low. As we saw in the comparison with the results of Moons and Van Poeck (2008), this constant is quite high. There are a few possible explanations for why the RCB interest rate was too low. The first one is that the RCB was not cautious
enough in setting the interest rate. A low interest rate causes inflation rates to increase more than is ideal for an economy. In graph 2 we saw that the RCB interest rate was too low up until March 2016 and after that RCB systematically sets the interest rate higher than the TR desired interest rate, which could indicate that they have become more cautious of inflation because of the crisis. The second possible explanation is variables have been omitted from the TR model, that the RCB takes in consideration when it sets its interest rate. If we look at the output of the regression and assume that the TR is the right model, we can conclude that the RCB did not perform well in setting the right interest rate compared to the TR interest rate.
The RMSIG of 2,591 is rather high and also indicates that the RCB interest rate did not follow the TR interest rate. We also did a comparison with EMU countries between 1999 and 2003. This comparison visualized how the interest rates of bigger countries in Europe had a better fit with the TR interest rate compared to smaller countries. The RMSIG of Russia is close to the RMSIG of the smaller countries in Europe. This also indicates that the RCB could have done a better job at setting the interest rate if we compare it to the TR interest rate.
6. Conclusion
In this thesis we researched how well the Russian Central Bank reacted to the
financial crisis that started in 2014 up to the end of 2018 according to the simple Taylor Rule. The assumption was made that the simple Taylor Rule is the right mechanism to evaluate
the monetary policy interest rate of the RCB. The interest rate setting by the RCB did not fit the Taylor Rule interest rate in this time period. However due to limitations, the assumption that the simple Taylor Rule was the right mechanism is doubtful. Therefore it cannot be said with certainty that the RCB has used the wrong interest rate policy. Therefore it would be conducive for further research to include more variables in the Taylor Rule equation that are known to improve the model for emerging market economies.
Appendices
Appendix 1
Output Gap Russia (2000-2018)
Appendix 2
GDP per Capita in terms of PPP (2000-2018)
-10,0% -8,0% -6,0% -4,0% -2,0% 0,0% 2,0% 4,0% 6,0% Output Gap $- $5.000 $10.000 $15.000 $20.000 $25.000 $30.000 $35.000 $40.000 RUS WLD BRA CHN IND EUU
