Research Article 
Corresponding author: Shweta Sikhwal ( shwetasikhwal8181@gmail.com ) © 2022 Nonprofit partnership “Voprosy Ekonomiki”.
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Citation:
Sikhwal S (2022) Effects of US interest rate shocks in the emerging market economies: Evidence from panel structural VAR. Russian Journal of Economics 8(3): 234254. https://doi.org/10.32609/j.ruje.8.89717

We examine, using a monthly dataset from 2007 to 2020, the US interest rate shocks’ effects on exchange rates, broad money aggregates, and foreign exchange reserves in emerging market economies (EMEs) post global financial crisis. To evaluate the impact of unconventional monetary policy initiatives, we employ WuXia’s shadow interest rates. There are two parts to the methodology. The first part focuses on the identification of the unanticipated US interest rate shock in a SVAR model. In the second part, we incorporate the US interest rate shock into the panel structural VAR to analyze its impact on 29 countries from various regions. A positive shock to US interest rates depreciates the exchange rate of EMEs against the US dollar. According to our findings, it results in a decline in the broad money aggregate and foreign exchange reserves. The findings are consistent across multiple EME regions.
US interest rate, emerging market economies, shadow rate, panel structural VAR.
Interest rates are one of the Federal Reserve’s key monetary policy instruments, and through international capital flows, they affect the economic conditions of emerging market economies (EMEs). The post Global Financial Crisis (GFC) era has witnessed a change in the monetary stance of the United States. The Federal Reserve of the US opted to use a series of unconventional monetary policy initiatives. To counter the effects of the zero lower bound (ZLB) on interest rates, extensive purchases of assets were carried out. These measures are also known as quantitative easing (QE) measures, which were implemented in the US between the end of 2008 and October 2014, as well as between the second half of 2019 and March 2022 (Dabrowski, 2021). The Fed’s interest rates were raised by 225 basis points between December 2015 and December 2018 and then again lowered to zero between July 2019 and March 2020.
The changes in the monetary stance of the US raised concerns in the emerging markets about the spillover effects. The EMEs saw a substantial rise in international capital flows from the implementation of QE in the US. Being so fragile due to their history of inflation, macroeconomic crises, etc., the EMEs have been affected by the Fed’s monetary policy decisions, especially regarding the Federal Fund Rates (FFR), or even by announcements of them (
This study examines the impact of interest rates in the US on the macroeconomic factors of the EMEs by incorporating the QE measure through shadow rates. A shadow rate captures the monetary policy at the ZLB and reflects the effects of unconventional policies. We substitute the shadow interest rates provided by
The empirical strategy is to use the panel structural Vector Autoregression (VAR) to assess the macroeconomic spillover effects of the US shadow interest rate on the EMEs. The results conclude that with a positive shock in interest rates in the US, the economies of emerging markets contract. It suggests that the exchange rate of EMs depreciates against the US dollar, broad money declines, and foreign exchange reserves fall. These results are statistically significant.
This article is comparable to studies that investigate the consequences of monetary policy shocks in the United States, such as
Several recent studies, such as
A “flight to safety/quality” phenomenon appears to be triggered by a US uncertainty shock, based on the consequences of financial variables: Despite the increase in uncertainty in the US, investors appear to be pulling capital out of emerging markets that are perceived to be riskier than the US, negatively impacting asset prices such as stock prices and exchange rates in EMEs and driving up their borrowing costs as country spreads visàvis the US widen. Because of the increase in net exports and the drop in capital inflows, one of the avenues through which the effects of the US uncertainty shock spread is through a decline in EME aggregate expenditure.
The effect of quantitative easing in the US on EMEs is evaluated by
Our paper studies the effect of US interest rates on the macroeconomic condition of the EMEs. The study focuses on the reaction of exchange rates, broad money aggregates, and foreign exchange reserves to changes in US monetary policy. Here, we examine shadow interest rates as a monetary policy instrument in the United States. We first identify US monetary policy shocks in a US SVAR model. We then employ a Panel Structural Vector Autoregression (PSVAR) model, developed by
The structure of the paper consists of six sections. In section 2, we explain the employed data and methodology for the identification of US interest rate shocks, as well as the PSVAR approach and its specifications. In section 3, the spillover effects are discussed. We evaluate the robustness of the estimated responses by identifying US interest rate shocks based on the effective FFR rather than shadow rates in section 4. Since the dataset includes a majority of the European countries, we further check the consistency of the responses of our model by segregating the countries in our dataset into several groupings based on their collective zones/regions and evaluating the results to see if they vary from the former one. Section 5 concludes the study.
For identification of the US interest rate shocks, we use the monthly dataset from 2007 to 2020 on the Industrial Production Index (IPI), Consumer Price Index (CPI), securities held outright by the Federal Reserve, and shadow interest rates of the United States. As mentioned before, we take the shadow rate as an alternative to the effective FFR to incorporate the effects of QE. To account for the zero lower bound and the stimulus provided by the unconventional monetary policy measures that followed the GFC, we employ WuXia (2016) shadow rate for the US interest rate. Their shadow rate estimates are substantially associated with the QErelated asset holdings by the Federal Reserve, as shown by
We use a monthly dataset from 2007 to 2020 for a total of 29 emerging economies to examine the spillover effects of US interest rate shocks. The Appendix section contains the list of the nations that were considered for the analysis. The dataset consists of each EME’s IPI, CPI, exchange rate, money supply (broad money), and foreign exchange reserves. We use the global database platform CEIC for collecting the data on all the variables. We take the natural log of the variables in the study. The Appendices A–D provides a comprehensive explanation of the creation of the dataset.
To get a more accurate picture of the economic conditions of the EMEs, we incorporate the IPI and CPI in our model. Our focus, however, is on the response of broad money, exchange rates, and total reserves in the emerging markets. The inclusion of emerging market countries in the sample is driven by the availability of comprehensive monthly data. We try to incorporate a wide range of countries from various regions of the world.
Fig.
2.2.1. Identification of US interest rate shocks
In order to study the effects of US interest rates, we need to examine the reasons behind the changes in interest rates since these causes may have a varied effect on EMEs. The causes can be investigated with the use of the Taylor rule. According to it, the interest rate is set at r = f (z) + u, where r is the US interest rate or shadow rate, z is the state of US economy, and u is the monetary policy shock. US interest rates may rise because of z (a strong US economy) or u (monetary policy shocks). Hence, if, for instance, the US interest rate rises due to a monetary policy shock, these unanticipated changes in the US interest rate might result in an outbound capital flow from the EMEs. A different response, however, might occur if the US interest rate increases because the US economy is stable. Basically, a robust US economy may encourage investors to be less risk averse, which would increase capital flows to EMEs (Rey, 2015). The focus of this study is to look at how these unanticipated changes brought on by monetary policy shocks affect the EMEs. To comprehend these effects, we use a US structural VAR (SVAR) model, in which the z variable is a collection of factors representing the stability of the US economy and the residual term, i.e., u, represents the interest rate shocks, as follows:
r_{t} = α _{0} + α_{1} z_{t} + u_{t}, (1)
where r_{t} is the US interest rate, z_{t} includes contemporaneous and lagged values of log IPI, log CPI, log of securities held outright on the balance sheet of the Federal Reserve and lagged values of interest rates^{1}. We estimate the shocks as the unexpected changes in interest rates that are not the result of the stability of the US economy. Our structural identification strategy imposes a Cholesky decomposition of the covariance matrix. In the Cholesky identification criteria, the first factor does not respond to any other variable contemporaneously, the second factor only responds to contemporaneous changes in the first factor, and so on. Although all variables respond to lagged changes in each other. In our SVAR model, we order the shadow rates at the last. The SVAR model includes 6 lags of endogenous variables.
To examine the impact of US interest rates on the emerging markets, we use a panel SVAR model. The PSVAR model in our analysis differs from the general specifications of Pedroni in that we first do not demean the endogenous variables in levels and then take their first difference. Instead, we just take the first difference without centralizing the variables. The rationale behind this is that demeaning the endogenous variables does not have any impact on the first differences anyway. So basically, it does not make sense to first demean the endogenous variables in level and then take the differences. Secondly, we consider a mix of panel data sets and pure time series data. The US interest rate shock in our dataset is not a panel variable but a time series one since it does not change between the crosssection units of our analysis, i.e., countries.
Now, consider a panel composed of, i = 1, ..., N countries, each of which consists of M × 1 vector of observed endogenous variables, y_{it}, for y_{m,it}, m = 1, …, M. We will now take the stationary form to be in terms of the differences of the variables, namely y_{i,t} for facilitating the shortrun restrictions on the dynamics. So, we use the following estimation model for our panel data set:
B_{i} y_{i,t} = A_{i} (L) y_{i,t–}_{1} + ε_{i,t}, (2)
where ${A}_{i}\left(L\right)=\sum _{s=0}^{{s}_{i}}{A}_{i,s}{L}^{s}$ is a lag polynomial allowing for countryspecific lag lengths according to the usual information criteria, i = 1, …, N_{t} and t = 1, …, T_{i}; the i and t subscripts on the time and crosssection dimensions take into account that the panel may be unbalanced; B_{i} is the coefficient matrix; ε_{i,t} is the composite white noise shocks for M × 1 vector of endogenous variables, ε_{m,it}, m = 1, …, M.
The PSVAR by Pedroni (2013) distinguish the composite shocks into common shocks and idiosyncratic shocks. Hence, the common shocks in our model will be: ${\epsilon}_{i,t}={({\mathrm{\epsilon \u0304}}_{it}^{\text{'}},{\mathrm{\epsilon \u0303}}_{it}^{\text{'}})}^{\text{'}},where{\mathrm{\epsilon \u0304}}_{it}^{\text{'}}and{\mathrm{\epsilon \u0303}}_{it}^{\text{'}}$ are M × 1 vectors of common and idiosyncratic white noise shocks respectively. Let Λ_{i} be an M × M diagonal matrix with loading coefficients Λ_{i}, m = 1, …, M. Then,
ε_{i,t} = Λ_{i} ε̄_{it} + ε̃̃_{it}, (3)
As mentioned earlier, our model differs from the general PSVAR model since we have US interest rates as pure time series data. Hence, the vector of endogenous variables i.e., y_{it} = (y_{1,it}, y_{2,it})' is a mix of panel data and pure time series data. This means that for y_{2,it}, the crosssectional average is trivially equal to itself. This makes it logically impossible to use it to identify common and idiosyncratic shocks.
The y_{1,it} panel contains the log IPI, log CPI, log exchange rate, log broad money, and log foreign exchange reserves. We follow Cholesky identification criteria by ordering the shadow rate shocks first so that they do not respond to the contemporaneous changes in the factors of the EMEs. The y_{2,it}, representing the US interest rate shocks, are the residuals that are estimated from the US SVAR model.
To obtain the structural residuals and responses, we estimate a set of N reducedform VARs, one for each country i:
$\u2206{y}_{1,t}={B}_{1}^{1}{A}_{1}\left(L\right){y}_{1,t1}+{\epsilon}_{i,t`}$
$\vdots $
$\u2206{y}_{N,t}={B}_{N}^{1}{A}_{N}\left(L\right)\u2206{y}_{N,t1}+{\epsilon}_{N,{t}^{.}}$ (4)
Fig.
Plots the unanticipated shocks in the US shadow interest rate, 2007–2020 (%).
Note: The unanticipated shocks are identified from the US SVAR model. The residuals from this model are considered as unanticipated shocks in the shadow rates. The largest contractionary shocks can be seen in 2008, depicting the quantitative easing measures. Source: Compiled by the author.
We are interested in the implications of US interest rates on the economic conditions of the emerging markets. The results from the PSVAR model are reported as impulse response functions (IRFs) in the following panels. The IRFs show how a onestandard deviation shock in one factor affects the other factor and how the effect dissipates over a course of time.
As mentioned earlier, since the US interest rate shock is a pure timeseries variable, the idiosyncratic shocks are not relevant as the average effect will be somewhat the same. So, we only pay attention to the common shocks. The following Impulse Response Functions (IRF) show the pointwise median as well as the 25^{th} quantile (as Q1) and 75^{th} quantile (as Q2) confidence intervals (interquantile range) for the response functions of IPI, CPI, exchange rates, broad money, and foreign exchange reserves. It displays the responses of these factors to onestandard deviation shocks.
Fig.
Impulse response functions to shock in US shadow rates.
Note: The figure depicts the impulse response functions of EMEs to a onestandard deviation shock (positive shock) in the US interest shadow rates. The horizontal axis represents the time period or horizon, while the vertical axis represents the percent of responses. The range between Q3 and Q1 is the interquartile range. The pointwise median impulse responses represent the impact of a positive shock on the IPI, CPI, exchange rate, broad money (M3), and foreign exchange reserves, respectively. The IRFs show how these factors behave over a period of time in accordance with the shock in the US shadow rate. Source: Compiled by the author.
We now move to focus on the extent of these responses. The exchange rate depreciates by approximately 0.3% in period 3, with one standard deviation positive shock in the shadow rate. Broad money amounts to 0.41% in period 7. The effect on foreign exchange reserves declines to about 0.2% on the impact and finally it hits the negative peak at 0.4% in period 6. These responses reflect capital flight from emerging markets and demonstrate that interest rate shocks in the US create a safe haven situation for investors, causing them to shift to investing in a strong global currency, resulting in the depreciation of EME domestic currencies against the US dollar, a low monetary aggregate, and lower foreign exchange reserves.
In this section, we extend our analysis by addressing the bias in our dataset. The dataset is heavily skewed towards European countries, so the results can be biased in showing the full picture for nonEuropean countries. Hence, we segregate the economies based on their regions, namely, Asia, Europe, and Latin America. The selection of regions is purely based on the number of countries in that region for which the data is available. The rationale behind this is to check if the results deviate across regions and from our earlier analysis as well. Due to the insufficient country count for the Middle Eastern and SubSaharan African regions, we do not divide them into subparts.
The results for the Asian countries are presented first. Fig.
Impulse response functions to shock in US shadow rate: Asia.
Note: The figure depicts the IRFs to a onestandard deviation shock in US shadow rates for the monthly dataset on EMEs from Asia. The median impulse responses represent the percentage response of an unanticipated positive shock in US shadow rate on the Asian emerging markets over time. Source: Compiled by the author.
Impulse response functions to shock in US shadow rates: Europe.
Note: The figure depicts the IRFs to a onestandard deviation positive shock in the US shadow rates for a monthly dataset on EMEs from Europe. The pointwise median responses show the percentage response of macroeconomic factors of European emerging markets due to a positive shock in the US shadow rates over time. Source: Compiled by the author.
Impulse response functions to shock in US shadow rates: Latin America.
Note: The figure depicts the IRFs to a onestandard deviation positive shock in US shadow rates for a monthly dataset on EMEs from Latin America. The pointwise median responses are the percentage response of macroeconomic factors due to an unanticipated positive shock in the US shadow rate over time. Source: Compiled by the author.
The analysis by dividing the countries in accordance with their regions helps us to see the heterogenous effects on a particular region. The results make it absolutely clear that the interest rates in the US affect the money supply and foreign exchange reserves to decline. It suggests a capital outflow from the emerging markets due to such shocks.
Is using the shadow rate for identifying US interest rate shocks really effective? What difference does it make if we use effective FFR to identify US monetary shocks? We assess these by incorporating effective FFR instead of shadow rate in this segment in the US SVAR model. We then consider the residuals as the unanticipated shocks in the US interest rates and use them in the Panel SVAR by following the same procedure. The baseline specifications for both the US SVAR and Panel SVAR models are exactly the same as in the former model. The estimates from this model are presented in the Appendix section. The responses from this approach substantially differ from our earlier results. According to the IRFs, the exchange rate appreciates on the impact, and broad money as well as foreign exchange reserves rise immediately. However, the exchange rate starts depreciating from period 2. Similarly, broad money estimate also shifts direction and declines from period 2 onwards. Finally, the foreign exchange reserves decline from the impact value, although they do not decline below the zero percent point. These results convey the shock responses to be lagged, that is, we see the effects of unanticipated shocks after period 2 and onwards. This comparison highlights the importance of using the shadow rate to identify shocks because the effective FFR cannot fall below zero, so the effects of monetary stimulus would not be reflected in the FFR due to the liquidity trap. Hence, we believe it might not capture the full picture.
The announcements made by the chair of the Federal Reserve, Jeremy Powell, in January 2022 with regard to raising interest rates in the upcoming years make it imperative to analyse the implications of this policy change on the economic conditions in the EMEs. Our research focuses primarily on the effects on the money supply, foreign exchange reserves, and the exchange rate.
Keeping in mind the ZLB on interest rates following the Great Recession, we consider the WuXia shadow rate instead of the effective FFR. We investigate the effects on 29 EMEs over the period 2007M1 to 2020M12. Our data set includes log values for the Industrial Production Index, Consumer Price Index, exchange rate, foreign exchange reserves, broad money, and US interest rates.
We first identify the unanticipated shocks in a SVAR model of the US economy. We use the Cholesky decomposition identification scheme to find the residuals from this model and identify these as the unanticipated shocks in the US interest rate. We then incorporate these shocks into the Panel SVAR model to capture the spillover effects of US interest rate shocks on the EMEs.
The findings suggest that with one standard deviation in the US interest rates, the IPI of EMEs declines and CPI rises. We see the exchange rates of the domestic currencies of EMEs depreciating as a response to a shock in US interest rates. The broad money declines substantially on the impact, and finally, the response function of the foreign exchange reserve depicts its estimate falling due to unanticipated shocks. The results are somewhat consistent when we employ the methodology across multiple EMEs by segregating them into groups based on their region/zone, i.e., Asia, Europe, and Latin America. The results for Asia and Europe are in line with our key conclusions. However, for Latin America, the results that we see in our main findings are not on the impact, they are rather delayed. Broad money does not decline on the impact; it first rises and then starts to fall after period 2. The effect on the exchange rate is also delayed for the Latin American region. The model only includes a small number of EMs from this region, which could account for the delayed reaction.
The contribution of the study is twofold. Firstly, we investigate the effects of US interest rate shocks on a large panel of 29 EMEs by using the shadow rate instead of FFR. The intuition behind using shadow rates is to capture the effects of unconventional monetary policy adopted after the GFC. Shadow rates could be more efficient for analyzing the effects of US monetary policy shocks as it takes into account largescale asset purchases. Secondly, we focus on the effect on the broad money of the EMs and the heterogeneous effect across multiple regions of the EMEs. A noteworthy point to consider is that these unanticipated shocks do not correspond to a strong and stable US economy. These are the residuals from the model, which is why they are regarded as unanticipated. Moreover, if the changes in interest rates do arise as a result of strong and stable economic conditions in the US, the results may vary as this may induce investors to be risktakers, which would increase the capital inflows towards the emerging markets. However, the unanticipated shocks make investors turn towards a more stable currency, which ultimately results in a capital outflow from the EMEs.
I would like to express my gratitude to my PhD supervisor, Dr Marek Dabrowski, for the consistent support and direction he has provided.
Variable  Construction  Source 
Industrial production index  Natural log of industrial production index  CEIC 
Consumer price index  Natural log of consumer price index  CEIC 
Exchange rate  Natural log of exchange rate against US dollar, period average  CEIC 
Broad money  Natural log of money supply, M_{3}  CEIC 
Foreign exchange reserves  Natural log of foreign exchange reserves  CEIC 
Securities held outright by the Federal Reserve  Natural log of securities held outright by the Federal Reserve  CEIC 
Shadow interest rate  WuXia shadow rate  Federal Reserve Bank of Atlanta 
US interest rate  Effective federal fund rate  FRED 
Impulse response functions to shock in US effective federal fund rate.
Note: The figure depicts the IRFs to a onestandard deviation shock in the effective FFR of the US for a monthly dataset on 29 emerging markets. The identification of unanticipated shocks in US FFR is done in the US SVAR model. The pointwise median responses represent the percentage responses of IPI, CPI, exchange rate, broad money, and foreign exchange reserves of EMEs due to a positive shock in the US FFR over time. Source: Compiled by the author.
The figures provided below are variance decomposition forecast which depicts the importance of a factor in explaining the other. Thus, here we can examine if the US interest rate is even important for explaining the changes in the macroeconomic factors of the EMEs.
Variance decomposition forecast due to shock in US shadow rates.
Note: The figure depicts the variance decomposition forecast for 29 EMEs due to unanticipated shock in the US shadow rates. Each plot shows the percentage of median responses due to shock in the US shadow rate with interquartile range. Source: Compiled by the author.
Variance decomposition forecast due to shock in US shadow rates: Asia.
Note: The figure depicts the variance decomposition forecast for EMEs from Asia due to unanticipated shock in the US shadow rates. Each plot shows the percentage of median responses due to shock in the US shadow rate with interquartile range. Source: Compiled by the author.
Variance decomposition forecast due to shock in US shadow rates: Europe.
Note: The figure depicts the variance decomposition forecast for EMEs from Europe due to unanticipated shock in the US shadow rates. Each plot shows the percentage of median responses due to shock in the US shadow rate with interquartile range. Source: Compiled by the author.
Variance decomposition forecast due to shock in US shadow rates: Latin America.
Note: The figure depicts the variance decomposition forecast for EMEs from Latin America due to unanticipated shock in the US shadow rates. Each plot shows the percentage of median responses due to shock in the US shadow rate with interquartile range. Source: Compiled by the author.
Variance decomposition forecast due to shock in US effective FFR.
Note: The figure depicts the variance decomposition forecast for 29 EMEs due to unanticipated shock in the US effective FFR. Each plot shows the percentage of median responses due to shock in the US shadow rate with interquartile range. Source: Compiled by the author.