Corresponding author: Shweta Sikhwal (

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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 (

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 (

The changes in the monetary stance of the US raised concerns in the emerging markets about the spillover effects. The

This study examines the impact of interest rates in the US on the macroeconomic factors of the

The empirical strategy is to use the panel structural Vector Autoregression (

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

The effect of quantitative easing in the US on

Our paper studies the effect of US interest rates on the macroeconomic condition of the

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

For identification of the US interest rate shocks, we use the monthly dataset from 2007 to 2020 on the Industrial Production Index (

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

To get a more accurate picture of the economic conditions of the

Fig.

The monthly effective federal fund rate and Wu-Xia shadow rates, 2007–2020 (%).

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

_{t} = α_{0} + _{1}_{t}_{t}

where _{t}_{t}^{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

Now, consider a panel composed of, _{it}_{m,it}_{i,t}

_{i} y_{i,t} = A_{i}_{i,t–}_{1} + _{i,t}

where _{t}_{i}_{i}_{i,t}_{m,it}

The _{i}_{i}

_{i,t} = Λ_{i} ε̄_{it}_{it}, (3)

As mentioned earlier, our model differs from the general _{it}_{1,it}, _{2,it})' is a mix of panel data and pure time series data. This means that for _{2,it}, the cross-sectional average is trivially equal to itself. This makes it logically impossible to use it to identify common and idiosyncratic shocks.

The _{1,it} panel contains the log _{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

Fig.

Plots the unanticipated shocks in the US shadow interest rate, 2007–2020 (%).

We are interested in the implications of US interest rates on the economic conditions of the emerging markets. The results from the

As mentioned earlier, since the US interest rate shock is a pure time-series 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 (^{th} quantile (as Q1) and 75^{th} quantile (as Q2) confidence intervals (inter-quantile range) for the response functions of

Fig.

Impulse response functions to shock in US shadow rates.

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 non-European 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 Sub-Saharan African regions, we do not divide them into sub-parts.

The results for the Asian countries are presented first. Fig.

Impulse response functions to shock in US shadow rate: Asia.

Impulse response functions to shock in US shadow rates: Europe.

Impulse response functions to shock in US shadow rates: Latin America.

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

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

Keeping in mind the

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

The findings suggest that with one standard deviation in the US interest rates, the

The contribution of the study is two-fold. Firstly, we investigate the effects of US interest rate shocks on a large panel of 29

I would like to express my gratitude to my PhD supervisor, Dr Marek Dabrowski, for the consistent support and direction he has provided.

In defining short run restrictions with Cholesky decomposition, the last ordered variable responds to contemporaneous and lagged values of every endogenous variable in the model. It, however, does not affect other variables contemporaneously, so only the lagged values of the last ordered variable affect the other endogenous variables in the model.

Albania Algeria Azerbaijan Bangladesh Bosnia and Herzegovina Brazil Bulgaria Chile Colombia Costa Rica Croatia Hungary India Kazakhstan Malaysia Mexico Mongolia Montenegro Pakistan Paraguay Philippines Poland Romania Russia Serbia South Africa Tunisia Turkey Ukraine |

Impulse response functions to shock in US effective federal fund rate.

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.

Variance decomposition forecast due to shock in US shadow rates: Asia.

Variance decomposition forecast due to shock in US shadow rates: Europe.

Variance decomposition forecast due to shock in US shadow rates: Latin America.

Variance decomposition forecast due to shock in US effective