Index

1. INTRODUCTION

A country’s exchange rate is the most important macro variable that links domestic economy to the rest of the world. As such, any change in the exchange rate can affect almost all other macro variables. As far as China is considered, indeed, many studies have investigated the impact of changes in the value of Yuan on Chinese economy and its macro variables. Examples include (Bahmani-Oskooee, Bose, & Zhang, 2018; Bahmani-Oskooee, Bose, & Zhang, 2019; Chou, 2000; SaangJoon, 2008) who assessed impact of changes in the value of yuan on China’s trade flows or trade balance in goods; Xu, Bahmani-Oskooee, and Karamelikli (2022)  who investigated the link between Chinese trade in services and the value of yuan; Bahmani-Oskooee, Xi, and Bahmani (2016) who considered the link between value of the yuan and demand for money in China; Jeanneney and Hua (2001) who looked into the nexus between exchange rate and income inequality;  and Hua (2007) who considered the link between the exchange rate and employment.

Bacchetta and Van Wincoop (2000) have argued that in order to improve a country’s trade and welfare, one needs to have a stable exchange rate and China is no exception.

Prior to 2005, China was on a non-uniform managed float against the U.S. Dollar. In 2005, it was decided to peg the yuan to a basket of currencies. Since then, the yuan fluctuates and China managed its value and for that reason, the U.S. has blamed China for manipulating its currency. A developing country that seeks a higher growth is recommended to have more flexible exchange rate (Levy-Yeyati & Sturzenegger, 2003).

Most studies such as those mentioned above that assessed the impact of changes in the value of the yuan on China’s economy, relied upon traditional method of estimating reduced form models through regression analysis. This paper attempts to investigate exchange rate shocks on macroeconomic performance of China over time through a dynamic impulse response function. In doing so we follow Tiwari, Cai, and Chang (2019) who argued that in considering exchange rate shocks we must pau close attention to their time-varying properties. Once we do that, we find that the macroeconomic performance of China is indeed time-varying. To show this, in Section II we present the TVP-VAR Vector Autoregression model that is followed by Section III in which we report our empirical results. The last section concludes the paper.

2. METHODOLOGY ¹

The literature supports two main approaches to investigate changes in the transmission mechanism over time. In the first approach is to estimate the model over two splatted samples. However, this approach embodies a problem in that it is not clear where to divide the sample. This issue is resolved in the second approach in which coefficients are allowed to change over the entire study period. As pointed out by Koop, Leon-Gonzalez, and Strachan (2009), it is more likely that economic conditions in a country changes  gradually rather than abruptly, making the second approach more attractive.

Following the second approach, we use several VAR models to investigate changes in the exchange rate on the Chinese economy. Following Tiwari et al. (2019) we estimate a Bayesian TVP model that includes stochastic volatility. Tiwari et al. (2019) pointed out that ignoring heteroskedasticity of shocks may cause changes in the size of the shocks to be confounded with changes in the transmission mechanism which could produce inconsistent estimates.

Following Tiwari et al. (2019) we consider the following Time-varying VAR model:

Following Franta, Horváth, and Rusnak (2014) and Tiwari et al. (2019)we also employ a stochastic volatility framework by assuming a geometric random walk where we can specify the parameters are specified by:

Furthermore, following Cogley and Sargent (2001) degrees of freedom are multiplied by the OLS estimates on training sample to obtain the appropriate scale matrix.  Paying a close attention to Chinese economy in which exchange rate changes affect all aspect of the economy, we impose some sign restrictions by assuming that exchange rate shocks on GDP, price level and interest rate are all negative, meaning that a depreciation is expected to boost net exports, hence the GDP. It is also expected to be inflationary and result in high interest rate.² Following Franta et al. (2014) and Tiwari et al. (2019), the sign of a response for output, price level and interest rate imposed for four quarters and exchange rate reaction is restricted to only the immediate impact of a shock. We use Givens rotations to implement identification restrictions. This approach is recommended by Fry and Pagan (2011) where the sign restrictions are important in transforming structural residuals into reduced-form residuals. For an orthonormal matrix Q, the following relation must hold:

3. DATA, EMPIRICAL RESULTS AND POLICY IMPLICATIONS

Quarterly data over the 996Q1-2016Q4 period are used to carry out the empirical exercise. ⁴ Except for interest rate, all of the remaining variables are transformed to log differences to render the variables as stationary. ⁵

Exchange rates are said to affect output Gross Domestic Product (GDP) through two channels: the channel through which expenditures are switch from imports to domestically produced goods and the interest rate channel. The combined effects of exchange rate shocks on GDP, CPI, Consumer Price Index. and the interest rate are presented in Figures 1- 3 respectively.  As can be seen, economic activity or output is weekend subsequent to exchange rate appreciation. We observe that exchange rate shocks have their strongest effects on GDP at approximately 4, 8 and 12 quarters subsequent to a shock Figure 1. However, effects on prices are long lasting Figure 2. Unlike Taylor (2000) who suggested that exchange rate pass-through declines over time, we do not find this trend in China.  As for the impact on interest rate, we find that exchange rate appreciation results in lower interest rates Figure 3. Exchange rate appreciation is said to be anti-inflationary policy since it lowers cost of imported goods. This could reduce the burden of monetary policy. From Figure 1 we gather that at different time-horizon, the transmission of exchange rate shocks has become stronger over time, including during the period of the recent financial crisis. This effect is particularly evident for output (GDP). From Figure 2 we learn that reaction of prices to an exchange rate shocks has been stable over time. Figure 3 reveals that the reaction of interest rate appears to be more persistent in recent period than in the past. Our findings suggest that China has conducted an exchange rate policy that is designed to maintain economic stability consistent with economic growth in China.

4. CONCLUSIONS

One of the most powerful policies to combat inflation or to fight recession and promote economic growth is to manipulate the supply of money or change the interest rates. Central banks reduce money supply or raise interest rates to fight inflation. They also do the opposite to fight recessions. Assessing the impact of such policies on macro variables will depend on the time-varying properties of these variables. Recently, Tiwari et al. (2019) investigated the impact of monetary policy in China on a few macro variables by using a TVP-VAR model. In this paper we adopt their approach to assess the effects of China’s exchange rate shocks on its GDP, CPI, and interest rates. Estimates of three-dimensional impulse response functions revealed that exchange rate shocks did affect output (GDP), price level (CPI) and interest rates in a stable manner, both in the short run and long run in China.⁶ The implication of our findings is that in conducting an exchange rate policy to maintain economic stability and growth in China, policy makers better account the nature of time-varying properties of the economy and hence, macro variables. It is shown that once we account for time-varying properties, the outcome is more accurate and consistent with our theoretical expectation, meaning that a stable yuan will result in stable GDP, CPI, and interest rate.

REFERENCES

Bacchetta, P., & Van Wincoop, E. (2000). Does exchange-rate stability increase trade and welfare? American Economic Review, 90(5), 1093-1109.Available at: https://doi.org/10.1257/aer.90.5.1093.

Bahmani-Oskooee, M., Xi, D., & Bahmani, S. (2016). Asymmetric effects of exchange rate changes on the demand for money in China. Applied Economics Letters, 23(15), 1104-1109.Available at: https://doi.org/10.1080/13504851.2015.1137540.

Bahmani-Oskooee, M., Bose, N., & Zhang, Y. (2018). Asymmetric cointegration, nonlinear ARDL, and the J-Curve: A bilateral analysis of China and its 21 trading partners. Emerging Markets Finance and Trade, 54(13), 3131-3151.Available at: https://doi.org/10.1080/1540496x.2017.1373337.

Bahmani-Oskooee, M., Bose, N., & Zhang, Y. (2019). Is there J-curve effect in the commodity trade between China and the U.S.? An asymmetry analysis. The World Economy, 42(10), 2854-2899.Available at: https://doi.org/10.1111/twec.12829.

Chou, W. L. (2000). Exchange rate variability and China's exports. Journal of Comparative Economics, 28(1), 61-79.Available at: https://doi.org/10.1006/jcec.1999.1625.

Cogley, T., & Sargent, T. J. (2001). Evolving post-world war II U.S. Inflation dynamics. NBER Macroeconomics Annual, 16, 1-59.

Franta, M., Horváth, R., & Rusnak, M. (2014). Evaluating changes in the monetary transmission mechanism in the Czech Republic. Empirical Economics, 46(3), 827-842.Available at: https://doi.org/10.1007/s00181-013-0699-0.

Fry, R., & Pagan, A. (2011). Sign restrictions in structural vector autoregressions: A critical review. Journal of Economic Literature, 49(4), 938-960.

Hua, P. (2007). Real exchange rate and manufacturing employment in China. China Economic Review, 18(3), 335-353.Available at: https://doi.org/10.1016/j.chieco.2007.02.001.

Jeanneney, S. G., & Hua, P. (2001). How does real exchange rate influence income inequality between urban and rural areas in China? Journal of Development Economics, 64(2), 529-545.Available at: https://doi.org/10.1016/s0304-3878(00)00149-8.

Koop, G., Leon-Gonzalez, R., & Strachan, R. W. (2009). On the evolution of the monetary policy transmission mechanism. Journal of Economic Dynamics and Control, 33(4), 997-1017.

Levy-Yeyati, E., & Sturzenegger, F. (2003). To float or to fix: Evidence on the impact of exchange rate regimes on growth. American Economic Review, 93(4), 1173-1193.Available at: https://doi.org/10.1257/000282803769206250.

SaangJoon, B. (2008). The bilateral real exchange rates and trade between China and the US. China Economic Review, 19(2), 117-127.Available at: https://doi.org/10.1016/j.chieco.2008.02.003.

Taylor, J. B. (2000). Low inflation, pass-through, and the pricing power of firms. European Economic Review, 44(7), 1389-1408.Available at: https://doi.org/10.1016/s0014-2921(00)00037-4.

Tiwari, A. K., Cai, Y., & Chang, T. (2019). Monetary shocks to macroeconomic variables in China using time-vary VAR model. Applied Economics Letters, 26(20), 1664-1669.Available at: https://doi.org/10.1080/13504851.2019.1591589.

Xu, J., Bahmani-Oskooee, M., & Karamelikli, H. (2022). China’s trade in services and role of the exchange rate: An asymmetric analysis. Economic Analysis and Policy, 74, 747-757.Available at: https://doi.org/10.1016/j.eap.2022.04.006.

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