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On the link between Chinese currency and its inpayments from and outpayments to trading partners: an asymmetric analysis

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A Correction to this article was published on 01 November 2021

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Abstract

A country devalues or allows its currency to depreciate so that it can export more and increase its inpayments and import less to reduce its outpayments. Since exchange rate changes work through affecting traded goods prices and since there is evidence that prices adjust to exchange rate changes in an asymmetric manner, we conjecture that China’s inpayments from trading partners and its outpayments to trading partners also adjust asymmetrically to exchange rate changes. Since estimating asymmetric effects of exchange rate changes requires using nonlinear models, such models yield much more significant results than the traditional linear models. Indeed, this is the case in the trade between China and its 21 major trading partners.

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Notes

  1. It should be noted that several studies have estimated the J-curve effect for China. The J-curve effect basically asserts that trade balance worsens in the short run but improves in the long run, after a currency depreciation. Examples include Brada et al. (1993), Zhang (1998, 1999) and Weixian (1999). Zhang (1999) also reviews China’s exchange rate policy.

  2. Note that some have only considered the link between trade flows and the exchange rate and engaged in Granger causality, assuming the link is symmetric, e.g., Gradojevic and Neely (2008). Some others have assessed asymmetric effects of exchange rate volatility and not the exchange rate itself. Examples include Fang et al. (2009), Yuan and Yang (2016), Bahmani-Oskooee and Aftab (2017) and Bahmani-Oskooee and Nouira (2020).

  3. Note that the estimates of these coefficients must also be negative. Note also that Bahmani-Oskooee (2020) has demonstrated that the estimates of λ0 in (3) and λ’0 in (4) are the same estimates of the coefficient attached to the lagged error-correction term in Engle and Granger (1987) settings.

  4. Another advantage of this method is that it performs better in models that sample size is small. This is demonstrated by Panopoulou, E. and N. Pittis (2004),

  5. Indeed, in applying the F test for cointegration Shin et al. (2014, p. 291) recommend treating the two partial sum variables as one variable so that the critical values of the F test stay at the same high level in both the linear and nonlinear models.

  6. Note that Shin et al (2014, p. 291) recommend using the same critical values for the F but not for the t test when we move from linear to nonlinear models.

  7. For some other applications see Halicioglu (2008), Nusair (2016), Kisswani and Nusair (2014), Durmaz (2015), Baghestani and Kherfi (2015), Aftab et al. (2017) and Hajilee and Niroomand (2019).

  8. We have also tested for model misspecification using Ramsey’s RESET test. Due to lack of space we have reported the results in the notes to Table 3.

  9. Other diagnostics are similar to those of the linear model and need no repetition.

  10. Again, other diagnostics are similar to those of the nonlinear inpayment models and repetition is not needed. Assessing asymmetric effects of the exchange rate on other macrovariables is now becoming a common practice. For example, Koutmos and Martin (2003) test asymmetric effects of exchange rate exposure on stock returns on nine sector indexes across four major countries. Chien-Hsiu (2011) does the same in the Asian emerging stock markets and Bahmani-Oskooee and Saha (2016a, 2016b) in other stock markets.

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Authors and Affiliations

  1. The Center for Research On International Economics, Department of Economics, The University of Wisconsin-Milwaukee, Milwaukee, WI, 53201, USA

    Mohsen Bahmani-Oskooee

  2. School of Economics and Finance, Shanghai International Studies University, Shanghai, 200083, China

    Jia Xu

Authors
  1. Mohsen Bahmani-Oskooee
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  2. Jia Xu
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Correspondence to Jia Xu.

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Valuable comments of two anonymous referees are greatly appreciated. Any remaining error, however, is our own.

The original version of this article was revised: the corresponding author of the article is Dr. Jia Xu. It has been corrected.

Appendices

Appendix

Variable definition and data sources

Quarterly data over the period 2000I–2018IV is used to carry out the empirical analysis. The data come from the following two sources:

A. Direction of Trade Statistics by the IMF.

B. International Financial statistics (IFS).

2.1 Variables

VXJ = China’s exports value to partner I or China’s inpayments from j. Source A.

VMJ = China’s imports value from partner j or China’s outpayments to partner j. Source A.

YCN = China’s aggregate output as measured by an index of real GDP. Source B.

YJ = Trading country j’s output as measured by an index of real GDP of country ‘j’. Source B.

REXi = The real bilateral exchange rate of the Chinese yuan against the currency of partner j. It is defined as REXj = (PCN. NEXj/ Pj) where NEXj is the nominal exchange rate defined as number of units of partner j’s currency per Chinese yuan, PCN is the price level in China (as measured by CPI) and Pj is the price level in country j (also measured by CPI). Thus, a decline in REX reflects a real depreciation of the yuan. All data come from source B.

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Bahmani-Oskooee, M., Xu, J. On the link between Chinese currency and its inpayments from and outpayments to trading partners: an asymmetric analysis. Econ Change Restruct 55, 335–359 (2022). https://doi.org/10.1007/s10644-020-09317-1

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  • DOI: https://doi.org/10.1007/s10644-020-09317-1

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