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Exchange Rate Pass-Through to Consumer Prices: The Increasing Role of Energy Prices

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Abstract

A number of researchers have found that the rate of exchange rate pass-through (ERPT) to domestic prices has declined substantially over the last few decades. We revisit this claim of a shrinking exchange rate effect on the Consumer Price Index (CPI) in a vector autoregressive (VAR) model for US macroeconomic data under the current floating exchange rate regime. Our VAR approach nests the conventional single equation method and reveals statistically significant evidence of ERPT to the CPI only during later observations, sharply contrasting with previous findings. After confirming structural breaks in ERPT via statistical tests by Hansen (Journal of Economic Perspectives, 15(4), 117–128, 2001) and Qu and Perron (Econometrica, 75(2), 459–502, 2007), we seek the source with disaggregated level CPIs, and pin down a key role of energy prices. US energy imports increased from the 1990s until the recent recession. This market changes magnify the effects of the exchange rate shocks on domestic energy prices, resulting in greater responses of the total CPI via the energy price channel.

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Notes

  1. We demean the VAR prior to estimations.

  2. The information set Ωt is adaptive in the sense that \({\Omega }_{j}\supseteq {\Omega }_{j-1},\forall j\).

  3. The IRF estimates \(E\left ({\Delta } y_{t+j}|u_{\Delta s,t}=1,{\Omega }_{t-1}\right ) -E\left ({\Delta } y_{t+j}|{\Omega }_{t-1}\right ) \) are also numerically identical even if Δyt and Δpt switch their location.

  4. Additional control variables can be added in the right hand side of the equation, which can be also easily nested by VAR models following the procedure in the present paper.

  5. One may estimate the standard error of (5) via the Delta method given the least squares estimation of (4), while our structural VAR approach allows us to estimate it using nonparametric bootstrap replications.

  6. When the ordering is \(\left [ {\Delta } s_{t}~{\Delta } p_{t}~{\Delta } y_{t}\right ]^{\prime }\), (7) remains the same but βy,0 = 0 by construction.

  7. We also implemented the same analysis with a 30-year window scheme. Results are qualitatively similar and are available upon requests.

  8. Alternatively, the Wald or the Likelihood Ratio statistics can be used.

  9. These results are available upon request.

  10. We obtain the long-run rate of ERPT from the IRF approach by taking the value of η(20). j = 20 (quarter) seems to be sufficient for the IRF to become stabilized.

  11. We obtained the data from the U.S. Energy Information Administration website.

  12. Detailed results are available upon requests.

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Correspondence to Ying Lin.

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Appendix

Appendix

Table 5 ERPT Estimates: IRF Approach \(\mathbf {x}_{t}={\sum }_{j=1}^{q}\mathbf {B}_{j}\mathbf {x}_{t-j}+\mathbf {Cu} _{t},~\mathbf {x}_{t}=[{\Delta } s_{t}~~{\Delta } y_{t}~~{\Delta } p_{t}]^{\prime }\) \(\phi (j)=E\left ({\Delta } p_{t+j}|u_{\Delta s,t}=1,{\Omega }_{t-1}\right ) -E\left ({\Delta } p_{t+j}|{\Omega }_{t-1}\right ) ,\) \(\eta (j)={\sum }_{s=0}^{j} \phi (s) \)
Table 6 ERPT Estimates: VAR Coefficients Approach \(\mathbf {x}_{t}={\sum }_{j=1}^{q}\mathbf {B}_{j}\mathbf {x}_{t-j}+\mathbf {Cu} _{t},~\mathbf {x}_{t}=[{\Delta } s_{t}~~{\Delta } y_{t}~~{\Delta } p_{t}]^{\prime }\) \({\Delta } p_{t}={\sum }_{j=1}^{q}\beta _{p,j}{\Delta } p_{t-j}+{\sum }_{j=0}^{q} \beta _{s,j}{\Delta } s_{t-j}+{\sum }_{j=0}^{q}\beta _{y,j}{\Delta } y_{t-j} +\varepsilon _{t}\) \(ERPT=\frac {{\sum }_{j=0}^{q}\beta _{s,j}}{1-{\sum }_{j=1}^{q}\beta _{p,j}}\)

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Kim, H., Lin, Y. & Thompson, H. Exchange Rate Pass-Through to Consumer Prices: The Increasing Role of Energy Prices. Open Econ Rev 32, 395–415 (2021). https://doi.org/10.1007/s11079-020-09601-7

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