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Exchange rate pass-through: evidence based on vector autoregression with sign restrictions
We estimate exchange rate pass-through (PT) into import, producer and consumer price indexes for nine OECD countries, using a method proposed by Uhlig (2005). In a Vector Autoregression (VAR) model, we identify the exchange rate shock by imposing restrictions on the signs of impulse responses for a small subset of variables. These restrictions are consistent with a large class of theoretical models and previous empirical findings. We find that exchange rate PT is less than one at both short and long horizons. Among three price indexes, exchange rate PT is greatest for import price index and smallest for consumer price index. In addition, greater exchange rate PT is found in an economy which has a smaller size, higher import share, more persistent exchange rate, more volatile monetary policy, higher inflation rate, and less volatile aggregate demand.
AUTHORS: Wang, Jian; An, Lian
Shock-Dependent Exchange Rate Pass-Through: Evidence Based on a Narrative Sign Approach
This paper studies shock-dependent exchange rate pass-through for Japan with a Bayesian structural vector autoregression model. We identify the shocks by complementing the traditional sign and zero restrictions with narrative sign restrictions related to the Plaza Accord. We find that the narrative sign restrictions are highly informative, and substantially sharpen and even change the inferences of the structural vector autoregression model originally identified with only the traditional sign and zero restrictions. We show that there is a significant variation in the exchange rate pass-through across different shocks. Nevertheless, the exogenous exchange rate shock remains the most important driver of exchange rate fluctuations. Finally, we apply our model to “forecast” the dynamics of the exchange rate and prices conditional on certain foreign exchange interventions in 2018, which provides important policy implications for our shock-identification exercise.
AUTHORS: An, Lian; Wynne, Mark A.; Zhang, Ren