Academic journal article NBER Reporter

International Finance and Macroeconomics

Academic journal article NBER Reporter

International Finance and Macroeconomics

Article excerpt

The NBER's Program on International Finance and Macroeconomics met in Cambridge on March 21. Roberto Chang, NBER and Rutgers University, and Menzie D. Chinn, NBER and University of Wisconsin, organized this program:

Charles Engel, University of Wisconsin and NBER, and Jian Wang, Federal Reserve Bank of Dallas, International Trade in Durable Goods: Understanding Volatility, Cyclicality, and Elasticities" (NBER Working Paper No. 13814)

Discussant: Paul Bergin, University. of California, Davis and NBER

Yu-chin Chen, University of Washington; Kenneth S. Rogoff, Harvard University and NBER; and Barabara Rossi, Duke University, Can Exchange Rates Forecast Commodity Prices. (NBER Working Paper No. 139011

Discussant: Jeffrey A. Frankel, Harvard University and NBER

A. Craig Burnside, Duke University and NBER; Martin S. Eichenbaum, Northwestern University and NBER; and Sergio Rebelo, Northwestern University and NBER, Understanding the Forward Premium Puzzle: A Microstructure Approach (NBER Working Paper No. 13278)

Discussant: Andrew K. Rose, University of California, Berkeley and NBER

Fabio Ghironi, Boston College and College, "The Domestic and International Effects of Financial Deregulation"

Discussant: Cedric Tille, University of Geneva

Maurice Obstfeld, University of California, Berkeley and NBER; Jay C. Shambaugh, Dartmouth College and NBER; and Alan M. Taylor, University of California, Davis and NBER, Financial Stability, the Trilemma, and International Reserves"

Discussant: Sebnem Kalemli-Ozcan, University of Houston and NBER

Laura Alfaro, Harvard University and NBER, and Fabio Kanczuk, University of Sao Paulo, "Optimal Reserve Management and Sovereign Debt" (NBER Working No. 13216)

Discussant: Vivian Yue, New York University

Data for OECD countries document that: 1) imports and exports are about three times as volatile as GDP; 2) imports and exports are pro-cyclical, and positively correlated with each other; and 3) net exports are counter-cyclical. Standard models fail to replicate the behavior of imports and exports, although they can match net exports relatively well. Inspired by the fact that a large fraction of international trade is in durable goods, Engel and Wang propose a two-country two-sector model, in which durable goods are traded across countries. Their model can match the business cycle statistics on the volatility and co-movement of the imports and exports relatively well. In addition, the model with trade in durables helps us to understand the empirical regularity noted in the trade literature: home and foreign goods are highly substitutable in the long run, but the short-run elasticity of substitution is low. The researchers note that durable consumption also has implications for the appropriate measures of consumption and prices to assess risk-sharing opportunities, as in the empirical work on the Backus-Smith puzzle. The fact that this model can match data better in multiple dimensions suggests that trade in durable goods may be an important element in open-economy macro models.

Chen and her co-authors demonstrate that "commodity currency" exchange rates have remarkably robust power in predicting future global commodity prices, both in-sample and out-of-sample. A critical clement of their in-sample approach is to allow for structural breaks, endemic to empirical exchange rate models, by implementing the approach of Rossi (2005b). Aside from its practical implications, their forecasting results provide perhaps the most convincing evidence to date that the exchange rate depends on the present value of identifiable exogenous fundamentals. They also find that the reverse relationship holds; that is, that commodity prices Granger-cause exchange rates. However, consistent with the vast post-Meese-Rogoff (1983a,b) literature on forecasting exchange rates, they find that the reverse forecasting regression does not survive out-of-sample testing. …

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