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Open Access Publications from the University of California


The Santa Cruz Center for International Economics (SCCIE) is a group of UCSC scholars working in the field of international economics, broadly defined to cover international finance, open economy macroeconomics, international trade, development economics (and linkages with environmental issues), and international political economy.

The objective of SCCIE is to broaden our understanding of international economic issues by sponsoring research, conferences, graduate studies, and the exchange of scholars.

We also support and participate in activities designed to bring greater public awareness and understanding to policy issues involving international economics. SCCIE supports public seminars, publication of working papers, and occasional public forums.

Santa Cruz Center for International Economics

There are 61 publications in this collection, published between 2003 and 2009.
Recent Work (61)

China, Hong Kong, and Taiwan: A Quantitative Assessment of Real and Financial Integration

The status of real and financial integration of China, Hong Kong, and Taiwan is investigated using monthly data on one-month interbank rates, exchange rates, and prices. Specifically, the degree of integration is assessed based on the empirical validity of real interest parity, uncovered interest parity, and relative purchasing power parity. There is evidence these parity conditions tend to hold over longer periods, although they do not hold instantaneously. Overall, the magnitude of deviations from the parity conditions is shrinking over time. In particular, China and Hong Kong appear to have experienced significant increases in integration during the sample period. It is also found that exchange rate variability plays a major role in determining the variability of deviations from these parity conditions.

Evaluating Foreign Exchange Market Intervention: Self-Selection, Counterfactuals and Average Treatment Effects

Studies of central bank intervention are complicated by the fact that we typically observe intervention only during periods of turbulent exchange markets. Furthermore, entering the market during these particular periods is a conscious “self-selection” choice made by the intervening central bank. We estimate the “counterfactual” exchange rate movements that allow us to determine what would have occurred in the absence of intervention and we introduce the method of propensity score matching to the intervention literature in order to estimate the “average treatment effect” (ATE) of intervention. Specifically, we estimate the ATE for daily Bank of Japan intervention over the January 1999 to March 2004 period. This sample encompasses a remarkable variation in intervention frequencies as well as unprecedented frequent intervention towards the latter part of the period. We find that the effects of intervention vary dramatically and inversely with the frequency of intervention: Intervention is effective over the 1999 to 2002 period, ineffective during 2003 and counterproductive during the first quarter of 2004.

What Do We Know about Recent Exchange Rate Models? In-Sample Fit and Out-of-Sample Performance Evaluated

Previous assessments of nominal exchange rate determination have focused upon a narrow set of models typically of the 1970’s vintage, including monetary and portfolio balance models. In this paper we re-assess the in-sample fit and out-of-sample prediction of a wider set of models that have been proposed in the last decade, namely interest rate parity, productivity based models, and "behavioral equilibrium exchange rate" models. These models are compared against a benchmark model, the Dornbusch-Frankel sticky price monetary model. First, the parameter estimates of the models are compared against the theoretically predicted values. Second, we conduct an extensive out-of-sample forecasting exercise, using the last eight years of data to determine whether our in-sample conclusions hold up. We examine model performance at various forecast horizons (1 quarter, 4 quarters, 20 quarters) using differing metrics (mean squared error, direction of change), as well as the “consistency” test of Cheung and Chinn (1998). We find that no model fits the data particularly well, nor does any model consistently out-predict a random walk, even at long horizons. There is little correspondence between how well a model conforms to theoretical priors and how well the model performs in a prediction context. However, we do confirm previous findings that out-performance of a random walk is more likely at long horizons.

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