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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Previous assessments of forecasting performance of exchange rate models have focused upon a narrow set of models typically of the 1970’s vintage. The canonical papers in this literature are by Meese and Rogoff (1983, 1988), who examined monetary and portfolio balance models. Succeeding works by Mark (1995) and Chinn and Meese (1995) focused on similar models. In this paper we re-assess exchange rate prediction using a wider set of models that have been proposed in the last decade: interest rate parity, productivity based models, and a composite specification incorporating the real interest differential, portfolio balance and nontradables price channels. The performance of these models is compared against two reference specifications – the purchasing power parity and the Dornbusch-Frankel sticky price monetary model. The models are estimated in error correction and first-difference specifications. Rather than estimating the cointegrating vector over the entire sample and treating it as part of the ex ante information set as is commonly done in the literature, we also update the cointegrating vector, thereby generating true ex ante forecasts. 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). No model consistently outperforms a random walk, by a mean squared error measure; however, along a direction-of-change dimension, certain structural models do outperform a random walk with statistical significance. Moreover, one finds that these forecasts are cointegrated with the actual values of exchange rates, although in a large number of cases, the elasticity of the forecasts with respect to the actual values is different from unity. Overall, model/specification/currency combinations that work well in one period will not necessarily work well in another period.

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.

Previous assessments of forecasting performance of exchange rate models have focused upon a narrow set of models typically of the 1970’s vintage. The canonical papers in this literature are by Meese and Rogoff (1983, 1988), who examined monetary and portfolio balance models. Succeeding works by Mark (1995) and Chinn and Meese (1995) focused on similar models. In this paper we re-assess exchange rate prediction using a wider set of models that have been proposed in the last decade: interest rate parity, productivity based models, and a composite specification incorporating the real interest differential, portfolio balance and nontradables price channels. The performance of these models is compared against two reference specifications – the purchasing power parity and the Dornbusch-Frankel sticky price monetary model. The models are estimated in error correction and first-difference specifications. Rather than estimating the cointegrating vector over the entire sample and treating it as part of the ex ante information set as is commonly done in the literature, we also update the cointegrating vector, thereby generating true ex ante forecasts. 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). No model consistently outperforms a random walk, by a mean squared error measure; however, along a direction-of-change dimension, certain structural models do outperform a random walk with statistical significance. Moreover, one finds that these forecasts are cointegrated with the actual values of exchange rates, although in a large number of cases, the elasticity of the forecasts with respect to the actual values is different from unity. Overall, model/specification/currency combinations that work well in one period will not necessarily work well in another period.