Nowcasting of the Short-Run Euro-Dollar Exchange Rate with Economic Fundamentals

The vector autoregressive (VAR) model is a popular approach that has been used to analyze the interdependence of economic variables. In the context of exchange rate nowcasting, the model can be used to capture the dynamic relationships between the Euro-Dollar exchange rate and other economic variables such as interest rates, inflation, and economic growth rates. The model can be estimated using historical data and then used to predict the exchange rate based on the current values of the other variables. The VAR model can be augmented with time-varying parameters to capture changes in the relationship between the variables over time. This approach is widely used in the literature and has been shown to be effective in predicting exchange rates.

The dynamic factor model (DFM) is another approach that can be used to nowcast the exchange rate. The DFM is a statistical model that extracts common factors from a large set of economic indicators and uses them to predict the exchange rate. The model can handle a large number of variables and can capture the underlying trends and relationships between the variables. The DFM is particularly useful when there are many economic indicators available, but it may require more data to estimate than the VAR model.

Machine learning techniques such as neural networks and support vector machines can also be used to nowcast the exchange rate. These methods can handle nonlinear relationships between the variables and can capture complex patterns in the data. However, they require large amounts of data to train the models, and their performance may depend on the choice of the input variables and model parameters.
Our results indicate that the specified model outperforms the Random Walk and other structural models at all forecasting horizons.

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