Modelling and Forecasting of Tunisian Current Account: Aggregate versus Disaggregate Approach

While there is considerable literature attempting to model current account, there is fewer studies to forecast current account balance. This study gives a comprehensive way to model and predict current account deficit (CAD) by evaluating the forecasting performance of direct and indirect approach. At disaggregated level, I use two variants to model current account component; in the first alternative I apply different ARIMA model with exogenous variables (ARIMA-X) to account for the pattern of the data and exogenous factors. In the second alternative, I integrate the cointegration relationship between exports and imports with ARIMA-X models. With respect to the direct approach, I use error correction model to allow for dynamics in current account. The data used spans from January 2000 to December 2014 and comes from Central Bank of Tunisia, Tunisian National Institute of Statistic, and OECD database. I find that for one-step ahead forecast ARIMA-X and reduced form model produce accurate forecast but with respect to dynamic forecast, direct method is more accurate comparatively to ARIMA-X. When cointegrating relationship between exports and imports is combined with ARIMA-X models, indirect approach outperforms direct approach. I also show that, as volatility of underlying components increase disaggregate approach using time series models become less reliable. In addition, I found that current account is mainly affected by local GDP, trade openness, fiscal deficit, exchange rate, credit to private sector and partner GDP. Estimation of ECM indicates that persistent effect is high and can take more than three quarters to die out. In addition I assess the performance of direct and indirect approach over time using naïve approach as benchmark. It appears that the MSE of naïve approach lies between direct and indirect approach in average up to horizon 12, but then worsen.