Nowcasting and short-term forecasting
The Central Bank of Malta closely monitors the outlook for the Maltese economy and regularly produces forecasts for the main macroeconomic variables. This process relies on different models and data sources that are designed to produce consistent, transparent, and plausible projections. Staff relies on different models to produce nowcasts and short-term forecasts of economic activity that are in turn used as inputs in the forecasting process.
A dynamic factor model (DFM) is used to nowcast and produce short-term forecasts of the growth rate of real gross domestic product. The methodology, similar to those adopted by several other central banks and institutions around the world, exploits the recent advances in computational and statistical methods. The DFM exploits the information contained in medium and large sized datasets that have become increasingly available to researchers in recent years. Such forecasts reflect and incorporate the flow of information that periodically becomes available. The DFM can handle mixed frequencies that are likely to exist in large datasets used to summarise the Maltese economy and, as an additional advantage, it is able to deal with any path of missing data. The latter feature is of crucial importance as data releases that are used to update the model do not take place in a synchronous way. The forecasting power of the DFM is compared with those of several other models available at the Central Bank of Malta. Overall, the results point towards a higher forecast accuracy of the DFM at very short horizons while, at longer ones (four-quarter ahead), Bayesian Vector Autoregressions (BVARs) appear to be more reliable.
Technical details of the DFM are found in WP/02/2022.
Technical details of the BVAR models are found in WP/04/2018.