World economic aggregates are compiled infrequently and released after considerable lags. There are, however, many potentially relevant series released in a timely manner and at a higher frequency that could provide significant information about the evolution of global aggregates.
The challenge is then to extract the relevant information from this multitude of indicators and combine it to track the real-time evolution of the target variables. We develop a methodology based on dynamic factor models adapted to accommodate for variables with heterogeneous frequencies, ragged ends and missing data. We apply this nowcast methodologies to three variables of interest: global trade in goods, global trade in services and world GDP in real terms. In addition to monitoring these variables in real time, this method can also be used to obtain short-term forecasts based on the most up-to-date values of the underlying indicators.
The main objective of this paper is the development of a nowcasting methodology for world trade.
The methods will also be used to nowcast global economic activity to demonstrate how they can be applied to other target variables. The standard dynamic factor model will be adapted to accommodate the characteristics of trade variables, and it will incorporate the information available in an extensive list of indicators. The nowcasts will be published in future releases of UNCTAD's Handbook of Statistics.
The rest of the paper is organized as follows. The next section will introduce the concept of nowcasting as specifically applied to global trade variables. After that, Section 3 will describe the dynamic factor model and the data transformations required. Section 4 will then present the application of this methodology to our variables of interest and the results obtained. A final section will conclude and introduce some possible areas of future work.