This paper investigates the trade-off between timeliness and quality in nowcasting practices. This trade-off arises when the frequency of the variable to be nowcast, such as GDP, is quarterly, while that of the underlying panel data is monthly; and the latter contains both survey and macroeconomic data. These two categories of data have different properties regarding timeliness and quality: the survey data are timely available (but might possess less predictive power), while the macroeconomic data possess more predictive power (but are not timely available because of their publication lags). In our empirical analysis, we use a modified dynamic factor model which takes three refinements for the standard dynamic factor model of Stock and Watson (2002) into account, namely mixed frequency, pre-selections and co-integration among the economic variables. Our main finding from a historical nowcasting simulation based on euro area GDP is that the predictive power of the survey data depends on the economic circumstances, namely, that survey data are more useful in tranquil times, and less so in times of turmoil.
nowcasting dynamic factor model mixed frequency pre-selections co-integration survey data trade-off between timeliness and quality turmoil and tranquility