Dynamic factor models: Does the specification matter?

Dynamic factor models (DFMs), which assume the existence of a small number of unobserved underlying factors common to a large number of variables, are very popular among empirical macroeconomists. Factors can be extracted using either nonparametric principal components or parametric Kalman filter and smoothing procedures, with the former being computationally simpler and robust against misspecification and the latter coping in a natural way with missing and mixed-frequency data, time-varying parameters, nonlinearities and non-stationarity, among many other stylized facts often observed in real systems of economic variables. This paper analyses the empirical consequences on factor estimation, in-sample predictions and out-of-sample forecasting of using alternative estimators of the DFM under various sources of potential misspecification. In particular, we consider factor extraction when assuming different number of factors and different factor dynamics. The factors are extracted from a popular data base of US macroeconomic variables, widely analyzed in the literature without consensus about the most appropriate model specification. We show that this lack of consensus is only marginally crucial when it comes to factor extraction, but it matters when the objective is out-of-sample forecasting.

Seasonality in COVID-19 times.

COVID-19 hit the economy in an unprecedented way, changing the data generating process of many series. We compare different seasonal adjustment methods through simulations, introducing outliers in the trend and seasonality to reproduce the heterogeneity in the series during COVID-19.

The relationship between road traffic accidents and real economic activity in Spain: common cycles and health issues.

This paper analyses the aggregate relationships between traffic accidents and real economic activity in Spain during the last 30 years. Our general approach is based on two basic assumptions: (1) the number of accidents depends on the use of cars and other exogenous variables, and (2) the level of economic activity affects variation in the stock of cars, as well as degree of utilization. We propose a novel turning point characterization for monthly seasonal data that allows to check whether economic and road accident cycles coincide and, to date the beginning and end of their respective cycles. Empirical results from this section are important in establishing posterior causal models and whether or not economic activity and road accidents have a common component in the long run and a varying lead-lag relationship, depending on the cycles. These models will be the basis to check when Spain will achieve the European Union figures in terms of the fatalities/accidents ratio under different scenarios. Empirical results as well as historical experiences from other European countries proved that reducing fatalities is not only a question of diminishing accidents rates.