Research

Job market paper

Tracking Sectoral Economic Conditions (Github, Google Drive)

Abstract: We construct a novel set of monthly U.S. sector-level economic conditions indices from a small but diverse set of sectoral economic indicators using mixed-frequency dynamic factor models. The resulting indices are driven by a balanced mix of the underlying indicators and display considerable heterogeneity, particularly in the depths, timing and duration of their downturns. Moreover, the sectoral economic conditions are driven by a common factor that explains most fluctuations in the overall economy and is closely related to aggregate production. Meanwhile, the service-providing sectors are additionally driven by a correction factor that handles the heterogeneous impacts of the financial crisis and covid pandemic. Lastly, sector-level GDP growth nowcasts are constructed, which are found to consistently outperform a simple autoregressive benchmark for almost all sectors, especially during the covid pandemic.


Working papers

(with Dick van Dijk), R&R Journal of Applied Econometrics

Abstract: This paper addresses the poor performance of the Expectation-Maximization (EM) algorithm in the estimation of low-noise dynamic factor models, commonly used in macroeconomic forecasting and nowcasting. We show analytically and in Monte Carlo simulations how the EM algorithm stagnates in a low-noise environment, leading to inaccurate estimates of factor loadings and latent factors. An adaptive version of EM considerably speeds up convergence, producing substantial improvements in estimation accuracy. Modestly increasing the noise level also accelerates convergence. A nowcasting exercise of euro area GDP growth shows gains up to 34% by using adaptive EM relative to the usual EM. 


(with Michel van der Wel), R&R Journal of Business & Economic Statistics

Abstract: We propose a smooth shadow-rate version of the dynamic Nelson-Siegel (DNS) model to analyze the term structure of interest rates during a zero lower bound (ZLB) period. By relaxing the no-arbitrage restriction, our shadow-rate model becomes highly tractable with a closed-form yield curve expression. The model easily permits the implementation of readily available DNS extensions such as time-varying loadings, shifting endpoints and the integration of macroeconomic variables. Using U.S. Treasury data, we provide clear evidence of a smooth transition of the yields entering and leaving the ZLB state. Moreover, we show that the smooth shadow-rate DNS model dominates the baseline DNS model in terms of fitting and forecasting the yield curve, while it is also able to produce plausible policy insights at the ZLB through shadow short-rate and liftoff-horizon estimates.


(with Dick van Dijk and Philip Hans Franses), R&R Journal of Money, Credit & Banking

Abstract: We analyze differences in output growth risk with respect to financial conditions across U.S. manufacturing industries. Using a multi-level quantile regression approach, we find strong heterogeneity in growth risk, particularly between the more vulnerable durable goods sector and the more resilient nondurable goods sector. Moreover, we show that industry characteristics significantly explain these differences. Large, or material intensive durable goods producing, or energy intensive nondurable goods producing industries are more vulnerable to adverse financial conditions, while industries engaging in labor hoarding, or with a high capital or overhead labor intensity are less susceptible.


Work in progress

(with Laurent Ferrara and Dick van Dijk)


(with Sam van Meer and Dick van Dijk)