Publications:
On adjusting the one-sided Hodrick-Prescott Filter, Journal of Money, Credit and Banking, 2024, with Frieder Mokinski and Yves Schüler
Abstract: We show that one should not use the one-sided Hodrick-Prescott filter (HP-1s) as the real-time version of the two-sided Hodrick-Prescott filter (HP-2s): First, in terms of the extracted cyclical component, HP-1s fails to remove low-frequency fluctuations to the same extent as HP-2s. Second, HP-1s dampens fluctuations at all frequencies – even those it is meant to extract. As a remedy, we propose two small adjustments to HP-1s, aligning its properties closely with those of HP-2s: (1) a lower value for the smoothing parameter and (2) a multiplicative rescaling of the extracted cyclical component. For example, for HP-2s with λ = 1,600 (value of smoothing parameter), the adjusted one-sided HP filter uses λ∗ = 650 and rescales the extracted cyclical component by a factor of 1.1513. Using simulated and empirical data, we illustrate the relevance of these adjustments. For instance, financial cycles may appear to be 70% more volatile than business cycles, where in fact volatilities differ only marginally.
Data revisions to German national accounts: Are initial releases good nowcasts?, International Journal of Forecasting, 2019, with Till Strohsal
Abstract: Data revisions to national accounts pose a serious challenge to policy decision making. Well-behaved revisions should be unbiased, small, and unpredictable. This article shows that revisions to German national accounts are biased, large, and predictable. Moreover, with use of filtering techniques designed to process data subject to revisions, the real-time forecasting performance of initial releases can be increased by up to 23%. For total real GDP growth, however, the initial release is an optimal forecast. Yet, given the results for disaggregated variables, the averaging out of biases and inefficiencies at the aggregate GDP level appears to be good luck rather than good forecasting.
Working Papers:
Conditional density forecasting: a tempered importance sampling approach, ECB Working Paper Series No 2745/Decemeber 2022, with Carlos Montes-Galdón and Joan Paredes (R&R at Journal of Applied Econometrics)
Abstract: This paper proposes a new and robust methodology to obtain conditional density forecasts, based on information not contained in an initial econometric model. The methodology allows to condition on expected marginal densities for a selection of variables in the model, rather than just on future paths as it is usually done in the conditional forecasting literature. The proposed algorithm, which is based on tempered importance sampling, adapts the model-based density forecasts to target distributions the researcher has access to. As an example, this paper shows how to implement the algorithm by conditioning the forecasting densities of a BVAR and a DSGE model on information about the marginal densities of future oil prices. The results show that increased asymmetric upside risks to oil prices result in upside risks to inflation as well as higher core-inflation over the considered forecasting horizon. Finally, a real-time forecasting exercise yields that introducing market-based information on the oil price improves inflation and GDP forecasts during crises times such as the COVID pandemic.
Selected as one of the winning papers of the ECB Forecasting Conference PhD competition
Abstract: This paper proposes a Skewed Stochastic Volatility (SSV) model to estimate asymmetric macroeconomic tail risks in the spirit of Adrian et al’s (2019) seminal paper ”Vulnerable Growth”. In contrary to their semi-parametric approach, the SSV model captures the evolution of the conditional density of future US GDP growth in a parametric, non-linear, non-Gaussian state space model. This allows to statistically test the effect of exogenous variables on the different moments of the conditional distribution and provides a law of motion to predict future values of volatility and skewness. The model is estimated using a particle MCMC algorithm. To increase estimation accuracy, I use a tempered particle filter that takes the time-varying volatility and asymmetry of the densities into account. I find that financial conditions affect the mean, variance and skewness of the conditional distribution of future US GDP growth. With a Bayes ratio of 1612.18, the SSV model is strongly favored by the data over a symmetric Stochastic Volatility (SV) model.
Also available on SSRN
A robust approach to tilting: parametric relative entropy, with Carlos Montes-Galdón and Joan Paredes
Abstract: We propose a new methodology, which we call "parametric tilting", to incorporate external information into econometric model-based density forecasts. The new methodology ensures that the final forecast distribution properly reflects the combination of the model and the external information, while at the same time solves shortcomings from traditional entropic tilting methods, which may deliver unreasonable distributions under specific circumstances.
Policy Briefs:
Conditional density forecasting: a tempered importance sampling approach, SUERF Policy Brief, No 547*, with Carlos Montes-Galdón and Joan Paredes
Ongoing Research Projects:
SkSVAR: Tracking structural endogenous risks in the euro area (joint with Carlos Montes-Galdón, and Eva Ortega)
Tempered Particle Learning and Smoothing for non-linear State-Space Models
Local Risk Premia in one-factor yield curve models (joint with Lars Winkelmann)