Welcome…
…to my website. I am Assistant Professor of Econometrics at the Department of Quantitative Economics, Maastricht University.
In my research, I aim to improve the econometric toolkit to address the pressing puzzles of our time in a data-driven way. In particular, I develop estimation methods for strongly persistent, non-stationary, possibly high-dimensional time series.
You can download my CV here. I am currently working on the NWO-funded project UnveilML, where I combine time series econometrics with machine learning methods to improve estimation and inference for latent time series. Click here for a broad introduction to the project.
Peer-Reviewed Publications
Hartl, T. (2026). Robust trend estimation for strongly persistent data with unobserved memory Journal of Business and Economic Statistics, 44(1), 242–254
Abstract: Economic analysis is often based on pre-filtered, de-trended, or seasonally adjusted data. Underlying filtering methods make strong assumptions about the memory of the series to be filtered, and inference about the memory is limited particularly when persistent cyclical variation overshadows the trend. This article introduces a data-driven method for filtering persistent series that requires no prior assumptions about the memory, thus, is robust to the actual memory of the data. It makes three primary contributions: first, it generalizes unobserved components (UC) models to fractionally integrated trends, making prior assumptions about the trend memory redundant while retaining the advantages of the state space structure of UC models; second, it establishes the asymptotic estimation theory for fractional UC models under mild assumptions; and third, it presents a computationally efficient estimator for the trend by deriving the closed-form solution to the Kalman filter optimization problem.
Hartl, T., Hutter, C., and Weber, E. (2026).Matching for three: big data evidence on search activity of workers, firms, and employment service. Economic Modelling, 155, 107434
Abstract: The standard labour market matching function neglects a substantial variation of hirings. A crucial driver of this latent part is the search intensity of job seekers and firms, as well as the placement intensity of employment agencies. However, there is still a lack of integration of all three search and placement intensities into a unified theoretical framework. Their combined labour market effects have not been estimated so far. We feed all three intensities into the theoretical framework of a labour market matching function to estimate their impact on the job finding rate – effects which are not settled a priori. This could provide important stylised facts for subsequent theory building. For measuring the search and placement intensities, we use big data on online activity obtained from the job exchange of the German Federal Employment Agency and from its internal placement-software. The results show that all three intensities significantly contribute to the variation in job findings beyond vacancies and unemployment. During the COVID-19 crisis, reduced search intensities accounted for 44 percent of lost hirings and 16 percent of the labour market related increase in unemployment.
Haimerl, P., and Hartl, T. (2023).Modeling the COVID-19 infection rates by regime-switching unobserved components models. Econometrics, 11(2)
Abstract: The COVID-19 pandemic is characterized as a recurring sequence of infection ebbs and flows. This article proposes a regime-switching unobserved components (UC) approach to model the trend of COVID-19 infections as a function of this peak and trough pattern. Estimated regime probabilities indicate the prevalence of either an infection up- or down-turning regime for every day of the observational period. This method provides an intuitive real-time analysis of the state of the pandemic as well as a tool to identify structural changes ex post. We find that when applied to U.S. data, the model closely tracks regime changes caused by viral mutations, policy interventions and public behavior.
Hartl, T., and Jucknewitz, R. (2023). Multivariate fractional components analysis. Journal of Financial Econometrics, 21(3), 880–914
Abstract: We propose a setup for fractionally cointegrated time series which is formulated in terms of latent integrated and short-memory components. It accommodates nonstationary processes with different fractional orders and cointegration of different strengths and is applicable in high-dimensional settings. In an application to realized covariance matrices, we find that orthogonal short- and long-memory components provide a reasonable fit and competitive out-of-sample performance compared with several competing methods.
Hartl, T., and Jucknewitz, R. (2022). Approximate state space modelling of unobserved fractional components. Econometric Reviews, 41(1), 75-98
Abstract: We propose convenient inferential methods for potentially nonstationary multivariate unobserved components models with fractional integration and cointegration. Based on finite-order ARMA approximations in the state space representation, maximum likelihood estimation can make use of the EM algorithm and related techniques. The approximation outperforms the frequently used autoregressive or moving average truncation, both in terms of computational costs and with respect to approximation quality. Monte Carlo simulations reveal good estimation properties of the proposed methods for processes of different complexity and dimension.
Working Papers
Hartl, T., Tschernig, R., and Weber, E. (2020). Fractional trends in unobserved components models
Abstract: We develop a generalization of unobserved components models that allows for a wide range of long-run dynamics by modelling the permanent component as a fractionally integrated process. The model allows for cointegration, does not require stationarity, and can be cast in state space form. We derive the Kalman filter estimator for the common fractionally integrated component and establish consistency and asymptotic (mixed) normality of the maximum likelihood estimator. We apply the model to extract a com- mon long-run component of three US inflation measures, where we show that the I(1) assumption is likely to be violated for the common trend.
Hartl, T., Tschernig, R., and Weber, E. (2020). Solving the unobserved components puzzle: A fractional approach to measuring the business cycle
Abstract: Measures for the business cycle obtained from trend-cycle decompositions are puzzling, as they often are noisy, at odds with the NBER chronology, and not well in line with economic theory. We argue that these results are driven by the neglect of fractionally integrated trends in log US real GDP. To account for fractional integration we develop a generalization of trend-cycle decompositions that avoids prior assumptions about the long-run dynamic characteristics and treats the integration order as a random variable. The integration order is jointly estimated with the other model parameters via a quasi maximum likelihood estimator that is shown to be consistent and asymptotically normal. In addition, single-step estimators for the latent components that are identical to the Kalman filter and smoother but computationally superior are derived. We find that log US real GDP is integrated of order around 1.3, the resulting trend-cycle decomposition is in line with the NBER chronology, and the model well explains the puzzling results in the literature that result from model misspecification.
Hartl, T. (2020). Macroeconomic forecasting with fractional factor models
Abstract: We combine high-dimensional factor models with fractional integration methods and derive models where nonstationary, potentially cointegrated data of different persistence is modelled as a function of common fractionally integrated factors. A two-stage estimator, that combines principal components and the Kalman filter, is proposed. The forecast performance is studied for a high-dimensional US macroeconomic data set, where we find that benefits from the fractional factor models can be substantial, as they outperform univariate autoregressions, principal components, and the factor-augmented error-correction model.