On the 2026 / 2027 job market

About

I am an Econometrics PhD Fellow at Aarhus University and a member of CoRE.

My research develops econometric methodology and theory that is closely connected to real-world applications. I focus on time series and panel techniques for nonstationary and heterogeneous data, aiming for rigorous and useful methodology that goes hand in hand with my empirical research in climate science.

From January to March 2026 I was a visiting researcher at the University of Oxford, hosted by Sophocles Mavroeidis and James Duffy. Before Aarhus I completed an MSc in Economic and Financial Research at Maastricht University and a BSc in Economics at the University of Regensburg.

Research

Publications

Haimerl, P., Smeekes, S., & Wilms, I. (accepted). Estimation of latent group structures in time-varying panel data models. Econometrics Journal.

Abstract
Replication PAGFL ↗
We consider panel data models where coefficients change smoothly over time and follow a latent group structure, being homogeneous within but heterogeneous across groups. To jointly estimate the group memberships and group-specific coefficient trajectories, we propose FUSE-TIME, a pairwise adaptive group fused-Lasso estimator combined with polynomial spline sieves. We establish consistency, derive the asymptotic distributions of the penalized sieve estimator and its post-selection version, and show oracle efficiency. Monte Carlo experiments demonstrate strong finite-sample performance in terms of estimation accuracy and group identification. An application to the CO2 intensity of GDP highlights the relevance of addressing both cross-sectional heterogeneity and time-variance in empirical exercises within climate econometrics.

Haimerl, P., & Hartl, T. (2023). Modeling COVID-19 infection rates by regime-switching unobserved components models. Econometrics, 11(2), 10. https://doi.org/10.3390/econometrics11020010

Abstract
Replication
The COVID-19 pandemic is characterized by a recurring sequence of peaks and troughs. This article proposes a regime-switching unobserved components (UC) approach to model the trend of COVID-19 infections as a function of this ebb and flow 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 for identifying 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.

Work in progress

Bennedsen, M., Duffy, J., Haimerl, P., Hillebrand, E., Mavroeidis, S., Nielsen, M. Ø., & Wirth, M. Identifying the carbon sink saturation threshold.

Abstract
Recent evidence suggests that the marginal carbon dioxide (CO2) uptake by the land and ocean sinks has declined. The implications for future atmospheric CO2 depend on the shape of this decline: sink uptake as a function of the atmospheric CO2 stock. We estimate this relationship using endogenously nonlinear cointegrated VARs that impose physically motivated restrictions and jointly determine emissions, atmospheric CO2, and land and ocean uptake. The approach is unique in its ability to accommodate contemporaneous interactions as well as endogenous nonlinearity: changes in sink efficacy are triggered by atmospheric CO2, which is itself determined within the system. Using annual Global Carbon Budget data from 1959 to 2023, we employ complementary threshold and smooth sieve specifications. The threshold specification estimates the atmospheric CO2 level at which sink efficacy changes, while the sieve traces its evolution flexibly over the observed range of the atmospheric stock. We test whether sink efficacy is constant, estimate where and how sharply it declines, and project the resulting carbon-cycle dynamics under CMIP emissions scenarios.

Haimerl, P. Evidence of sink rate decline despite a near-constant airborne fraction.

Abstract
The terrestrial biosphere (land sink) and oceanic biosphere (ocean sink) have absorbed more than half of all anthropogenic carbon dioxide (CO2) emissions in recent decades. However, mounting evidence suggests that, as atmospheric CO2 rises, the rate at which the sinks absorb CO2 declines. We analyse sink robustness to rising atmospheric CO2 in a nonlinear state-space model and show that the common affine sink uptake specification, an intercept plus slope in atmospheric CO2, mechanically imposes a constant sink rate, masking sink weakening. Correcting this, we find a sink rate decline of just below 40% from 1960 to 2023. Furthermore, we highlight that constancy of the airborne fraction (AF), the percentage of emissions remaining in the atmosphere, does not imply sink robustness without additional strong assumptions.

Haimerl, P., Lembrechts, J., Schiffelers, L., Smeekes, S., & Wilms, I. Trends in the in situ and free-air temperature offset across 20 years and a large number of locations.

Abstract
Microclimates can buffer or amplify macroclimate warming, shaping terrestrial thermal exposure. Yet heterogeneous long-term change in the offset between near-surface and free-air temperatures remains poorly resolved across environments. We analyse near-surface temperature offsets at 304 distinct locations worldwide from 2000 to 2019; these locations span diverse land-cover classes and multiple continents. Our statistical model jointly estimates smooth nonlinear trends and learns groups of locations with common trajectories and seasonal patterns, without imposing regional or habitat classes. By pooling locations, the model reduces hundreds of location histories to a small number of interpretable response types. We identify three latent trajectories, one of which reverses direction near the middle of the study period. These results provide a broad account of long-term change in near-surface temperature offsets and show that it cannot be summarized by a single linear direction or rate.

Software

PAGFL R package · v1.1.4

Identifies latent group structures and estimates group-specific coefficients in panel data models in a single step, implementing the pairwise adaptive group fused Lasso of Mehrabani (2023) together with its time-varying extension, FUSE-TIME, following Haimerl et al. (2026).

CRAN GitHub Paper ↗

BTtest R package · v0.10.3

Estimates the number of common factors in large nonstationary panels via the Barigozzi and Trapani (2022) test, separating trending, zero-mean I(1), and zero-mean I(0) factors, with complementary measures from Bai (2004).

CRAN GitHub

Teaching

Winter 2026

Applied Machine Learning Tutorial · MSc · 10 ECTS · Aarhus University

Winter 2026

Econometrics 1 (3630) Tutorial · BSc · 10 ECTS · Aarhus University

Summer 2025

Econometrics (2648) Tutorial · BSc · 10 ECTS · Aarhus University

Ongoing

Thesis supervision Bachelor’s and Master’s level · Aarhus University

Talks

2026EMCC X, Aalborg; ACE James G. MacKinnon 75th Birthday Conference, Aarhus▪; internal seminar, Copenhagen Business School; flash presentation seminar, University of Oxford.

202519th CFE-CMStatistics, London†; Danish Graduate Programme in Economics, Køge; CoRE members’ retreat, Sandbjerg▪; internal seminar, Aarhus University; EMCC IX, Victoria (BC); internal seminar, Bielefeld University; Workshop in Time Series Econometrics XV, Zaragoza.

2024Danish Graduate Programme in Economics, Middelfart; Aarhus Workshop in Econometrics V, Aarhus▪; EMCC VIII, Cambridge; Netherlands Econometric Study Group, Maastricht▪.

† invited    ▪ poster

Awards

2025Marcelo Reyes Award for the best presentation by a junior researcher, Workshop in Time Series Econometrics XV, Zaragoza.

2024Master Student Prize for an excellent Master’s thesis, one of two at the School of Business and Economics, Maastricht University.

2023Christa-Lindner Prize for the best Bachelor’s thesis of the Economics department, University of Regensburg.

2021Deutschlandstipendium, a scholarship funded by the German Federal Ministry of Education and Research.

2020Honors Bachelor Programme, admission to the elite programme of the Economics department, University of Regensburg.

Referee Service

Journal of Econometrics.

Contact

paul.haimerl@econ.au.dk

Department of Economics and Business Economics Aarhus University Building 1816, room 318 Universitetsbyen 51 8000 Aarhus C, Denmark