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Eva Janssens

@evajanster.bsky.social

Assistant Professor at the University of Michigan, interested in econometrics and macroeconomics. UvA/TI/EUR alumna :)

258 Followers  |  197 Following  |  7 Posts  |  Joined: 07.11.2024  |  1.7197

Latest posts by evajanster.bsky.social on Bluesky

NEP/RePEc link to paper

A Test for Jumps in Metric-Space Conditional Means

02.09.2025 02:45 β€” πŸ‘ 0    πŸ” 1    πŸ’¬ 0    πŸ“Œ 0
Efficient Estimation of Nonlinear DSGE Models This paper introduces a computationally efficient method for full-information estimation of nonlinear Dynamic Stochastic General Equilibrium (DSGE) models. In c

New! Efficient Estimation of Nonlinear DSGE Models, w/ Sean McCrary
πŸ“„ papers.ssrn.com/sol3/papers....

We propose a broadly applicable computationally efficient method for full-information estimation of nonlinear DSGE models. It avoids two key bottlenecks: global solutions and nonlinear filters.

06.06.2025 18:41 β€” πŸ‘ 6    πŸ” 3    πŸ’¬ 1    πŸ“Œ 0

This approach captures nonlinearities such as state dependence while remaining tractable and fast.
β€’ DMP model: accuracy on par with global methods
β€’ Application: state-dependent monetary policy transmission

Extensions: regime-switching, SV, and heterogeneous agents. Feedback welcome!

06.06.2025 18:41 β€” πŸ‘ 1    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0

Unlike standard approach that separates solution and likelihood evaluation, our method integrates the solution within the filtering step.
How? We construct local linear approximations dynamically along the forecasted state path, enabling likelihood-based inference via a time-varying Kalman filter..

06.06.2025 18:41 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0
Efficient Estimation of Nonlinear DSGE Models This paper introduces a computationally efficient method for full-information estimation of nonlinear Dynamic Stochastic General Equilibrium (DSGE) models. In c

New! Efficient Estimation of Nonlinear DSGE Models, w/ Sean McCrary
πŸ“„ papers.ssrn.com/sol3/papers....

We propose a broadly applicable computationally efficient method for full-information estimation of nonlinear DSGE models. It avoids two key bottlenecks: global solutions and nonlinear filters.

06.06.2025 18:41 β€” πŸ‘ 6    πŸ” 3    πŸ’¬ 1    πŸ“Œ 0
Preview
2025 Summer School on Structural Estimation in Corporate Finance

rodneywhitecenter.wharton.upenn.edu/2025-summer-...

12 more days to apply to our summer school in structural estimation! It's a hands-on course in which you learn how to do SMM and then work on a problem set with your classmates. Open to PhD students in finance, accounting, econ. And faculty.

05.03.2025 14:13 β€” πŸ‘ 24    πŸ” 2    πŸ’¬ 4    πŸ“Œ 0

Wait, there are people that have zero errors in their latex file?! πŸ™ƒ

26.02.2025 16:20 β€” πŸ‘ 3    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0

Congratulations!

15.02.2025 04:02 β€” πŸ‘ 1    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
Post image

We propose a double robust Lagrange multiplier (DRLM) test for testing the hypotheses specified on the parameters of interest. The DRLM test is robust to both misspecification and weak identification, which are common in applied work www.econometricsociety.org/publications...

05.12.2024 21:48 β€” πŸ‘ 2    πŸ” 1    πŸ’¬ 0    πŸ“Œ 0
EJM - Econ Job Market

To all job market candidates in macro: University of Copenhagen has a 6 year tenure-track assistant professor position. See more here: econjobmarket.org/positions?show…
If you have any questions, please contact me.

19.11.2024 14:08 β€” πŸ‘ 6    πŸ” 3    πŸ’¬ 0    πŸ“Œ 1

Congratulations!

19.11.2024 13:50 β€” πŸ‘ 1    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0

Very interesting!

18.11.2024 22:54 β€” πŸ‘ 1    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
Preview
Tinbergen Institute Job Market Candidates 2024-2025 Tinbergen Institute is proud to support our PhD students in successfully preparing for the academic job market and promote their work.

Introducing 2024-2025 Job Market Candidates of Tinbergen Institute!

All candidates & their personal websites can be viewed on
our website: tinbergen.nl/job-market-c...

14.11.2024 09:27 β€” πŸ‘ 16    πŸ” 10    πŸ’¬ 0    πŸ“Œ 1

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