45 Key Publications

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  Friedrich, M. and Lin, Y. (2024). Sieve bootstrap inference for linear time-varying coefficient models Journal of Econometrics, 239(1):1--29.

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  Tommasi, D. and Zhang, L. (2024). Bounding Program Benefits When Participation Is Misreported Journal of Econometrics, 238(1):.

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  Tommasi, D. and Zhang, L. (2024). Identifying program benefits when participation is misreported Journal of Applied Econometrics, :1123--1148.

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  Creal, D., Koopman, S.J., Lucas, A. and Zamojski, M. (2024). Observation-driven filtering of time-varying parameters using moment conditions Journal of Econometrics, 238(2):.

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  Naghi, AndreaA., O'Neill, E. and Danielova Zaharieva, M. (2024). The benefits of forecasting inflation with machine learning: New evidence Journal of Applied Econometrics, :.

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  De Vos, I. and Stauskas, O. (2024). Cross-section bootstrap for CCE regressions Journal of Econometrics, 240(1):1--20.

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  Kleen, O. (2024). Scaling and measurement error sensitivity of scoring rules for distribution forecasts Journal of Applied Econometrics, 39(5):833--849.

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  Frazier, DavidT., Renault, E., Zhang, L. and Zhao, X. (2024). Weak Identification in Discrete Choice Models Journal of Econometrics, :.

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  Li, M. and Koopman, S. (2021). Unobserved components with stochastic volatility: Simulation-based estimation and signal extraction Journal of Applied Econometrics, 36(5):614--627.

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  De Vos, I. and Everaert, G. (2021). Bias-Corrected Common Correlated Effects Pooled Estimation in Dynamic Panels Journal of Business and Economic Statistics, 39(1):294--306.

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  Li, M., Koopman, S.J., Lit, R. and Petrova, D. (2020). Long-term forecasting of El Niño events via dynamic factor simulations Journal of Econometrics, 214(1):46--66.

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  Friedrich, M., Smeekes, S. and Urbain, J.P. (2020). Autoregressive wild bootstrap inference for nonparametric trends Journal of Econometrics, 214(1):81--109.

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  Conrad, C. and Kleen, O. (2020). Two are better than one: Volatility forecasting using multiplicative component GARCH-MIDAS models Journal of Applied Econometrics, 35(1):19--45.

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  Koopman, S.J., Lit, R., Lucas, A. and Opschoor, A. (2018). Dynamic discrete copula models for high-frequency stock price changes Journal of Applied Econometrics, 33(7):966--985.

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  Keijsers, B., Diris, B. and Kole, E. (2018). Cyclicality in losses on bank loans Journal of Applied Econometrics, 33(4):533--552.

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  Schwaab, B., Koopman, S. and Lucas, A. (2017). Global Credit Risk: World, Country and Industry Factors Journal of Applied Econometrics, 32(2):296--317.

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  Koopman, S. and Mesters, G. (2017). Empirical Bayes Methods for Dynamic Factor Models Review of Economics and Statistics, 99(3):486--498.

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  Koopman, S., Lit, R. and Lucas, A. (2017). Intraday Stochastic Volatility in Discrete Price Changes: the Dynamic Skellam Model Journal of the American Statistical Association, 112(520):1490--1503.

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  Koopman, S., Lucas, A. and Scharth, M. (2016). Predicting time-varying parameters with parameter-driven and observation-driven models Review of Economics and Statistics, 98(1):97--110.

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  Koopman, S., Lucas, A. and Scharth, M. (2015). Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State Space Models Journal of Business and Economic Statistics, 33(1):114--127.