Research

• Beutner, E., A. Heinemann and S. Smeekes (2024). A residual bootstrap for conditional Value-at-Risk. Journal of Econometrics, 238 (2), 105554. <Code&Software> [Working Paper Version]

• Adamek, R., S. Smeekes and I. Wilms (2023). Local projection inference in high dimensions. Econometrics Journal, forthcoming.<Code&Software>

• Smeekes, S and I. Wilms (2023). bootUR: An R Package for Bootstrap Unit Root Tests. Journal of Statistical Software, 106 (12), 1-39. [Open Access] <Code&Software>

• Beutner, E., Y. Lin, and S. Smeekes (2023). GLS estimation and confidence sets for the date of a single break in models with trends. Econometric Reviews, 42 (2), 195-219. [Open Access]

• Adamek, R., S. Smeekes and I. Wilms (2023). Lasso inference for high-dimensional time series. Journal of Econometrics, 235 (2), 1114-1143. [Open Access] <Code&Software>

• Hecq, A., L. Margaritella and S. Smeekes (2023). Granger causality testing in high-dimensional VARs: a post-double-selection procedure. Journal of Financial Econometrics, 21 (3), 915–958. [Open Access] <Code&Software>

• Beutner, E., A. Heinemann and S. Smeekes (2021). A justification of conditional confidence intervals. Electronic Journal of Statistics 15 (1), 2517-2565. [Open Access]

• Smeekes, S. and E. Wijler (2021). An automated approach towards sparse single-equation cointegration modelling. Journal of Econometrics 221 (1), 247-276. [Open Access by UM Library] <Code&Software>

• Schiavoni, C., F. C. Palm, S. Smeekes and J. van den Brakel (2021). A dynamic factor model approach to incorporate Big Data in state space models for official statistics. Journal of the Royal Statistical Society - Series A 184 (1), 324-353. [Open Access] <Code&Software>

• Friedrich, M., E. Beutner, H. Reuvers, S. Smeekes, J.-P. Urbain, W. Bader, B. Franco, B. Lejeune and E. Mahieu (2020). A statistical analysis of time trends in atmospheric ethane. Climatic Change, 162 (1), 105-125. [Open Access] <Code&Software>

• Friedrich, M., S. Smeekes and J.-P. Urbain (2020). Autoregressive wild bootstrap inference for nonparametric trends. Journal of Econometrics, 214 (1), 81-109. [Open Access by UM Library] <Code&Software>

• S. Smeekes and J. Westerlund (2019). Robust block bootstrap panel predictability tests. Econometric Reviews 38 (9), 1089-1107. [Open Access] <Code&Software>

• Smeekes, S. and E. Wijler (2018). Macroeconomic forecasting using penalized regression methods. International Journal of Forecasting 34 (3), 408-430. [Open Access by UM Library]

• Hurlin, C., S. Laurent, R. Quaedvlieg and S. Smeekes (2017). Risk measure inference. Journal of Business and Economic Statistics 35 (4), 499-512. [Open Access]

• Götz, T. B., A. Hecq and S. Smeekes (2016). Testing for Granger causality in large mixed-frequency VARs. Journal of Econometrics 193 (2), 418-432. [Author Accepted Manuscript]

• Smeekes, S. (2015). Bootstrap sequential tests to determine the order of integration of individual units in a time series panel. Journal of Time Series Analysis 36 (3), 398-415. [Open Access by UM Library] <Code&Software>

• Cavaliere, G., P. C. B. Phillips, S. Smeekes and A. M. R. Taylor (2015). Lag length selection for unit root tests in the presence of nonstationary volatility. Econometric Reviews 34 (4), 512-536. [Open Access by UM Library & Extended Paper] <Code&Software>

• Smeekes, S. and J.-P. Urbain (2014). On the applicability of the sieve bootstrap in time series panels. Oxford Bulletin of Economics and Statistics 76 (1), 139-151. [Open Access by UM Library]

• Smeekes, S. (2013). Detrending bootstrap unit root tests. Econometric Reviews 32 (8), 869-891. [Open Access by UM Library] <Code&Software>

• Smeekes, S. and A. M. R. Taylor (2012). Bootstrap union tests for unit roots in the presence of nonstationary volatility. Econometric Theory 28 (2), 422-456. [Open Access by UM Library] <Code&Software>

• Palm, F. C., S. Smeekes and J.-P. Urbain (2011). Cross-sectional dependence robust block bootstrap panel unit root tests. Journal of Econometrics 163 (1), 85-104. [Open Access by UM Library] <Code&Software>

• Palm, F. C., S. Smeekes and J.-P. Urbain (2010). A sieve bootstrap test for cointegration in a conditional error correction model. Econometric Theory 26 (3), 647-681. [Open Access by UM Library & Extended Paper & Supplementary Material] <Code&Software>

• Palm, F. C., S. Smeekes and J.-P. Urbain (2008). Bootstrap unit root tests: Comparison and extensions. Journal of Time Series Analysis 29 (2), 371-401. [Open Access by UM Library & Author Accepted Manuscript & Supplementary Material] <Code&Software>

• Smeekes, S. and E. Wijler (2020). Unit Roots and Cointegration. In P. Fuleky (Ed.), Macroeconomic Forecasting in the Era of Big Data, Chapter 17, pp. 541-584. Advanced Studies in Theoretical and Applied Econometrics, vol. 52. Springer. [Pre-print] <Code&Software>

• Friedrich, M., L. Margaritella and S. Smeekes (2023) High-dimensional causality for climatic attribution. arXiv e-print 2302.03996. <Code&Software>

• Hecq, A., L. Margaritella and S. Smeekes (2023). Inference in non-stationary high-dimensional VARs. arXiv e-print 2302.01433. <Code&Software>

• Adamek, R., S. Smeekes and I. Wilms (2023). Sparse high-dimensional vector autoregressive bootstrap. arXiv e-print 2302.01233.

• Jassem, A., L. Lieb, R.J. Almeida, N. Baştürk and S. Smeekes (2021). Min(d)ing the President: A text analytic approach to measuring tax news. arXiv e-print 2104.03261. <Code&Software>

• Schiavoni, C., S.J. Koopman, F.C. Palm, S. Smeekes and J. van den Brakel (2021). Time-varying state correlations in state space models and their estimation via indirect inference. Tinbergen Institute Discussion Paper 2021-020/III. <Code&Software>

• Lieb, L. and S. Smeekes (2019). Inference for impulse responses under model uncertainty. arXiv e-print 1709.09583. <Code&Software>

• Beutner, E., A. Heinemann and S. Smeekes (2019). A general framework for prediction in time series models. arXiv e-print 1902.01622.

• Smeekes, S. and J.-P. Urbain (2014). A multivariate invariance principle for modied wild bootstrap methods with an application to unit root testing. GSBE Research Memorandum RM/14/008, Maastricht University. <Code&Software>

My research can be described along several projects or themes which group my papers together (there is obviously overlap). These are described in detail here.