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QE / Econometrics

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  1. 2019
  2. E-pub ahead of print
    Tempelaar, D. (2019). Supporting the less-adaptive student: the role of learning analytics, formative assessment and blended learning. Assessment & Evaluation in Higher Education. https://doi.org/10.1080/02602938.2019.1677855
  3. Published
    Goetz, T. B., & Hecq, A. W. (2019). Granger Causality Testing in Mixed-Frequency VARs with Possibly (Co)Integrated Processes. Journal of Time Series Analysis, 40(6), 914-935. https://doi.org/10.1111/jtsa.12462
  4. Published
    Avenyo, E. K., Konte, M., & Mohnen, P. (2019). The employment impact of product innovations in sub-Saharan Africa: Firm-level evidence. Research Policy, 48(9), [103806]. https://doi.org/10.1016/j.respol.2019.103806
  5. Published
    Rienties, B., Tempelaar, D., Nguyen, Q., & Littlejohn, A. (2019). Unpacking the intertemporal impact of self-regulation in a blended mathematics environment. Computers in Human Behavior, 100, 345-357. https://doi.org/10.1016/j.chb.2019.07.007
  6. Published
    Smeekes, S., & Westerlund, J. (2019). Robust block bootstrap panel predictability tests. Econometric Reviews, 38(9), 1089-1107. https://doi.org/10.1080/07474938.2018.1536102
  7. Published
    Boonstra, H. J., van den Brakel, J., & Das, S. (2019). Multilevel time-series modeling of mobility trends - Final Report. Statistics Netherlands.
  8. Published
    Tempelaar, D. (2019). Digitaal toetsen van wiskunde in het HO. Examens, 2019(3), 3-13. [1].
  9. Published
    Hussain, A. H. M. B., Endut, N., Das, S., Chowdhury, M. T. A., Haque, N., Sultana, S., & Ahmed, K. J. (2019). Does financial inclusion increase financial resilience? Evidence from Bangladesh. Development in Practice, 29(6), 798-807. https://doi.org/10.1080/09614524.2019.1607256
  10. Published
    Lohmeyer, J., Palm, F., Reuvers, H., & Urbain, J-P. (2019). Focused information criterion for locally misspecified vector autoregressive models. Econometric Reviews, 38(7), 763-792. https://doi.org/10.1080/07474938.2017.1409410
  11. Published
    Das, S., & Haslett, S. (2019). A Comparison of Methods for Poverty Estimation in Developing Countries. International Statistical Review, 87(2), 368-392. https://doi.org/10.1111/insr.12314
  12. Published
    Cubadda, G., Hecq, A., & Telg, S. (2019). Detecting Co-Movements in Non-Causal Time Series. Oxford Bulletin of Economics and Statistics, 81(3), 697-715. https://doi.org/10.1111/obes.12281
  13. Published
    Hecq, A., Jacobs, J. P. A. M., & Stamatogiannis, M. P. (2019). Testing for news and noise in non-stationary time series subject to multiple historical revisions. Journal of Macroeconomics, 60, 396-407. https://doi.org/10.1016/j.jmacro.2019.03.003
  14. Published
    Bastürk, N., Borowska, A., Grassi, S., Hoogerheide, L., & van Dijk, H. K. (2019). Forecast density combinations of dynamic models and data driven portfolio strategies. Journal of Econometrics, 210(1), 170-186. https://doi.org/10.1016/j.jeconom.2018.11.011
  15. Published
    Das, S., Rahman, A., Ahamed, A., & Rahman, S. T. (2019). Multi-level models can benefit from minimizing higher-order variations: an illustration using child malnutrition data. Journal of Statistical Computation and Simulation, 89(6), 1090-1110. https://doi.org/10.1080/00949655.2018.1553242
  16. Published
    van Delft, A., & Eichler, M. (2019). Data-Adaptive Estimation of Time-Varying Spectral Densities. Journal of Computational and Graphical Statistics, 28(2), 244-255. https://doi.org/10.1080/10618600.2018.1512866
  17. Published
    Tempelaar, D., Rienties, B., & Nguyen, Q. (2019). Analysing the Use of Worked Examples and Tutored and Untutored Problem-Solving in a Dispositional Learning Analytics Context. In H. Lane, S. Zvacek, & J. Uhomoibhi (Eds.), Proceedings of the 11th International Conference on Computer Supported Education (CSEDU 2019) (Vol. 2, pp. 38-47). (Proceedings of the International Conference on Computer Supported Education, CSEDU). Lisbon, Portugal: SCITEPRESS.
  18. E-pub ahead of print
    Tempelaar, D., Nguyen, Q., & Rienties, B. (2020). Learning Feedback Based on Dispositional Learning Analytics. In M. Virvou, E. Alepis, G. A. Tsihrintzis, & L. C. Jain (Eds.), Machine Learning Paradigms: Advances in Learning Analytics (Vol. 158, pp. 69-89). (Intelligent Systems Reference Library book series; Vol. 158). Cham, Switzerland: Springer.
  19. Published
    Friedrich, M., Beutner, E., Reuvers, H., Smeekes, S., Urbain, J-P., Bader, W., ... Mahieu, E. (2019). Nonparametric estimation and bootstrap inference on trends in atmospheric time series: an application to ethane. (arXiv e-prints; No. 1903.05403). arXiv.org at Cornell University Library.
  20. Published
    Hecq, A., Margaritella, L., & Smeekes, S. (2019). Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure. (arXiv e-prints; No. 1902.10991). arXiv.org at Cornell University Library.
  21. Published
    Beutner, E., Heinemann, A., & Smeekes, S. (2019). A General Framework for Prediction in Time Series Models. (arXiv e-prints; No. 1902.01622). arXiv.org at Cornell University Library.
  22. Published
    Das, S., Chandra, H., & Saha, U. R. (2019). District level estimates and mapping of prevalence of diarrhoea among under-five children in Bangladesh by combining survey and census data. PLOS ONE, 14(2), [0211062]. https://doi.org/10.1371/journal.pone.0211062
  23. Published
    Hou, J., Huang, C., Licht, G., Mairesse, J., Mohnen, P., Mulkay, B., ... Zhen, F. (2019). Does innovation stimulate employment? Evidence from China, France, Germany, and The Netherlands. Industrial and Corporate Change, 28(1), 109-121. https://doi.org/10.1093/icc/dty065
  24. Published
    Dosi, G., & Mohnen, P. (2019). Innovation and employment: an introduction. Industrial and Corporate Change, 28(1), 45-49. https://doi.org/10.1093/icc/dty064
  25. Published
    Schiavoni, C., Palm, F., Smeekes, S., & van den Brakel, J. (2019). A dynamic factor model approach to incorporate Big Data in state space models for official statistics. (arXiv e-prints; No. 1901.11355). arXiv.org at Cornell University Library.
  26. Published
    Bhowmik, K. R., & Das, S. (2019). On selection of an appropriate logistic model to determine the risk factors of childhood stunting in Bangladesh. Maternal and Child Nutrition, 15(1), [12636]. https://doi.org/10.1111/mcn.12636
  27. Published
    Heinemann, A. M. (2019). Bootstrap inference for conditional risk measures. Maastricht: ProefschriftMaken Maastricht. https://doi.org/10.26481/dis.20190620ah
  28. Published
    Bikker, R., van den Brakel, J., Ouwehand, P., van der Stegen, R., & Krieg, S. (2019). Consistent Multivariate Seasonal Adjustment for Gross Domestic Product and its Breakdown in Expenditures. Journal of Official Statistics, 35(1), 9-30. https://doi.org/10.2478/jos-2019-0002
  29. Published
    Staudigl, M., & Mertikopoulos, P. (2019). Convergent Noisy forward-backward-forward algorithms in non-monotone variational inequalities. In G. Giordano (Ed.), 15th IFAC Symposium on Large Scale Complex Systems LSS 2019: Delft, The Netherlands, 26-28 May 2019 (3 ed., Vol. 52, pp. 120-125). (IFAC-PapersOnLine; Vol. 52, No. 3). IFAC Secretariat. https://doi.org/10.1016/j.ifacol.2019.06.021
  30. Published
    Karabiyik, H., Palm, F. C., & Urbain, J-P. (2019). Econometric Analysis of Panel Data Models with Multifactor Error Structures. In P. Aghion, & H. Rey (Eds.), ANNUAL REVIEW OF ECONOMICS, VOL 11, 2019 (Vol. 11, pp. 495-522). (Annual Review of Economics; Vol. 11). Annual Reviews Inc.. https://doi.org/10.1146/annurev-economics-063016-104338
  31. Published
    Radu Ioan Bot, Mertikopoulos, P., Staudigl, M., & Phan Tuo Vong (2019). Forward-backward-forward methods with variance reduction for stochastic variational inequalities. arXiv.org at Cornell University Library.
  32. Published
    Bomze, I. M., Mertikopoulos, P., Schachinger, W., & Staudigl, M. (2019). Hessian barrier algorithms for linearly constrained optimization problems. Siam Journal on Optimization, 29(3), 2100-2127. https://doi.org/10.1137/18M1215682
  33. Published
    Mairesse, J., Pezzoni, M., & Visentin, F. (2019). Impact of family characteristics on the gender publication gap: evidence for physicists in France. Interdisciplinary Science Reviews, 44(2), 204-220. https://doi.org/10.1080/03080188.2019.1603884
  34. Published
    Dytianquin, N. (2019). Technology in the Asian Miracle and Debacle Debates: Applications of and Insights from the Field of Influence Approach to Input-Output Analysis. Maastricht: ProefschriftMaken. https://doi.org/10.26481/dis.20191003nd
  35. Published
    Leymarie, J. (2019). Three essays in financial econometrics. Maastricht: Datawyse / Universitaire Pers Maastricht. https://doi.org/10.26481/dis.20191205jl
  36. Published
    Lohmeyer, J. H. (2019). Time series analysis under model uncertainty. Maastricht: ProefschriftMaken Maastricht. https://doi.org/10.26481/dis.20190524jl
  37. 2018
  38. Published
    Tempelaar, D., Rienties, B., & Nguyen, Q. (2018). A multi-modal study into students’ timing and learning regulation: time is ticking. Interactive Technology and Smart Education, 15(4), 298-313. [2]. https://doi.org/10.1108/ITSE-02-2018-0015
  39. Published
    Bartz-Zuccala, W., Mohnen, P., & Schweiger, H. (2018). The role of innovation and management practices in determining firm productivity. Comparative Economic Studies, 60(4), 502-530. https://doi.org/10.1057/s41294-018-0075-3
  40. Published
    Smeekes, S., & Wijler, E. (2018). An Automated Approach Towards Sparse Single-Equation Cointegration Modelling. (arXiv e-print; No. 1809.08889).
  41. Published
    Beutner, E., Heinemann, A., & Smeekes, S. (2018). A Residual Bootstrap for Conditional Value-at-Risk. (arXiv e-prints; No. 1808.09125).
  42. Published
    Buelens, B., Burger, J., & van den Brakel, J. A. (2018). Comparing Inference Methods for Non-probability Samples. International Statistical Review, 86(2), 322-343. https://doi.org/10.1111/insr.12253
  43. Published
    Friedrich, M., Smeekes, S., & Urbain, J-P. (2018). Autoregressive Wild Bootstrap Inference for Nonparametric Trends. (arXiv e-prints; No. 1807.02357).
  44. Published
    Smeekes, S., & Wijler, E. (2018). Macroeconomic forecasting using penalized regression methods. International Journal of Forecasting, 34(3), 408-430. https://doi.org/10.1016/j.ijforecast.2018.01.001
  45. Published
    Dobbelaere, S., & Mairesse, J. (2018). Comparing micro-evidence on rent sharing from two different econometric models. Labour Economics, 52, 18-26. https://doi.org/10.1016/j.labeco.2018.02.009
  46. Published
    Fu, X., Mohnen, P., & Zanello, G. (2018). Innovation and productivity in formal and informal firms in Ghana. Technological Forecasting and Social Change, 131, 315-325. https://doi.org/10.1016/j.techfore.2017.08.009
  47. Published
    Celbis, M. G., & de Crombrugghe, D. (2018). Internet infrastructure and regional convergence: Evidence from Turkey. Papers in Regional Science, 97(2), 387-409. https://doi.org/10.1111/pirs.12244
  48. Published
    van den Brakel, J. (2018). Statistical challenges of administrative and transaction data by David J. Hand (page 588). Journal of the Royal Statistical Society Series A-Statistics in Society, 181(3), 555-605. https://doi.org/10.1111/rssa.12315
  49. Published
    Bluhm, R., de Crombrugghe, D., & Szirmai, A. (2018). Poverty accounting. European Economic Review, 104, 237-255. https://doi.org/10.1016/j.euroecorev.2018.03.003
  50. Published
    Chevillon, G., Hecq, A., & Laurent, S. (2018). Generating univariate fractional integration within a large VAR(1). Journal of Econometrics, 204(1), 54-65. https://doi.org/10.1016/j.jeconom.2018.01.002
  51. Published
    Mittelmeier, J., Rienties, B., Tempelaar, D., Hillaire, G., & Whitelock, D. (2018). The influence of internationalised versus local content on online intercultural collaboration in groups: A randomised control trial study in a statistics course. Computers & Education, 118(1), 82-95. https://doi.org/10.1016/j.compedu.2017.11.003
  52. Published
    Wilms, I., Barbaglia, L., & Croux, C. (2018). Multiclass vector auto-regressive models for multistore sales data. Journal of the Royal Statistical Society Series C-Applied Statistics, 67(2), 435-452. https://doi.org/10.1111/rssc.12231
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