Jan R. Magnus

Published articles

2010 and later

last updated: May 29, 2019

Published articles — Jan R. Magnus

2010

90. Magnus, J.R., O. Powell, and P. Prufer (2010). A comparison of two modelaveraging techniques with an application to growth empirics, Journal ofEconometrics, 154, 139–153. [PDF] (The associated MATLAB and Statafiles and data can be downloaded HERE.)

91. Magnus, J.R. and C. Muris (2010). Specification of variance matrices forpanel data models, Econometric Theory, 26, 301–310. [PDF]

92. Magnus, J.R. and A.A. Peresetsky (2010). The price of Moscow apart-ments, Прикладная Эконометрика (Prikladnaya Ekonometrika, AppliedEconometrics), 17, 89–105. [PDF] Russian translation [PDF]

93. Magnus, J.R. (2010). On the concept of matrix derivative, Journal ofMultivariate Analysis, 101, 2200–2206. [PDF]

2011

94. Magnus, J.R., A. Wan, and X. Zhang (2011). Weighted average leastsquares estimation with nonspherical disturbances and an application tothe Hong Kong housing market, Computational Statistics & Data Analysis,55, 1331–1341. [PDF]

95. Magnus, J.R., B. Melenberg, and C. Muris (2011). Global warming andlocal dimming: the statistical evidence (with discussion and rejoinder),Journal of the American Statistical Association, 106, 452–468. [PDF]

96. Douhou, S., J.R. Magnus, and A. van Soest (2011). The perception ofsmall crime, European Journal of Political Economy, 27, 749–763. [PDF]

97. De Luca, G. and J.R. Magnus (2011). Bayesian model averaging andweighted average least squares: equivariance, stability, and numerical is-sues, The Stata Journal, 11, 518–544. [PDF]

2012

98. Poghosyan, K. and J.R. Magnus (2012). WALS estimation and forecast-ing in factor-based dynamic models with an application to Armenia, In-ternational Econometric Review, 4, 40–58. [PDF] (A somewhat extendedversion including some corrections can be downloaded HERE.)

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Published articles — Jan R. Magnus

Dinner celebrating centenary publication, Faculty Club, Tilburg University, 26 April 2012. Fromleft to right: Jan van Tongeren, Salima Douhou, Dmitry Danilov (three former PhD students),me, and J.S. (Mars) Cramer, my former teacher (picture taken by Masako Ikefuji).

99. Douhou, S., J.R. Magnus, and A. van Soest (2012). Peer reporting andthe perception of fairness, De Economist, 160, 289–310. [PDF]

100. van Tongeren, J.W. and J.R. Magnus (2012). Bayesian integration oflarge SNA data frameworks with an application to Guatemala, Journal ofEconomic and Social Measurement, 37, 277–316. [PDF]

2013

101. Ikefuji, M., R.J.A. Laeven, J.R. Magnus, and C. Muris (2013). Paretoutility, Theory and Decision, 75, 43–57. [PDF]

102. Kumar, K. and J.R. Magnus (2013). A characterization of Bayesian robust-ness for a normal location parameter, Sankhya (Series B), 75, 216–237.[PDF] (The associated MATLAB and Stata files and data can be down-loaded HERE.)

103. Magnus, J.R. (2013). Predicting the past, valedictory lecture, TilburgUniversity. [PDF]

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Published articles — Jan R. Magnus

104. Magnus, J.R., B. Melenberg, C. Muris, and M. Wild (2013). Statisticalclimate-change scenarios, Journal of Environmental Statistics, 5, 1–18.[PDF]

2014

105. Ji, K., J.R. Magnus, and W. Wang (2014). Natural resources, institu-tional quality, and economic growth in China, Environmental and ResourceEconomics, 57, 323–343. [PDF]

106. Klaassen, F. and J.R. Magnus (2014). Analyzing Wimbledon. ITF Coach-ing and Sport Science Review, 62 (22), 6–7. [PDF]

107. Ikefuji, M., J.R. Magnus, and H. Sakamoto (2014). The effect of healthbenefits on climate change mitigation policies, Climatic Change, 126, 229–243. [PDF] (Supplementary background material can be downloaded HERE.)

108. Magnus, J.R. and W. Wang (2014). Concept-based Bayesian model aver-aging and growth empirics, Oxford Bulletin of Economics and Statistics, 76,874–897. [PDF] (Supplementary material is available in our backgrounddocument HERE.)

2015

109. Magnus, J.R. and A. Vasnev (2015). Interpretation and use of sensitivity ineconometrics, illustrated with forecast combinations, International Journalof Forecasting, 31, 769–781. [PDF]

110. Ikefuji, M., R.J.A. Laeven, J.R. Magnus, and C. Muris (2015). Expectedutility and catastrophic consumption risk, Insurance: Mathematics andEconomics, 64, 306–312. [PDF]

2016

111. Magnus, J.R., W. Wang, and X. Zhang (2016). Weighted-average leastsquares prediction, Econometric Reviews, 35, 1040–1074, posted online20 October 2014. [PDF]

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Published articles — Jan R. Magnus

112. Magnus, J.R. and G. De Luca (2016). Weighted-average least squares: Areview, Journal of Economic Surveys, 30, 117–148. [PDF] (The associatedMATLAB and Stata files and data can be downloaded HERE.)

113. Claeskens, G., J.R. Magnus, A.L. Vasnev, and W. Wang (2016). The fore-cast combination puzzle: A simple theoretical explanation, InternationalJournal of Forecasting, 32, 754–762. [PDF]

2018

114. De Luca, G., J.R. Magnus, and F. Peracchi (2018). Balanced variableaddition in linear models, Journal of Economic Surveys, 32, 1183–1200.[PDF]

115. Magnus, J.R. and A.A. Peresetsky (2018). Grade expectations: rationalityand overconfidence, Frontiers in Psychology — Quantitative Psychologyand Measurement, 00, 000–000, doi:10.3389/fpsyg.2017.02346. [PDF]

116. De Luca, G., J.R. Magnus, and F. Peracchi (2018). Weighted-average leastsquares estimation of generalized linear models, Journal of Econometrics,204, 1–17. [PDF]

117. Magnus, G. and J.R. Magnus (2018). The estimation of normal mixtureswith latent variables, Communications in Statistics — Theory and Meth-ods, 00, 000–000. [PDF] Associated software at [zipfile].

2019

118. De Luca, G., J.R. Magnus, and F. Peracchi (2019). Comments on “Un-observable selection and coefficient stability: Theory and evidence” and“Poorly measured confounders are more useful on the left than on theright”, Journal of Business & Economic Statistics, 37, 217–222. [PDF]

119. Ikefuji, M., R.J.A. Laeven, J.R. Magnus, and C. Muris (2019). Expectedutility and catastrophic risk in a stochastic economy-climate model, Journalof Econometrics, 00, 000–000. [PDF]

120. Magnus, J.R. (2019). On using the t-ratio as a diagnostic, in: SpecialIssue “Towards a New Paradigm for Statistical Evidence” (Eds: J. H. Kimand M. I. Bhatti), Econometrics, 7, 000–000. [PDF]

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