Journal articles
Snoke, J., Meijer, E., Phillips, D., Wilkens, J., & Lee, J. (2024). Synthesizing surveys with multiple units of observation: An application to the Longitudinal Aging Study in India. Journal of Survey Statistics and Methodology. (Accepted for publication)
Petrosyan, S., et al. (2024). The association of multilingualism with diverse language families and cognition among adults with and without education in India. Neuropsychology. (Accepted for publication)
Gao, H., et al. (2024). Early identification of cognitive impairment in community environments through modeling subtle inconsistencies in questionnaire responses: Machine learning model development and validation. JMIR Formative Research. (Accepted for publication) https://doi.org/10.2196/54335
Schneider, S., et al. (2024). Can you tell people’s cognitive ability level from their response patterns in questionnaires? Behavior Research Methods, 56, 6741–6758. https://doi.org/10.3758/s13428-024-02388-2
Angrisani, M., et al. (2024). Modifiable risk factors for dementia in India: Revisiting estimates and reassessing prevention potential. BMJ Public Health, 2, e001362. https://doi.org/10.1136/bmjph-2024-001362
Lee, J., & Meijer, E. (2024). Different reasonable methodological choices can lead to vastly different estimates of the economic burden of diseases [Invited comment]. The Lancet Healthy Longevity, 5, e504–e505. https://doi.org/10.1016/S2666-7568(24)00130-2
Nichols, E., et al. (2024). Considerations for the use of the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) in cross-country comparisons of cognitive aging and dementia. Alzheimer’s & Dementia, 20, 4635–4648. https://doi.org/10.1002/alz.13895
Geldsetzer, P., Chang, A. Y., Meijer, E., Sudharsanan, N., Charu, V., Kramlinger, P., & Haarburger, R. (2024). Interviewer biases in medical survey data: The example of blood pressure measurements. PNAS Nexus, 3(3), pgae109. https://doi.org/10.1093/pnasnexus/pgae109
Angrisani, M., Casanova, M., Lee, J., & Meijer, E. (2024). The economic burden of dementia in India. AEA Papers and Proceedings, 114, 418–422. https://doi.org/10.1257/pandp.20241061
Schneider, S., et al. (2024). Cognitive functioning and the quality of survey responses: An individual participant data meta-analysis of 10 epidemiological studies of aging. Journals of Gerontology: Psychological Sciences, 79(5), gbae030. https://doi.org/10.1093/geronb/gbae030
Kobayashi, L. C., et al. (2024). Cross-national comparisons of later-life cognitive function using data from the Harmonized Cognitive Assessment Protocol (HCAP): Considerations and recommended best practices. Alzheimer’s & Dementia, 20, 2273–2281. https://doi.org/10.1002/alz.13694
Hernandez, R., et al. (2024). Attrition from longitudinal ageing studies and performance across domains of cognitive functioning: An individual participant data meta-analysis. BMJ Open, 14, e079241. https://doi.org/10.1136/bmjopen-2023-079241
Gross, A. L., et al. (2024). Prevalence of DSM-5 mild and major neurocognitive disorder in India: Results from the LASI-DAD. PLoS One, 19, e0297220. https://doi.org/10.1371/journal.pone.0297220
Khobragade, P., et al. (2024). Performance of the Informant Questionnaire on Cognitive Decline for the Elderly (IQCODE) in a nationally representative study in India: The LASI-DAD study. International Psychogeriatrics, 36, 177–187. https://doi.org/10.1017/S1041610222000606
Schneider, S., et al. (2023). Using item response times in online questionnaires to detect mild cognitive impairment. Journals of Gerontology: Psychological Sciences, 78, 1278–1283. https://doi.org/10.1093/geronb/gbad043
Lee, J., et al. (2023). Prevalence of dementia in India: National and state estimates from a nationwide study. Alzheimer’s & Dementia, 19, 2898–2912. https://doi.org/10.1002/alz.12928
Lee, J., et al. (2023). Deep phenotyping and genomic data from a nationally representative study on dementia in India. Scientific Data, 10, 45. https://doi.org/10.1038/s41597-023-01941-6
Junghaenel, D. U., et al. (2023). Inferring cognitive abilities from response times to web-administered survey items in a population-representative sample. Journal of Intelligence, 11(1), 3. https://doi.org/10.3390/jintelligence11010003
Gatz, M., Schneider, S., Meijer, E., Darling, J. E., Orriens, B., Liu, Y., & Kapteyn, A. (2022). Identifying cognitive impairment among older participants in a nationally representative internet panel. Journals of Gerontology: Psychological Sciences. https://doi.org/10.1093/geronb/gbac172
Meijer, E., Casanova, M., Kim, H., Llena-Nozal, A., & Lee, J. (2022). Economic costs of dementia in 11 countries in Europe: Estimates from nationally representative cohorts of a panel study. The Lancet Regional Health – Europe, 20, 100445. https://doi.org/10.1016/j.lanepe.2022.100445
Schneider, S., Junghaenel, D. U., Meijer, E., Zelinski, E. M., Jin, H., Lee, P.-J., & Stone, A. A. (2022). Quality of survey responses at older ages predicts cognitive decline and mortality risk. Innovation in Aging, 6(3), 1–11. https://doi.org/10.1093/geroni/igac027
Schneider, S., Jin, H., Orriens, B., Junghaenel, D. U., Kapteyn, A., Meijer, E., & Stone, A. A. (2022). Using attributes of survey items to predict response times may benefit survey research. Field Methods. https://doi.org/10.1177/1525822X221100904
Liu, Y., Schneider, S., Orriens, B., Meijer, E., Darling, J. E., Gutsche, T., & Gatz, M. (2022). Self-administered web-based tests of executive functioning and perceptual speed: Measurement development study with a large probability-based survey panel. Journal of Medical Internet Research, 24(5), e34347. https://doi.org/10.2196/34347
Meijer, E., Spierdijk, L., & Wansbeek, T. (2022). Moment conditions for the quadratic regression model with measurement error. Econometric Reviews, 41, 749–774. https://doi.org/10.1080/07474938.2022.2052666
Lee, J., Wilkens, J., Meijer, E., Sekher, T. V., Bloom, D. E., & Hu, P. (2022). Hypertension awareness, treatment, and control and their association with healthcare access in the middle-aged and older Indian population: A nationwide cohort study. PLoS Medicine, 19(1), e1003855. https://doi.org/10.1371/journal.pmed.1003855
Schneider, S., Junghaenel, D. U., Zelinski, E. M., Meijer, E., Stone, A. A., Langa, K. M., & Kapteyn, A. (2021). Subtle mistakes in self-report surveys predict future transition to dementia. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 13(1), e12252. https://doi.org/10.1002/dad2.12252
Jung, D., Lee, J., & Meijer, E. (2021). Revisiting the effect of retirement on cognition: Heterogeneity and endowment. Journal of the Economics of Ageing, 21, 100361. https://doi.org/10.1016/j.jeoa.2021.100361
Jin, H., Chien, S., Meijer, E., Khobragade, P., & Lee, J. (2021). Learning from clinical consensus diagnosis in India to facilitate automatic classification of dementia: Machine learning study. JMIR Mental Health, 8(5), e27113. https://doi.org/10.2196/27113
Lee, J., Meijer, E., Phillips, D., & Hu, P. (2021). Disability incidence rates for men and women in 23 countries: Evidence on health effects of gender inequality. Journals of Gerontology: Medical Sciences, 76, 328–338. https://doi.org/10.1093/gerona/glaa288
Meijer, E., Oczkowski, E., & Wansbeek, T. (2021). How measurement error affects inference in linear regression. Empirical Economics, 60, 131–155. https://doi.org/10.1007/s00181-020-01942-z
Angrisani, M., Lee, J., & Meijer, E. (2020). The gender gap in education and late-life cognition: Evidence from multiple countries and birth cohorts. Journal of the Economics of Ageing, 16, 100232. https://doi.org/10.1016/j.jeoa.2019.100232
Gross, A. L., Khobragade, P. Y., Meijer, E., & Saxton, J. A. (2020). Measurement and structure of cognition in the Longitudinal Aging Study in India – Diagnostic Assessment of Dementia (LASI-DAD). Journal of the American Geriatrics Society, 68, S11–S19. https://doi.org/10.1111/jgs.16738
Angrisani, M., Casanova, M., & Meijer, E. (2020). Work-life balance and labor force attachment at older ages. Journal of Labor Research, 41, 34–68. https://doi.org/10.1007/s12122-020-09301-8
Kapteyn, A., et al. (2020). Tracking the effect of the COVID-19 pandemic on American households. Survey Research Methods, 14(2), 179–186. https://doi.org/10.18148/srm/2020.v14i2.7737
Lee, J., Lau, S., Meijer, E., & Hu, P. (2020). Living longer, with or without disability? A global and longitudinal perspective. Journals of Gerontology: Medical Sciences, 75(1), 162–167. https://doi.org/10.1093/gerona/glz007
Hurd, M. D., Meijer, E., Moldoff, M., & Rohwedder, S. (2019). Reducing cross-wave variability in survey measures of household wealth. Journal of Economic and Social Measurement, 44, 117–139. https://doi.org/10.3233/JEM-190465
Galesic, M., Bruine de Bruin, W., Dumas, M., Kapteyn, A., Darling, J. E., & Meijer, E. (2018). Asking about social circles improves election predictions and illuminates voting behaviour. Nature Human Behaviour, 2, 187–193. https://doi.org/10.1038/s41562-018-0302-y
Meijer, E., Spierdijk, L., & Wansbeek, T. (2017). Consistent estimation of linear panel data models with measurement error. Journal of Econometrics, 200, 169–180. https://doi.org/10.1016/j.jeconom.2017.06.003
Meijer, E., & Karoly, L. A. (2017). Representativeness of the low-income population in the Health and Retirement Study. Journal of the Economics of Ageing, 9, 90–99. https://doi.org/10.1016/j.jeoa.2016.08.004
Angrisani, M., Hurd, M. D., Meijer, E., Parker, A. M., & Rohwedder, S. (2017). Personality and employment transitions at older ages: Direct and indirect effects through non-monetary job characteristics. Labour, 31, 127–152. https://doi.org/10.1111/labr.12090
Gutsche, T. L., Kapteyn, A., Meijer, E., & Weerman, B. (2014). The RAND Continuous 2012 Presidential Election Poll. Public Opinion Quarterly, 78, 233–254. https://doi.org/10.1093/poq/nfu009
Galama, T. J., Hullegie, P., Meijer, E., & Outcault, S. (2012). Is there empirical evidence for decreasing returns to scale in a health capital model? Health Economics, 21, 1080–1100. PMCID: PMC3412934; https://doi.org/10.1002/hec.2843
Meijer, E., Rohwedder, S., & Wansbeek, T. (2012). Measurement error in earnings data: Using a mixture model approach to combine survey and register data. Journal of Business & Economic Statistics, 30, 191–201. PMCID: PMC3604906; https://doi.org/10.1198/jbes.2011.08166
Meijer, E., Kapteyn, A., & Andreyeva, T. (2011). Internationally comparable health indices. Health Economics, 20, 600–619. PMCID: PMC3601939; https://doi.org/10.1002/hec.1620
Meijer, E., Karoly, L. A., & Michaud, P.-C. (2010). Using matched survey and administrative data to estimate eligibility for the Medicare Part D low-income subsidy program. Social Security Bulletin, 70(2), 63–82. http://www.socialsecurity.gov/policy/docs/ssb/v70n2/
Meijer, E., & Ypma, J. Y. (2008). A simple identification proof for a mixture of two univariate normal distributions. Journal of Classification, 25, 113–123. https://doi.org/10.1007/s00357-008-9008-6
Wansbeek, T., & Meijer, E. (2007). Comments on: Panel data analysis—advantages and challenges. Test, 16, 33–36. https://doi.org/10.1007/s11749-007-0050-1
Meijer, E., & Wansbeek, T. (2007). The sample selection model from a method of moments perspective. Econometric Reviews, 26, 25–51. https://doi.org/10.1080/07474930600972194
Meijer, E. (2007). Citations, reference list, and author index with apacite. Eutypon, 16–19, 1–31. http://www.eutypon.gr/eutypon/e-cont-16-19.html
Meijer, E., & Rouwendal, J. (2006). Measuring welfare effects in models with random coefficients. Journal of Applied Econometrics, 21, 227–244. https://doi.org/10.1002/jae.841
Meijer, E. (2005). Matrix algebra for higher order moments. Linear Algebra and its Applications, 410, 112–134. https://doi.org/10.1016/j.laa.2005.02.040
De Haan, J., Leertouwer, E., Meijer, E., & Wansbeek, T. (2003). Measuring central bank independence: A latent variables approach. Scottish Journal of Political Economy, 50, 326–340. https://doi.org/10.1111/1467-9485.5003005
Rouwendal, J., & Meijer, E. (2001). Preferences for housing, jobs, and commuting: A mixed logit analysis. Journal of Regional Science, 41, 475–505. https://doi.org/10.1111/0022-4146.00227
Wansbeek, T., Wedel, M., & Meijer, E. (2001). Comment on Microeconometrics by J.A. Hausman. Journal of Econometrics, 100, 89–91. https://doi.org/10.1016/S0304-4076(00)00065-8
Meijer, E., & Wansbeek, T. (2000). Measurement error in a single regressor. Economics Letters, 69, 277–284. https://doi.org/10.1016/S0165-1765(00)00328-1
Meijer, E., & Wansbeek, T. (1999). Quadratic prediction of factor scores. Psychometrika, 64, 495–507. https://doi.org/10.1007/BF02294569
Busing, F. M. T. A., Meijer, E., & Van der Leeden, R. (1999). Delete-m jackknife for unequal m. Statistics and Computing, 9, 3–8. https://doi.org/10.1023/A:1008800423698
Meijer, E., & Mooijaart, A. (1996). Factor analysis with heteroscedastic errors. British Journal of Mathematical and Statistical Psychology, 49, 189–202. https://doi.org/10.1111/j.2044-8317.1996.tb01082.x
Meijer, E., & Mooijaart, A. (1994). The use of third-order moments in structural models. Qüestiió, 18, 75–84. http://www.idescat.cat/sort/questiio/sumaris/sum181.html
Book chapters
Meijer, E., Spierdijk, L., & Wansbeek, T. (2015). Measurement error in panel data. In B. H. Baltagi (Ed.), The Oxford handbook of panel data (pp. 325–362). Oxford, UK: Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199940042.013.0011
Kapteyn, A., & Meijer, E. (2014). A comparison of different measures of health and their relation to labor force transitions at older ages. In D. A. Wise (Ed.), Discoveries in the economics of aging (pp. 115–156). Chicago, IL: University of Chicago Press. (with a comment by S. F. Venti) https://doi.org/10.7208/chicago/9780226146126.003.0004
Meijer, E., Spierdijk, L., & Wansbeek, T. (2013). Measurement error in the linear dynamic panel data model. In B. C. Sutradhar (Ed.), ISS-2012 proceedings volume on longitudinal data analysis subject to measurement error, missing values and/or outliers (pp. 77–92). New York, NY: Springer. https://doi.org/10.1007/978-1-4614-6871-4_4
Fernandes, M., Meijer, E., & Zamarro, G. (2008). Comparison between SHARE, ELSA, and HRS. In A. Börsch-Supan et al. (Eds.), Health, ageing and retirement in Europe (2004–2007): Starting the longitudinal dimension (pp. 23–63). Mannheim, Germany: Mannheim Research Institute for the Economics of Aging (MEA). http://www.share-project.org/publications/books0/first-results-books.html
De Leeuw, J., & Meijer, E. (2008). Introduction to multilevel analysis. In J. de Leeuw & E. Meijer (Eds.), Handbook of multilevel analysis (pp. 1–75). New York, NY: Springer. https://doi.org/10.1007/978-0-387-73186-5_1
Van der Leeden, R., Meijer, E., & Busing, F. M. T. A. (2008). Resampling multilevel models. In J. de Leeuw & E. Meijer (Eds.), Handbook of multilevel analysis (pp. 401–433). New York, NY: Springer. https://doi.org/10.1007/978-0-387-73186-5_11
Wansbeek, T., & Meijer, E. (2001). Measurement error and latent variables. In B. H. Baltagi (Ed.), A companion to theoretical econometrics (pp. 162–179). Malden, MA: Blackwell. https://doi.org/10.1002/9780470996249.ch9
Meijer, E., Busing, F. M. T. A., & Van der Leeden, R. (1998). Estimating bootstrap confidence intervals for two-level models. In J. J. Hox & E. D. De Leeuw (Eds.), Assumptions,robustness, and estimation methods in multivariate modeling (pp. 35–47). Amsterdam, Netherlands: TT Publicaties.
Busing, F. M. T. A., Meijer, E., & Van der Leeden, R. (1995). The MLA program for two-level analysis with resampling options. In T. A. B. Snijders, B. Engel, J. C. van Houwelingen, A. Keen, G. J. Stemerdink, & M. Verbeek (Eds.), SSS’95: Toeval zit overal (pp. 37–58). Groningen, Netherlands: iec ProGAMMA.
Books
De Leeuw, J., & Meijer, E. (Eds.). (2008). Handbook of multilevel analysis. New York, NY: Springer. https://doi.org/10.1007/978-0-387-73186-5
Wansbeek, T., & Meijer, E. (2000). Measurement error and latent variables in econometrics. Amsterdam, Netherlands: North-Holland. Available from Amazon
Meijer, E. (1998). Structural equation models for nonnormal data. Leiden, Netherlands: DSWO Press.
Reports
Wilkens, J., et al. (2024). Harmonized LASI-DAD documentation, version A.4. Los Angeles, CA: Gateway to Global Aging Data. https://doi.org/10.25553/h5wx-ay45 (Also earlier versions [2020–2022])
Young, C., et al. (2024). Harmonized HRS-HCAP documentation, version A. Los Angeles, CA: Gateway to Global Aging Data. https://doi.org/10.25553/sfaq-br29
Bugliari, D., et al. (2024). RAND HRS detailed imputations file 2020 (v2) documentation. Santa Monica, CA: RAND Center for the Study of Aging. http://hrsonline.isr.umich.edu/modules/meta/rand/index.html (Also earlier versions under slightly different names [2013–2023])
Bugliari, D., et al. (2024). RAND HRS longitudinal file 2020 (v2) documentation. Santa Monica, CA: RAND Center for the Study of Aging. https://doi.org/10.7249/TLA2097-1-v3 (Also earlier versions under slightly different names [2013–2023])
Wilkens, J., et al. (2024). Harmonized ELSA-HCAP documentation, version A.2. Los Angeles, CA: Gateway to Global Aging Data. https://doi.org/10.34729/gmcv-6g43
Chien, S., et al. (2023). Harmonized Mex-Cog documentation, version A.2. Los Angeles, CA: Gateway to Global Aging Data. https://doi.org/10.34729/3JTX-WE73
Chien, S., et al. (2023). Harmonized LASI documentation, version A.3. Los Angeles, CA: Gateway to Global Aging Data. https://doi.org/10.25549/h-lasi (Also earlier versions under slightly different names [2020–2021])
LASI-DAD Study Collaborators. (2022). Harmonized diagnostic assessment of dementia for the Longitudinal Aging Study in India (LASI-DAD) wave 1 report. Los Angeles, CA: University of Southern California, Center for Economic and Social Research. https://doi.org/10.34729/HAKB-5045
LASI Investigators. (2021). User guide for 2017–2019 Longitudinal Aging Study in India (LASI) wave 1. Los Angeles, CA: University of Southern California, Center for Economic and Social Research. https://g2aging.org
Angrisani, M., Kapteyn, A., Meijer, E., & Saw, H.-W. (2019). Sampling and weighting the Understanding America Study (Working Paper No. 2019-004). Los Angeles, CA: University of Southern California, Center for Economic and Social Research. https://doi.org/10.2139/ssrn.3502405
Hurd, M. D., Meijer, E., Pantoja, P., & Rohwedder, S. (2018). Addition to the RAND HRS longitudinal files: IRA withdrawals in the HRS, 2000 to 2014 (Working Paper No. WP 2018-388). Ann Arbor, MI: Michigan Retirement Research Center. https://doi.org/10.2139/ssrn.3337842
Pantoja, P., et al. (2018). RAND HRS tax calculations 2014 (V2) documentation. Santa Monica, CA: RAND Corporation, Center for the Study of Aging. Available from http://hrsonline.isr.umich.edu/modules/meta/rand/index.html (Also an earlier version under a slightly different name [2017])
Beaumaster, S., et al. (2017). Harmonized SHARE documentation, version D.4. Los Angeles, CA: University of Southern California, Center for Economic and Social Research. https://g2aging.org (Also earlier versions [2011–2017])
Sikoki, B., Witoelar, F., Strauss, J., Meijer, E., & Suriastini, W. (2013). IFLS East user’s guide and field report. Yogyakarta, Indonesia: SurveyMETER. http://surveymeter.org/research/3/iflseast
Chien, S., Feeney, K., Liu, J., Meijer, E., & Lee, J. (2013). Harmonized LASI pilot data documentation, version: A (Working Paper No. WR-1018). Santa Monica, CA: RAND Corporation. https://doi.org/10.2139/ssrn.2354660
Kapteyn, A., Meijer, E., & Weerman, B. (2012). Methodology of the RAND Continuous 2012 Presidential Election Poll (Working Paper No. WR-961). RAND Corporation. https://doi.org/10.2139/ssrn.2146149
Foster, K., Meijer, E., Schuh, S., & Zabek, M. A. (2011). The 2009 Survey of Consumer Payment Choice (Public Policy Discussion Paper No. 11-01). Federal Reserve Bank of Boston. https://doi.org/10.2139/ssrn.1864854
Lee, J., Kapteyn, A., Meijer, E., & Yang, J.-S. (2010). Pre- and post-retirement asset portfolios (Publication No. 213). Filene Research Institute. http://staging.filene.org/research/report/pre-and-post-retirement-asset-portfolios
Foster, K., Meijer, E., Schuh, S., & Zabek, M. A. (2009). The 2008 Survey of Consumer Payment Choice (Public Policy Discussion Paper No. 09-10). Federal Reserve Bank of Boston. https://doi.org/10.2139/ssrn.1559959
Meijer, E., Karoly, L. A., & Michaud, P.-C. (2009). Estimates of potential eligibility for low-income subsidies under Medicare Part D (Tech. Rep. No. TR-686). RAND Corporation. http://www.rand.org/pubs/technical_reports/TR686/
Meijer, E. (2004). Computation of characteristics of value-of-time distributions and their standard errors (Research Report No. 04F09). University of Groningen, SOM Research School. https://doi.org/10.2139/ssrn.2052669
Busing, F. M. T. A., Meijer, E., & Van der Leeden, R. (1994). MLA: Software for multilevel analysis of data with two levels. User’s guide for version 1.0b (Tech. Rep. No. PRM 94-01). Leiden University, Department of Psychology. (Version 4.1, 2005)
Working papers
Kapteyn, A., et al. (2024). COVID-19 infections and cognitive function (CESR-Schaeffer Working Paper No. 2024-003). Los Angeles, CA: University of Southern California. https://doi.org/10.2139/ssrn.4884504
Perez-Arce, F., Prados, M., Meijer, E., & Lee, J. (2021). Social Security coverage around the world: The case of China, India and Mexico (Working Paper No. WP 2021-439). Ann Arbor, MI: Michigan Retirement and Disability Research Center. https://mrdrc.isr.umich.edu/publication_types/working-papers/
Meijer, E., Pérez-Arce, F., & Prados, M. (2020). A framework for cost-benefit analysis of totalization agreements (Working Paper No. WP 2020-410). Ann Arbor, MI: Michigan Retirement and Disability Research Center. https://doi.org/10.2139/ssrn.3885782
Angrisani, M., Kapteyn, A., & Meijer, E. (2019). Sorting into jobs and labor supply and demand at older ages (Working Paper). National Bureau of Economic Research. https://doi.org/10.2139/ssrn.3493892
Prados, M. J., Meijer, E., & Pérez-Arce, F. (2019). Macroeconomic effects of social security totalization agreements (Working Paper No. WP 2019-407). Ann Arbor, MI: Michigan Retirement and Disability Research Center. https://doi.org/10.2139/ssrn.3885751
Angrisani, M., Casanova, M., & Meijer, E. (2017). Work-life balance and labor force attachment at older ages (Working Paper No. WP 2017-366). Ann Arbor, MI: Michigan Retirement Research Center. https://doi.org/10.2139/ssrn.3110845
Hurd, M. D., Meijer, E., Moldoff, M., & Rohwedder, S. (2016). Improved wealth measures in the Health and Retirement Study: Asset reconciliation and cross-wave imputation (Working Paper No. WR-1150). Santa Monica, CA: RAND Corporation. https://doi.org/10.2139/ssrn.2791708
Angrisani, M., Kapteyn, A., & Meijer, E. (2015). Nonmonetary job characteristics and employment transitions at older ages (Working Paper No. WP 2015-326). Ann Arbor, MI: Michigan Retirement Research Center. https://doi.org/10.2139/ssrn.2689805
Lee, J., Meijer, E., & Phillips, D. (2015). The effect of using different imputation methods for economic variables in aging surveys (Working Paper No. 2015-019). Los Angeles, CA: University of Southern California, Center for Economic and Social Research. https://doi.org/10.2139/ssrn.2650214
Angrisani, M., Hurd, M. D., & Meijer, E. (2012). Investment decisions in retirement: The role of subjective expectations (Working Paper No. WP 2012-274). Michigan Retirement Research Center. https://doi.org/10.2139/ssrn.2188403
Hung, A. A., Meijer, E., Mihaly, K., & Yoong, J. (2009). Building up, spending down: financial literacy, retirement savings management, and decumulation (Working Paper No. WR-712). RAND Corporation. https://doi.org/10.2139/ssrn.1520203
Gilbert, P. D., & Meijer, E. (2006). Money and credit factors (Working Paper No. 2006-3). Bank of Canada. http://www.bankofcanada.ca/2006/03/publications/research/working-paper-2006-3/
Gilbert, P. D., & Meijer, E. (2005). Time series factor analysis with an application to measuring money (Research Report No. 05F10). University of Groningen, SOM Research School. http://irs.ub.rug.nl/ppn/289322812
Cools, K., Le Grand, H., Meijer, E., & Wansbeek, T. (2004). Solving the value metrics puzzle (Working Paper). University of Groningen, Department of Economics. https://doi.org/10.2139/ssrn.568423
See also
RAND: http://www.rand.org/pubs/authors/m/meijer_erik.html
RePEc: http://ideas.repec.org/f/pme236.html
SSRN: http://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=386896
Other publications
Meijer, E., & Wansbeek, T. (2001, 2005). Microeconometrie (Lecture Notes). Groningen, Netherlands: University of Groningen, Faculty of Economics.
Meijer, E. (1999). [Book review of D. A. Harville (1997), Matrix algebra from a statistician’s perspective]. Kwantitatieve Methoden, 20(60), 129–131. (in Dutch)
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Meurs, H., Meijer, E., & Pommer, J. (1997). No parking, no business? Verkeerskunde, 48(9), 30–34. (in Dutch)
Meurs, H., Meijer, E., & Pommer, J. (1997). Parkeerkwaliteit langs de meetlat [A yardstick for parking quality]. Verkeerskunde, 48(7/8), 30–34. (in Dutch)
Busing, F. M. T. A., Van der Leeden, R., & Meijer, E. (1995). MLA: Software for two-level analysis with resampling options. Multilevel Modelling Newsletter, 7(3), 11–13. http://www.bristol.ac.uk/cmm/learning/support/new7-3.pdf
Meijer, E., Van der Leeden, R., & Busing, F. M. T. A. (1995). Implementing the bootstrap for multilevel models. Multilevel Modelling Newsletter, 7(2), 7–11. http://www.bristol.ac.uk/cmm/learning/support/new7-2.pdf