RAND: Erik Meijer2014-08-21T10:17:19ZCopyright (c) 2014, The RAND CorporationRAND Corporationhttp://www.rand.org/about/people/m/meijer_erik.htmlHarmonized LASI Pilot Data Documentationhttp://www.rand.org/pubs/working_papers/WR1018.html2013-11-19T05:30:00Z2013-11-19T05:30:00ZDocuments the Harmonized Longitudinal Aging Study in India (LASI) file.Methodology of the RAND Continuous 2012 Presidential Election Pollhttp://www.rand.org/pubs/working_papers/WR961.html2012-09-24T07:45:00Z2012-09-24T07:45:00ZGives a detailed account of the methodology underlying the he RAND Continuous 2012 Presidential Election Poll.Point and Set Identification in Linear Panel Data Models with Measurement Errorhttp://www.rand.org/pubs/working_papers/WR941.html2012-06-01T08:15:00Z2012-06-01T08:15:00ZIdentifies moment conditions in linear panel data models with measurement error.Empirical Evidence for Decreasing Returns to Scale in a Health Capital Modelhttp://www.rand.org/pubs/working_papers/WR928.html2012-03-13T08:00:00Z2012-03-13T08:00:00ZEstimates a health investment equation, derived from a health capital model with particular emphasis on whether the health production function has constant returns to scale, or decreasing returns to scale.Internationally Comparable Health Indiceshttp://www.rand.org/pubs/external_publications/EP20110041.html2011-04-30T21:00:00Z2011-04-30T21:00:00ZThis project addressed the lack of comparable health measures and indices across nations by developing a cross-national model for measuring health status. In applying the measures to several nations, the study found significant variability in genuine health that correlates most closely with national income.Internationally Comparable Health Indiceshttp://www.rand.org/pubs/external_publications/EP201000165.html2010-05-31T21:00:00Z2010-05-31T21:00:00ZThis project addressed the lack of comparable health measures and indices across nations by developing a cross-national model for measuring health status.. Using Matched Survey and Administrative Data to Estimate Eligibility for the Medicare Part D Low Income Subsidy Programhttp://www.rand.org/pubs/working_papers/WR743.html2010-02-18T07:35:00Z2010-02-18T07:35:00ZUses matched survey and administrative data to estimate eligibility for the Medicare part D low income subsidy program.Using Matched Survey and Administrative Data to Estimate Eligibility for Medicare Part D Low Income Subsidy Programhttp://www.rand.org/pubs/external_publications/EP201000174.html2009-12-31T21:00:00Z2009-12-31T21:00:00ZThis article uses matched survey and administrative data to estimate, as of 2006, the size of the population eligible for the Low-Income Subsidy (LIS), which was designed to provide "extra help" with premiums, deductibles, and copayments for Medicare Part D beneficiaries with low income and limited assets.Building Up, Spending Down: Financial Literacy, Retirement Savings Management, and Decumulationhttp://www.rand.org/pubs/working_papers/WR712.html2009-12-07T12:08:00Z2009-12-07T12:08:00ZExamines if employees are well-equipped to make decisions regarding how much to contribute to their employer-provided pension plans, how to allocate their retirement accounts, and how they will decumulate their retirement funds during retirement.Estimates of Potential Eligibility for Low-Income Subsidies Under Medicare Part Dhttp://www.rand.org/pubs/technical_reports/TR686.html2009-04-16T13:28:00Z2009-04-16T13:28:00ZAn estimated 12 million persons (29 percent of Medicare beneficiaries) were potentially eligible for the Medicare Part D Low-Income Subsidy in 2006, but there is uncertainty in this estimate due to differences in the two main data sources employed.Health Indexes and Retirement Modeling in International Comparisonshttp://www.rand.org/pubs/working_papers/WR614.html2008-09-08T15:02:00Z2008-09-08T15:02:00ZDevelops a cross-country health measurement model that uses the relationship among functional limitations, self-reports, and physical measures to construct health indexes for modeling retirement decisions.Prediction of Latent Variables in a Mixture of Structural Equation Models, with an Application to the Discrepancy Between Survey and Register Datahttp://www.rand.org/pubs/working_papers/WR584.html2008-06-13T14:39:00Z2008-06-13T14:39:00ZStudies the prediction of latent variables in a finite mixture of linear structural equation models.
A Simple Identification Proof for a Mixture of Two Univariate Normal Distributionshttp://www.rand.org/pubs/external_publications/EP20080621.html2007-12-31T21:00:00Z2007-12-31T21:00:00ZA simple proof of the identification of a mixture of two univariate normal distributions is given.The Sample Selection Model from a Method of Moments Perspectivehttp://www.rand.org/pubs/external_publications/EP20070117.html2006-12-31T21:00:00Z2006-12-31T21:00:00ZIt is shown how the usual two-step estimator for the standard sample selection model can be seen as a method of moments estimator.