Estimation in the Presence of Measurement Error

The importance of measurement error for parameter estimation and for the design of statistical studies, particularly sample surveys, is examined. Beginning with a brief review of Hansen\u27s contributions, the discussion concentrates on estimation problems in which measurement error leads to bias in the usual estimators. Estimation of distribution functions and regression equations are discussed, and the implications for the design of surveys are presented

A comparison of unit root test criteria

During the past fifteen years, the ordinary least squares estimator and the corresponding pivotal statistic have been widely used for testing the unit root hypothesis in autoregressive processes. Recently, several new criteriia, based on the maximum likelihood estimators and weighted symmetric estimators, have been proposed. In this article, we describe several different test criteria. Results from a Monte Carlo study that compares the power of the different criteria indicates that the new tests are more powerful against the stationary alternative. Of the procedures studied, the weighted symmetric estimator and the unconditional maximum likelihood estimator provide the most powerful tests against the stationary alternative. As an illustration, we analyze the quarterly change in busine;ss investories

Welfare Reform and Labor Participation: Are There Urban and Rural Differences?

Although welfare reform began in 1996 at the national level, Iowa was one of the earliest states to obtain a waiver to initiate the Iowa Family Investment Program (FIP) in 1993. To gain a better understanding of welfare recidivism, we use Iowa administrative quarterly data between October1993 and September 1995, impute the education attainment for the caseheads with missing education attainment using fractional imputation and study the factors that affect the probability of working, the potential wage for the caseheads and the possibility of leaving FIP based on the potential wage. We find higher education (i.e. higher skills) leads to higher labor force participation, especially for single-mothers with children. Metro or urban location is associated with the probability of working and potential wage earnings, but has no effect on FIP participation. The local unemployment rate does not affect labor participation of low-income individuals, but does affect the potential wage and FIP status. Those with lower education, and nonwhites are more affected by the local labor market environment than others. If an individual moves once in a year, he or she will earn more money than in the original job; no gains are achieved through moving more than once. The possibility of leaving FIP is relatively high if there is only one move.Labor and Human Capital,

Medical-Legal Partnerships to Support Continuity of Care for Immigrants Impacted by HIV: Lessons Learned from California.

The United States (US) has experienced a surge of anti-immigrant policies and rhetoric, raising concerns about the influence on health outcomes for immigrants living in the US. We conducted qualitative interviews (n = 20) with health care and social service providers, attorneys, and legal/policy experts in California to understand how agencies were maintaining access to HIV care and prevention for immigrant clients. We conducted a thematic analysis to describe the role of medical-legal partnerships (MLPs) and document best practices. Informants reported high demand for legal services. Referrals were facilitated by case managers, medical providers, and pre-existing relationships between clinics and legal agencies. Informants identified a need for additional funding and further guidance on screening for and supporting patients with legal needs. MLPs have the capacity to create sustainable, efficient, comprehensive structural changes that minimize barriers to HIV prevention and treatment and improve health outcomes among immigrant populations

SMALL AREA PREDICTION UNDER ALTERNATIVE MODEL SPECIFICATIONS

Construction of small area predictors and estimation of the prediction mean squared error, given different types of auxiliary information are illustrated for a unit level model. Of interest are situations where the mean and variance of an auxiliary variable are subject to estimation error. Fixed and random specifications for the auxiliary variables are considered. The efficiency gains associated with the random specification for the auxiliary variable measured with error are demonstrated. A parametric bootstrap procedure is proposed for the mean squared error of the predictor based on a logit model. The proposed bootstrap procedure has smaller bootstrap error than a classical double bootstrap procedure with the same number of samples