A pre-test like estimator dominating the least-squares method

We develop a pre-test type estimator of a deterministic parameter vector β$\mathit{\beta }$ in a linear Gaussian regression model. In contrast to conventional pre-test strategies, that do not dominate the least-squares (LS) method in terms of mean-squared error (MSE), our technique is shown to dominate LS when the effective dimension is greater than or equal to 4. Our estimator is based on a simple and intuitive approach in which we first determine the linear minimum MSE (MMSE) estimate that minimizes the MSE. Since the unknown vector β$\mathit{\beta }$ is deterministic, the MSE, and consequently the MMSE solution, will depend in general on β$\mathit{\beta }$ and therefore cannot be implemented. Instead, we propose applying the linear MMSE strategy with the LS substituted for the true value of β$\mathit{\beta }$ to obtain a new estimate. We then use the current estimate in conjunction with the linear MMSE solution to generate another estimate and continue iterating until convergence. As we show, the limit is a pre-test type method which is zero when the norm of the data is small, and is otherwise a non-linear shrinkage of LS.     

Convergent Evolution of Innovative Teams in Technical Services

Abstract

In recent years UNC-Chapel Hill Libraries, Duke University Libraries, and NC State University Libraries each formed a new team in response to the increasing need to repurpose and reuse data and metadata in and across domains, systems, and environments in new ways. During this session at NC Serials 2019 the speakers described their teams, how those teams came about, and their responsibilities; discussed similarities and differences across their teams; and shared communication strategies, lessons, and opportunities they’ve learned so far. This report captures the discussions that occurred during this session.     

Some aspects of the use of birth expectations data from sample surveys for population projections

The value of national sample survey data relating to birth expectations for projecting births is reassessed in the light of data limitations pointed out by Ryder and Westoff among others and of the methods of projection used by the United States Bureau of the Census. The annual level of fertility under the cohort-fertility projection method depends on the assumptions regarding completed fertility, about which the available survey data are fairly informative, and on the assumptions regarding the timing of births, about which the survey data tell us very little. Test calculations suggest that Ryder and Westoff have overstated the significance of timing relative to completed family size for the level of future births. We believe that the fall in the annual total fertility rate in the first half of the sixties is to be explained only in part by a general delay in childbearing; a moderate to substantial decrease in completed family size has also occurred. Analysis of the latest set of fertility projections of the Census Bureau also suggests that the assumptions about completed fertility are a much more important determinant of the level of future births than timing, both in the short and long term. Although the available expectations data cannot help in predicting short-term annual changes in fertility, they appear useful for making long-term projections of annual fertility. Expansion of the size, frequency, and content of the sample surveys and incorporation of parity and birth interval into the projection method may improve projections.     

Partial information on decisional competences and the desirability of the expert rule in uncertain dichotomous choice situations

The paper analyzes the expert resolution problem by employing extended versions of the uncertain dichotomous choice model. The main purpose of this study is to illustrate how the analysis of optimal decision rules can be carried out while dispensing with the common restrictive assumption of full information regarding individual decisional competences. In contrast to most previous studies in this field we here evaluate the expert rule under alternative assumptions regarding the available partial information on judgmental competences rather than compare it in an ad hoc manner to some common alternative rules, such as simple majority rule. A fuller optimality analysis allowing the evaluation of all relevant decision rules, and not merely the expert rule, is attempted for a five-member panel of experts assuming a uniform distribution of individual competences. For three-member groups the optimality issue is resolved by assuming no information on individual competences and interpreting the expert rule as an even-chance lottery on skills.     

Order relations among efficient decision rules

The paper introduces the concept of polar decision rules and establishes that majority rules are polar rules. We identify second best rules and penultimate rules in cases that majority rules are optimal or the most inferior, respectively. We especially specify the almost expert rule and the almost majority rule as the secondary rules of the expert and majority rules, respectively.     

How Much More XXX is Generation X Consuming? Evidence of Changing Attitudes and Behaviors Related to Pornography Since 1973

We use data from the General Social Survey (GSS) over a 40-year period (1973–2012) to evaluate changes in attitudes about pornography and pornography consumption among American young adults. One of the major challenges in making comparisons across birth generations is separating the effect of birth cohort from age and period effects. We use an intrinsic estimator to separately identify the effects of age, birth cohort, and time period using 40 years of repeated cross-section data. We find that, relative to the general population, young people's beliefs about whether pornography should be illegal have stayed relatively constant over this 40-year period and, if anything, have slightly increased. We also find that pornography consumption has been increasing across birth generations, though this increase has been smaller than would be inferred based on differences across generations at a single point in time, due to a strong age component in consumption patterns.     

INCOMPLETE DATA IN GENERALIZED LINEAR MODELS WITH CONTINUOUS COVARIATES

This paper proposes a method for estimating the parameters in a generalized linear model with missing covariates. The missing covariates are assumed to come from a continuous distribution, and are assumed to be missing at random. In particular, Gaussian quadrature methods are used on the E-step of the EM algorithm, leading to an approximate EM algorithm. The parameters are then estimated using the weighted EM procedure given in Ibrahim (1990). This approximate EM procedure leads to approximate maximum likelihood estimates, whose standard errors and asymptotic properties are given. The proposed procedure is illustrated on a data set.     

Technology and the Labor Market

Review of Economics of the Household - Economic developments of the past three decades posed new questions to economists: what are the causes of fluctuations in rates of return to human capital?...