Estimating fecundability from data on waiting times to first conception

"This article tests assumptions invoked in the demographic literature to estimate the population distribution of fecundability from data on waiting times to first conception. In continuous time, the key assumption is that waiting times are realizations from a mixture of exponentials distribution. In discrete time, the key assumption is that waiting times are realizations from a mixture of geometrics distribution. The [U.S.] Hutterite data analyzed by Sheps (1965) are consistent with this assumption. Various models, however, have one representation in mixture of exponentials form. A fundamental identification problem plagues the conventional estimation procedure. Our analysis calls into question the conventional practice of checking model specification by using goodness-of-fit tests. The practical importance of the identification problem in duration models is demonstrated."     

Line transects of two-dimensional random fields: Estimation and design

In line-transect sampling data are collected continuously along the path travelled. These data are then used to estimate some characteristic of an entire region. We model the characteristic of the region as a linear functional of Z(), a realization of a random field, and our data as the values of Z(x) for x along the path. We study how to estimate the characteristic of interest and how to choose the path to accurately estimate it.     

Estimating the Technology of Cognitive and Noncognitive Skill Formation

This paper formulates and estimates multistage production functions for children's cognitive and noncognitive skills. Skills are determined by parental environments and investments at different stages of childhood. We estimate the elasticity of substitution between investments in one period and stocks of skills in that period to assess the benefits of early investment in children compared to later remediation. We establish nonparametric identification of a general class of production technologies based on nonlinear factor models with endogenous inputs. A by‐product of our approach is a framework for evaluating childhood and schooling interventions that does not rely on arbitrarily scaled test scores as outputs and recognizes the differential effects of the same bundle of skills in different tasks. Using the estimated technology, we determine optimal targeting of interventions to children with different parental and personal birth endowments. Substitutability decreases in later stages of the life cycle in the production of cognitive skills. It is roughly constant across stages of the life cycle in the production of noncognitive skills. This finding has important implications for the design of policies that target the disadvantaged. For most configurations of disadvantage it is optimal to invest relatively more in the early stages of childhood than in later stages.     

Using natural language processing technology for qualitative data analysis

Social researchers often apply qualitative research methods to study groups and their communications artifacts. The use of computer-mediated communications has dramatically increased the volume of text available, but coding such text requires considerable manual effort. We discuss how systems that process text in human languages (i.e. natural language processing [NLP]) might partially automate content analysis by extracting theoretical evidence. We present a case study of the use of NLP for qualitative analysis in which the NLP rules showed good performance on a number of codes. With the current level of performance, use of an NLP system could reduce the amount of text to be examined by a human coder by an order of magnitude or more, potentially increasing the speed of coding by a comparable degree. The paper is significant as it is one of the first to demonstrate the use of high-level NLP techniques for qualitative data analysis.     

Structural Equations,Treatment Effects,and Econometric Policy Evaluation1

This paper uses the marginal treatment effect (MTE) to unify the nonparametric literature on treatment effects with the econometric literature on structural estimation using a nonparametric analog of a policy invariant parameter; to generate a variety of treatment effects from a common semiparametric functional form; to organize the literature on alternative estimators; and to explore what policy questions commonly used estimators in the treatment effect literature answer. A fundamental asymmetry intrinsic to the method of instrumental variables (IV) is noted. Recent advances in IV estimation allow for heterogeneity in responses but not in choices, and the method breaks down when both choice and response equations are heterogeneous in a general way.     

Computerization: reengineering the lawyering process for the 21st century

Alvin and Heidi Toffler, describe the benefits of "informationware" and how one of the most tradition-bound organizations, the armed forces, has substantially changed its operations. According to the authors, mere knowledge is not sufficient to prevail; how one manages or uses knowledge separates winners from losers. Further, the authors say "...basically add new elements or create new combinations of old elements within an existing 'game.' A true revolution goes beyond that to change the game itself, including its rules, its equipment, the size and organization of the 'teams,' their training, doctrine, tactics, and just about everything else. Even more important, it changes the relationship of the game to society itself." Application of this theory to today's litigation is obvious and compelling. The ways in which clients and defense counsel obtain, analyze, and strategically apply evidence and legal principles must change to incorporate the informationware this society uses in other lines of business. This means more extensive and appropriate usage of computer hardware in a software environment that fully supports emerging client and defense counsel needs. The computer system must provide case direction, helpful suggestions, and direct, rapid access to data bases containing information presently available only through person-to-person networking.     

Nonparametric Identification and Estimation of Nonadditive Hedonic Models

This paper studies the identification and estimation of preferences and technologies in equilibrium hedonic models. In it, we identify nonparametric structural relationships with nonadditive heterogeneity. We determine what features of hedonic models can be identified from equilibrium observations in a single market under weak assumptions about the available information. We then consider use of additional information about structural functions and heterogeneity distributions. Separability conditions facilitate identification of consumer marginal utility and firm marginal product functions. We also consider how identification is facilitated using multimarket data.     

Switching nonparametric regression models

We propose a methodology to analyse data arising from a curve that, over its domain, switches among J states. We consider a sequence of response variables, where each response y depends on a covariate x according to an unobserved state z. The states form a stochastic process and their possible values are j=1,?…?, J. If z equals j the expected response of y is one of J unknown smooth functions evaluated at x. We call this model a switching nonparametric regression model. We develop an Expectation–Maximisation algorithm to estimate the parameters of the latent state process and the functions corresponding to the J states. We also obtain standard errors for the parameter estimates of the state process. We conduct simulation studies to analyse the frequentist properties of our estimates. We also apply the proposed methodology to the well-known motorcycle dataset treating the data as coming from more than one simulated accident run with unobserved run labels.