In empirical studies, the data usually don't include all the variables of interest in an economic model. This paper shows the identification of unobserved variables in observations at the population level. When the observables are distinct in each observation, there exists a function mapping...

The focus of this Element is on the idea that choice is hierarchical so that there exists an order of acquisition of durable goods and assets as real incomes increase. Two main approaches to deriving such an order are presented, the so-called Paroush approach and Item Response Theory. An...

Economic models often depend on quantities that are unobservable, either for privacy reasons or because they are difficult to measure. Examples of such variables include human capital (or ability), personal income, unobserved heterogeneity (such as consumer "types"), etc. This situation has...

This paper studies the asymptotic properties of empirical nonparametric regressions that partially misspecify the relationships between nonstationary variables. In particular, we analyze nonparametric kernel regressions in which a potential nonlinear cointegrating regression is misspecified...

Hurdle and zero-inflated models are the two foremost methods used to deal with excess zeros. However, their reliance on the nonparticipation assumption is a drawback when applied to recreation demand analysis because there is not a theoretical framework convincingly explaining presence of...

In this paper we study the process of children's skill formation. The identification of this process is challenging because children's skills are observed only through arbitrarily scaled and imperfect measures. Using a dynamic la- tent factor structure, we provide new identification results...

A recent and growing area of research applies latent factor models to study the development of children's skills. Some normalization is required in these models because the latent variables have no natural units and no known location or scale. We show that the standard practice of...

We develop a new estimator for the process of children's skill formation in which children's skills endogenously develop according to a dynamic latent factor structure. Rather than assuming skills are measured perfectly by a particular measure, we accommodate the variety of skills measures used...

A recent and growing area of research applies latent factor models to study the development of children's skills. Some normalization is required in these models because the latent variables have no natural units and no known location or scale. We show that the standard practice of...

Paul Green, writing with Carmone and Wachpress (1976), was among the first scholars to introduce latent variable models to marketing by utilizing the CANDECOMP procedure (Carroll 1980) on contingency tables. A year later, Green, Carmone, and Wachpress (1977) introduced logit and log-linear...

Reliable measures of body composition are essential in order to develop effective policies to tackle the costs of obesity. To date the lack of an acceptable gold-standard for measuring fatness has made it difficult to evaluate alternative measures of obesity. In this paper we draw on work in...

This paper discusses aspects of a framework for modeling labor supply where the notion of job choice is fundamental. In this framework, workers are assumed to have preferences over latent job opportunities belonging to worker-specific choice sets from which they choose their preferred job. The...

Intro -- Table of Contents -- Foreword -- Introduction -- 1 Characteristics and Main Types of Measurement -- 1.1. Introduction -- 1.2. The basis of the measure -- 1.3. Attitude scales -- 1.4. Conclusion -- 1.5. Knowledge tests -- 2 Standardizing or Constructing a Measurement Scale -- 2.1....

Measurement invariance is an important assumption in the Rasch model and mixture models constitute a flexible way of checking for a violation of this assumption by detecting unobserved heterogeneity in item response data. Here, a general class of Rasch mixture models is established and...

Given a model that can be simulated, conditional moments at a trial parameter value can be calculated with high accuracy by applying kernel smoothing methods to a long simulation. With such conditional moments in hand, standard method of moments techniques can be used to estimate the parameter....