This paper considers parametric inference in a wide range of structural econometric models. It illustrates how the indirect inference principle can be used in the inference of these models. Specifically, we show that an ordinary least squares (OLS) estimation can be used as an auxiliarymodel, which leads to amethod that is similar in spirit to a two-stage least squares (2SLS) estimator. Monte C...

Consider an ergodic Markov chain on the real line, with parametric models for the conditional mean and variance of the transition distribution. Such a setting is an instance of a quasi-likelihood model. The customary estimator for the parameter is the maximum quasi-likelihood estimator. It is not eecient, but as good as the best estimator that ignores the parametric model for the conditional va...

The present research develops and tests a hierarchical model of the underlying behavioral processes of team cognition. Team cognition is represented as a second-order construct comprised of three first-order dimensions (team learning, team reflexivity, and team mental model). The proposed second-order construct was embedded in a nomological network as a mediator between collaboration mode (coll...

In this paper, we propose an approach for learning regression models efficiently in an environment where multiple features and data-points are added incrementally in a multistep process. At each step, any finite number of features maybe added and hence, the setting is not amenable to low rank updates. We show that our approach is not only efficient and optimal for ordinary least squares, weight...

As with ordinary least squares regression or logistic regression, we can consider significance tests for individual estimates, such as intercepts, slopes, and their variances, as well as whether the full model accounts for a significant amount of variance in the dependent variable. In between, there is also the possibility of determining whether of subset of predictors contribute significantly....

This chapter provides a tutorial introduction to gradientbased optical flow estimation. We discuss least-squares and robust estimators, iterative coarse-to-fine refinement, different forms of parametric motion models, different conservation assumptions, probabilistic formulations, and robust mixture models.

Stable autoregressive models are considered with martingale differences errors scaled by an unknown nonparametric time-varying function generating heterogeneity. An important special case involves structural change in the error variance, but in most practical cases the pattern of variance change over time is unknown and may involve shifts at unknown discrete points in time, continuous evolution...

This paper investigates the impact of instructional days on student performance. Because school year length is endogenously determined, I estimate the causal impact of school year length through two quasi-experiments that exploit different sources of variation in instructional days. The first identifies school year length’s effect through weather-related cancellations in Colorado and Maryland. ...

Stable autoregressive models are considered with martingale differences errors scaled by an unknown nonparametric time-varying function generating heterogeneity. An important special case involves structural change in the error variance, but in most practical cases the pattern of variance change over time is unknown and may involve shifts at unknown discrete points in time, continuous evolution...