scholarly journals The Empirical Content of Binary Choice Models

Econometrica ◽  
2021 ◽  
Vol 89 (1) ◽  
pp. 457-474
Author(s):  
Debopam Bhattacharya
Keyword(s):  
Unobserved Heterogeneity ◽  
Revealed Preference ◽  
Binary Choice ◽  
Economic Rationality ◽  
Practical Applications ◽  
Nonparametric Models ◽  
Policy Interventions ◽  
Binary Choice Models ◽  
Shape Restrictions ◽  
Budget Sets

An important goal of empirical demand analysis is choice and welfare prediction on counterfactual budget sets arising from potential policy interventions. Such predictions are more credible when made without arbitrary functional‐form/distributional assumptions, and instead based solely on economic rationality, that is, that choice is consistent with utility maximization by a heterogeneous population. This paper investigates nonparametric economic rationality in the empirically important context of binary choice. We show that under general unobserved heterogeneity, economic rationality is equivalent to a pair of Slutsky‐like shape restrictions on choice‐probability functions. The forms of these restrictions differ from Slutsky inequalities for continuous goods. Unlike McFadden–Richter's stochastic revealed preference, our shape restrictions (a) are global, that is, their forms do not depend on which and how many budget sets are observed, (b) are closed form, hence easy to impose on parametric/semi/nonparametric models in practical applications, and (c) provide computationally simple, theory‐consistent bounds on demand and welfare predictions on counterfactual budge sets.

scholarly journals A Fast Estimator for Binary Choice Models with Spatial, Temporal, and Spatio-Temporal Interdependence

2021 ◽  
pp. 1-7
Author(s):  
Julian Wucherpfennig ◽  
Aya Kachi ◽  
Nils-Christian Bormann ◽  
Philipp Hunziker
Keyword(s):  
Computational Cost ◽  
Choice Models ◽  
Reduced Form ◽  
Binary Choice ◽  
Monte Carlo Experiments ◽  
Binary Choice Models ◽  
Spatio Temporal ◽  
Computationally Intensive ◽  
Pseudo Maximum Likelihood ◽  
Parameter Values

Abstract Binary outcome models are frequently used in the social sciences and economics. However, such models are difficult to estimate with interdependent data structures, including spatial, temporal, and spatio-temporal autocorrelation because jointly determined error terms in the reduced-form specification are generally analytically intractable. To deal with this problem, simulation-based approaches have been proposed. However, these approaches (i) are computationally intensive and impractical for sizable datasets commonly used in contemporary research, and (ii) rarely address temporal interdependence. As a way forward, we demonstrate how to reduce the computational burden significantly by (i) introducing analytically-tractable pseudo maximum likelihood estimators for latent binary choice models that exhibit interdependence across space and time and by (ii) proposing an implementation strategy that increases computational efficiency considerably. Monte Carlo experiments show that our estimators recover the parameter values as good as commonly used estimation alternatives and require only a fraction of the computational cost.

An Economic Assessment of Parents' Self-Composure: The Case of Physical Child Abuse

2004 ◽  
Vol 4 (1) ◽  
Author(s):  
Mark D Agee ◽  
Thomas Crocker ◽  
Jason F Shogren
Keyword(s):  
Child Abuse ◽  
Unobserved Heterogeneity ◽  
Economic Assessment ◽  
Child Behaviors ◽  
Physical Child Abuse ◽  
Policy Interventions ◽  
Ex Post ◽  
Maximum Likelihood Procedure ◽  
Child Violence ◽  
Parent Child

Abstract This paper uses a maximum likelihood procedure that accounts for unobserved heterogeneity in the sample to implement a preference-based model to assess factors that influence parents' likelihood of losing their composure and physically abusing their children. A basic supposition of the model is that parents prefer to deal with parent-child conflict by choosing tactics and behaviors that do not exceed a specified level of violence; however, endogenous parent and child behaviors and exogenous circumstances may arouse parents' emotions that cause this level to be exceeded. Our results suggest policy interventions that influence such circumstances and associated behaviors may strongly influence the incidence of physical child abuse. We estimate the ex ante annual value parents attach to risk reductions of self-composure losses associated with excessive parent-child violence. This value is shown to be greater than currently estimated annual savings in ex post costs associated with a comparable decrease in U.S. physical child abuse incidence.

scholarly journals SUBJECTIVE ASSESSMENT OF FINANCIAL DISTRESS IN PURCHASING A SUFFICIENT AMOUNT OF FOOD. ECONOMETRIC ANALYSIS OF POLISH MICRODATA

2017 ◽  
Vol 16 (1) ◽  
pp. 5-12 ◽  
Author(s):  
Hanna Dudek
Keyword(s):  
Financial Distress ◽  
Subjective Assessment ◽  
Econometric Analysis ◽  
Binary Choice ◽  
Economic Factors ◽  
Factors Affecting ◽  
Financial Difficulties ◽  
Binary Choice Models ◽  
Over Time ◽  
Socio Economic Factors

The paper analyses subjective aspects of food poverty in Poland. It deals with households’ assessment of financial difficulties in purchasing a sufficient amount of food in the period 2009–2015. The study is based on Social Diagnosis data. Its purpose is to identify the socio-economic factors affecting financial distress among Polish households. The study also aims to test whether the probability of experiencing financial difficulties is persistent over time. In econometric analysis binary choice models for panel data are applied. The findings state that apart from equivalent incomes and owned savings, loans or debts, factors having a significant impact on the final results are places of residence and biological types of households.

scholarly journals A Gaussian Process Model for Color Camera Characterization: Assessment in Outdoor Levantine Rock Art Scenes

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4610 ◽  
Author(s):  
Adolfo Molada-Tebar ◽  
Gabriel Riutort-Mayol ◽  
Ángel Marqués-Mateu ◽  
José Luis Lerma
Keyword(s):  
Gaussian Process ◽  
Process Model ◽  
Rock Art ◽  
Predictive Performance ◽  
Practical Applications ◽  
Nonparametric Models ◽  
Novel Approach ◽  
Characterization Procedure ◽  
Leave One Out ◽  
Gp Model

In this paper, we propose a novel approach to undertake the colorimetric camera characterization procedure based on a Gaussian process (GP). GPs are powerful and flexible nonparametric models for multivariate nonlinear functions. To validate the GP model, we compare the results achieved with a second-order polynomial model, which is the most widely used regression model for characterization purposes. We applied the methodology on a set of raw images of rock art scenes collected with two different Single Lens Reflex (SLR) cameras. A leave-one-out cross-validation (LOOCV) procedure was used to assess the predictive performance of the models in terms of CIE XYZ residuals and Δ E a b * color differences. Values of less than 3 CIELAB units were achieved for Δ E a b * . The output sRGB characterized images show that both regression models are suitable for practical applications in cultural heritage documentation. However, the results show that colorimetric characterization based on the Gaussian process provides significantly better results, with lower values for residuals and Δ E a b * . We also analyzed the induced noise into the output image after applying the camera characterization. As the noise depends on the specific camera, proper camera selection is essential for the photogrammetric work.

scholarly journals OPTIMIZING POLICYMAKERS’ LOSS FUNCTIONS IN CRISIS PREDICTION: BEFORE, WITHIN OR AFTER?

2019 ◽  
pp. 1-24
Author(s):  
Peter Sarlin ◽  
Gregor von Schweinitz
Keyword(s):  
Early Warning ◽  
Loss Functions ◽  
Model Specification ◽  
Binary Choice ◽  
Forecast Errors ◽  
Ex Post ◽  
Binary Choice Models ◽  
Out Of Sample ◽  
Threshold Setting ◽  
Two Alternatives

Recurring financial instabilities have led policymakers to rely on early-warning models to signal financial vulnerabilities. These models rely on ex-post optimization of signaling thresholds on crisis probabilities accounting for preferences between forecast errors, but come with the crucial drawback of unstable thresholds in recursive estimations. We propose two alternatives for threshold setting with similar or better out-of-sample performance: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. Given probabilistic model output, it is intuitive that a decision rule is independent of the data or model specification, as thresholds on probabilities represent a willingness to issue a false alarm vis-à-vis missing a crisis. We provide real-world and simulation evidence that this simplification results in stable thresholds, while keeping or improving on out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.

Robust Estimation in Binary Choice Models

2009 ◽  
Vol 39 (2) ◽  
pp. 266-279 ◽  
Author(s):  
Yulia Kotlyarova ◽  
Victoria Zinde-Walsh
Keyword(s):  
Robust Estimation ◽  
Choice Models ◽  
Binary Choice ◽  
Binary Choice Models

scholarly journals Testing for Consistency using Artificial Regressions

1989 ◽  
Vol 5 (3) ◽  
pp. 363-384 ◽  
Author(s):  
Russell Davidson ◽  
James G. MacKinnon
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Information Matrix ◽  
Binary Choice ◽  
Parameter Estimates ◽  
Linear Regression Models ◽  
Score Tests ◽  
Binary Choice Models ◽  
Lagrange Multiplier Tests ◽  
Information Matrix Test

We consider several issues related to Durbin-Wu-Hausman tests; that is, tests based on the comparison of two sets of parameter estimates. We first review a number of results about these tests in linear regression models, discuss what determines their power, and propose a simple way to improve power in certain cases. We then show how in a general nonlinear setting they may be computed as “score” tests by means of slightly modified versions of any artificial linear regression that can be used to calculate Lagrange multiplier tests, and explore some of the implications of this result. In particular, we show how to create a variant of the information matrix test that tests for parameter consistency. We examine the conventional information matrix test and our new version in the context of binary-choice models, and provide a simple way to compute both tests using artificial regressions.

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