Partial observability in bivariate probit models

This study investigates random utility models in which the observed binary outcome does not reflect the binary choice of a single decision-maker, but rather the joint unobserved binary choices of two decision-makers. Under the usual normality assumptions, the model that arises for the observed binary outcome is not a univariate probit model, but rather a bivariate probit model in which only one of the four possible outcomes is observed. Estimation and identification issues are discussed, and the implications for sample selectivity problems are noted.     

Estimation in a generalization of bivariate probit models with dummy endogenous regressors

The purpose of this paper is to provide guidelines for empirical researchers who use a class of bivariate threshold crossing models with dummy endogenous variables. A common practice employed by the researchers is the specification of the joint distribution of unobservables as a bivariate normal distribution, which results in a bivariate probit model. To address the problem of misspecification in this practice, we propose an easy‐to‐implement semiparametric estimation framework with parametric copula and nonparametric marginal distributions. We establish asymptotic theory, including root‐n normality, for the sieve maximum likelihood estimators that can be used to conduct inference on the individual structural parameters and the average treatment effect (ATE). In order to show the practical relevance of the proposed framework, we conduct a sensitivity analysis via extensive Monte Carlo simulation exercises. The results suggest that estimates of the parameters, especially the ATE, are sensitive to parametric specification, while semiparametric estimation exhibits robustness to underlying data‐generating processes. We then provide an empirical illustration where we estimate the effect of health insurance on doctor visits. In this paper, we also show that the absence of excluded instruments may result in identification failure, in contrast to what some practitioners believe.     

Discrete Dynamics for the Core-periphery Model

Abstract

It is known that the discretisation of continuous-time models can introduce chaotic behaviour, even when this is not consistent with observations or even the model's assumptions. We propose generic dynamics describing discrete-time core-periphery models that comply with the established assumptions in the literature and are consistent with observed behaviour. The desired properties of the dynamics are proved analytically in the general case. We also give particular forms for the dynamics for those interested in applying our model.     

Semiparametric estimation of a bivariate Tobit model

The existing semiparametric estimation literature has mainly focused on univariate Tobit models and no semiparametric estimation has been considered for bivariate Tobit models. In this paper, we consider semiparametric estimation of the bivariate Tobit model proposed by Amemiya (1974), under the independence condition without imposing any parametric restriction on the error distribution. Our estimator is shown to be consistent and asymptotically normal, and simulation results show that our estimator performs well in finite samples. It is also worth noting that while Amemiya’s (1974) instrumental variables estimator (IV) requires the normality assumption, our semiparametric estimator actually outperforms his IV estimator even when normality holds. Our approach can be extended to higher dimensional multivariate Tobit models.     

The comparative forecast performance of univariate and multivariate models: an application to real interest rate forecasting

Does the use of information on the past history of the nominal interest rates and inflation entail improvement in forecasts of the ex ante real interest rate over its forecasts obtained from using just the past history of the realized real interest rates? To answer this question we set up a univariate unobserved components model for the realized real interest rates and a bivariate model for the nominal rate and inflation which imposes cointegration restrictions between them. The two models are estimated under normality with the Kalman filter. It is found that the error-correction model provides more accurate one-period ahead forecasts of the real rate within the estimation sample whereas the unobserved components model yields forecasts with smaller forecast variances. In the post-sample period, the forecasts from the bivariate model are not only more accurate but also have tighter confidence bounds than the forecasts from the unobserved components model.     

Estimating a generalized correlation coefficient for a generalized bivariate probit model

In this paper we consider semiparametric estimation of a generalized correlation coefficient in a generalized bivariate probit model. The generalized correlation coefficient provides a simple summary statistic measuring the relationship between the two binary decision processes in a general framework. Our semiparametric estimation procedure consists of two steps, combining semiparametric estimators for univariate binary choice models with the method of maximum likelihood for the bivariate probit model with nonparametrically generated regressors. The estimator is shown to be consistent and asymptotically normal. The estimator performs well in our simulation study.     

Recovered errors and normal diagnostics in regression

Diagnostics for normal errors in regression typically utilize ordinary residuals, despite the failure of assumptions to validate their use. Case studies here show that such misuse may be critical. A remedy invokes recovered errors having the required properties, taking into account that such errors are closer to normality than are disturbances in the observations themselves. Simulation studies show consistent improvement over the usual methods in small samples. In addition, effects on normal diagnostics due to various model violations are examined. Received: January 1999     

Estimation of sons' intergenerational earnings mobility in the presence of censoring

Based on intergenerational data from the Panel Study of Income Dynamics, I provide estimates of income mobility across generations for men. Special attention is paid to issues of censoring caused by a son's unemployment. Employing non‐parametric bounds estimates, I illustrate that previous income mobility estimates rely heavily on (unjustified) assumptions of exogenous selection. Assuming a son's potential income is instead a function of his reason for unemployment and work history, I re‐estimate mobility. Allowing for sampling variability, the range of feasible slopes consistent with these modified bounds restrictions is 0.27 to 0.55. Copyright © 2002 John Wiley & Sons, Ltd.     

Measuring the Benefits of Homeowning: Effects on Children

In this paper we examine whether homeowning benefits children by testing whether children of homeowners stay in school longer than children of renters and whether daughters of homeowners are less likely to have children as teenagers than daughters of renters. We use both probit models and a bivariate probit technique which takes account of possible selection bias due to differences between parents who choose to own versus rent. We find in several data sets that both effects are statistically significant and quantitatively important—particularly for low-income households. We also estimate that the dollar benefit per low- income household of parents being homeowners rather than renters is at least $31,000.     

Returns to specialization,competition, population,and growth

Using an expanding-variety endogenous growth model with purposive human capital accumulation, this paper provides an alternative explanation of why we may observe an ambiguous correlation between product market competition (PMC) and economic growth, and between population and economic growth rates. Our explanation is based on the notion of ‘returns to specialization’. Under the model's assumptions, PMC and economic growth are ambiguously correlated when returns to specialization are decreasing, whereas population growth and productivity growth are ambiguously correlated when returns to specialization are increasing. From a theoretical point of view, these results are explained by the presence or absence of an ‘increasing production-complexity’ effect associated to the use of a larger number of intermediate-input varieties in the same production process.     

Identification and estimation with contaminated data: When do covariate data sharpen inference?

Contaminated or corrupted data typically require strong assumptions to identify parameters of interest. However, weaker assumptions often identify bounds on these parameters. This paper addresses when covariate data—variables in addition to the one of interest—tighten these bounds. First, we construct the identification region for the distribution of the variable of interest. This region demonstrates that covariate data are useless without knowledge about the distribution of erroneous data conditional on the covariates. Then, we develop bounds both on probabilities and on parameters of this distribution that respect stochastic dominance.     

Strategic management of worker health,safety, and environmental issues in Mexico's Maquiladora industry

Conflict arising from differences in business practices, standards, values, and norms guiding behavior are inherent in international business. These conflicts are evident in Mexico's maquiladora industry, particularly relating to issues of worker health, safety, and environment. This article extends a model of conflict management; illustrates the model's dimensions with case study examples; discusses strategic human resource management implications of the model's various outcomes; offers recommendations for managing worker health, safety, and environment issues; and identifies directions for future research. © 1993 by John Wiley & Sons, Inc.     

A real options approach to optimal bidding in construction projects considering volume uncertainty

Building on the numerical solution by Ribeiro et al. (2108), this paper proposes a model to assess the impact of volume uncertainty on construction projects' value and on the optimal bidding price. The model's outcome is the threshold amount for the incremental investment that managers have to undertake in order to resolve the uncertainty regarding the volume of work to be performed. Any amount of investment below the threshold will add value to the project and produces a more competitive bid price. A numerical example is presented, and a sensitivity analysis is performed to the model's most relevant components.     

Non-causality in bivariate binary time series

In this paper we develop a dynamic discrete-time bivariate probit model, in which the conditions for Granger non-causality can be represented and tested. The conditions for simultaneous independence are also worked out. The model is extended in order to allow for covariates, representing individual as well as time heterogeneity. The proposed model can be estimated by Maximum Likelihood. Granger non-causality and simultaneous independence can be tested by Likelihood Ratio or Wald tests. A specialized version of the model, aimed at testing Granger non-causality with bivariate discrete-time survival data is also discussed. The proposed tests are illustrated in two empirical applications.     

A qualitative response analysis of UK firms' employment and output decisions

This paper investigates firms' employment and output decisions and presents some empirical evidence concerning the rationality of firms' expectations. The dymanic model is based on the assumptions of convex adjustment costs and monopolistic competition in the product market. The results are obtained using categorical information on individual firms contained in business surveys collected by the Confederation of British Industry. Ordered probit models are used to estimate the employment and output equations. We find that expected demand is a critical determinant of firms' decisions, the effect of changes in cost conditions is not as well determined, and the data are not consistent with the rational expectations hypothesis.     

The impact of obesity on employment

Using data from two rounds of the Health Survey for England I investigate the impact of obesity on employment. I use three approaches: a univariate probit model; propensity score matching; and IV regression using a recursive bivariate probit model. Conditional on a comprehensive set of covariates, the findings show that obesity has a statistically significant and negative effect on employment in both males and females. In males the endogeneity of obesity does not significantly affect the estimates, and the magnitude of effect is similar across the three methods. In females, failure to account for endogeneity leads to underestimation of the negative impact of obesity on employment.     

Effects of Household Consumption Patterns on CO2 Requirements

In order to evaluate the relation between the consumption pattern of various household types and their CO₂ requirements, we combine input-output tables energy flow matrices, CO₂ emissions factors, and national consumer survey statistics into an integrated modelling framework, and relate differences in household types to differences in private consumption and again to differences in CO₂ emissions. We identify household characteristics with a significant influence on CO₂ emissions. Comparing our results with those of other studies reveals that national differences in climate and population density cause differences in the contribution to CO₂ emissions. Finally, national differences in income and expenditure elasticities of both energy and CO₂ are due to differences in the disparity in CO₂ intensities amongst commodities and to the model's assumptions on foreign technology.     

Type I error and test power of different tests for testing interaction effects in factorial experiments

A simulation study was conducted to investigate the effect of non normality and unequal variances on Type I error rates and test power of the classical factorial anova F‐test and different alternatives, namely rank transformation procedure (FR), winsorized mean (FW), modified mean (FM) and permutation test (FP) for testing interaction effects. Simulation results showed that as long as no significant deviation from normality and homogeneity of the variances exists, generally all of the tests displayed similar results. However, if there is significant deviation from the assumptions, the other tests are observed to be affected at considerably high levels except FR and FP tests. As a result, when the assumptions of factorial anova F‐test are not met or, in the case those assumptions are not tested whether met, it can be concluded that using FR and FP tests is more suitable than the classical factorial anova F‐test.     

Are You All Normal? It Depends!

The assumption of normality has underlain much of the development of statistics, including spatial statistics, and many tests have been proposed. In this work, we focus on the multivariate setting and first review the recent advances in multivariate normality tests for i.i.d. data, with emphasis on the skewness and kurtosis approaches. We show through simulation studies that some of these tests cannot be used directly for testing normality of spatial data. We further review briefly the few existing univariate tests under dependence (time or space), and then propose a new multivariate normality test for spatial data by accounting for the spatial dependence. The new test utilises the union-intersection principle to decompose the null hypothesis into intersections of univariate normality hypotheses for projection data, and it rejects the multivariate normality if any individual hypothesis is rejected. The individual hypotheses for univariate normality are conducted using a Jarque–Bera type test statistic that accounts for the spatial dependence in the data. We also show in simulation studies that the new test has a good control of the type I error and a high empirical power, especially for large sample sizes. We further illustrate our test on bivariate wind data over the Arabian Peninsula.     

Information Precision and Common Value Partnership Dissolution: An Experimental Study

This paper develops a simple model of ½ auctions in a partnership dissolution game with common values, analyzes how information precision affects the equilibrium, and uses a laboratory study to test the model's predictions. Consistent with the model's prediction, the study shows that when bidders have the same quality information, higher precision leads to more extreme bids. In the case of unequal precision, bidders must use the same bidding functions regardless of their own information quality, and thus, the equilibrium strategy should be the same for both bidders. The experimental results, however, contradicts this prediction. Copyright © 2016 John Wiley & Sons, Ltd.