GMM with Weak Identification

This paper develops asymptotic distribution theory for GMM estimators and test statistics when some or all of the parameters are weakly identified. General results are obtained and are specialized to two important cases: linear instrumental variables regression and Euler equations estimation of the CCAPM. Numerical results for the CCAPM demonstrate that weak‐identification asymptotics explains the breakdown of conventional GMM procedures documented in previous Monte Carlo studies. Confidence sets immune to weak identification are proposed. We use these results to inform an empirical investigation of various CCAPM specifications; the substantive conclusions reached differ from those obtained using conventional methods.     

On the Measurements of Board Composition: Poor Consistency and a Serious Mismatch of Theory and Operationalization

Over two dozen operationalizations of board composition can be identified from the empirical literature. A structural equations confirmatory factor analysis (LISREL 8.03) suggests that these operationalizations do not constitute a single construct of board independence. Instead, analyses strongly indicate three separate constructs. Common operationalizations of board composition, then, are neither tenable surrogates for one another nor are they interchangeable. Implications for empirical aggregation of studies, theory/measurement convergence, and the current corporate governance public policy debate are discussed.     

The paradox of parental participation and legal representation in ‘edge of care’ meetings

This paper assesses the nature of parental participation and legal representation in pre‐proceedings meetings in England and Wales. These are called when a local authority is considering care proceedings on a child. The parent(s) are invited to a meeting to discuss the concerns, and are entitled to attend with a lawyer. The paper draws on findings from a study of the process which included a file survey of over 200 cases, observations of 36 meetings and interviews with more than 90 key informants, including parents. The aim of the process is (usually) to reach an agreement to prevent the case going to court, but the families are usually well known to children's services, and have been through many meetings and agreements before. What then are the possibilities for parental participation and legal representation in the meetings? The study shows that they may help bring a greater degree of clarity to the local authority's proposals, but are not expected to challenge them. Paradoxically, they serve to reinforce the authority's position. The meetings can help divert cases, but it is important to be realistic about the chances of change in these often long‐standing ‘edge of care’ cases.     

Bayesian penalized smoothing approaches in models specified using differential equations with unknown error distributions

A full Bayesian approach based on ordinary differential equation (ODE)-penalized B-splines and penalized Gaussian mixture is proposed to jointly estimate ODE-parameters, state function and error distribution from the observation of some state functions involved in systems of affine differential equations. Simulations inspired by pharmacokinetic (PK) studies show that the proposed method provides comparable results to the method based on the standard ODE-penalized B-spline approach (i.e. with the Gaussian error distribution assumption) and outperforms the standard ODE-penalized B-splines when the distribution is not Gaussian. This methodology is illustrated on a PK data set.     

Non‐parametric Estimation of Extreme Risk Measures from Conditional Heavy‐tailed Distributions

In this paper, we introduce a new risk measure, the so‐called conditional tail moment. It is defined as the moment of order a ≥ 0 of the loss distribution above the upper α‐quantile where α ∈ (0,1). Estimating the conditional tail moment permits us to estimate all risk measures based on conditional moments such as conditional tail expectation, conditional value at risk or conditional tail variance. Here, we focus on the estimation of these risk measures in case of extreme losses (where α ↓0 is no longer fixed). It is moreover assumed that the loss distribution is heavy tailed and depends on a covariate. The estimation method thus combines non‐parametric kernel methods with extreme‐value statistics. The asymptotic distribution of the estimators is established, and their finite‐sample behaviour is illustrated both on simulated data and on a real data set of daily rainfalls.     

Method of Moments Estimation in Linear Regression with Errors in both Variables

Recently, in this journal, there has been revised attention on estimating the parameters of the errors in variables, linear structural model. For example, O’Driscoll and Ramirez (2011) used a geometric approach to give insight into the performance of various slope estimators for the linear structural model as introduced by the present author. This article aims to provide a unified method of moments approach for estimating the parameters in the linear structural model, concentrating attention on estimators using the higher moments, which to date has received only little attention in the literature.     

Uncertainty and Variability in Health-Related Damages from Coal-Fired Power Plants in the United States

The health‐related damages associated with emissions from coal‐fired power plants can vary greatly across facilities as a function of plant, site, and population characteristics, but the degree of variability and the contributing factors have not been formally evaluated. In this study, we modeled the monetized damages associated with 407 coal‐fired power plants in the United States, focusing on premature mortality from fine particulate matter (PM_2.5). We applied a reduced‐form chemistry‐transport model accounting for primary PM_2.5 emissions and the influence of sulfur dioxide (SO₂) and nitrogen oxide (NO_x) emissions on secondary particulate formation. Outputs were linked with a concentration‐response function for PM_2.5‐related mortality that incorporated nonlinearities and model uncertainty. We valued mortality with a value of statistical life approach, characterizing and propagating uncertainties in all model elements. At the median of the plant‐specific uncertainty distributions, damages across plants ranged from $30,000 to $500,000 per ton of PM_2.5, $6,000 to $50,000 per ton of SO₂, $500 to $15,000 per ton of NO_x, and $0.02 to $1.57 per kilowatt‐hour of electricity generated. Variability in damages per ton of emissions was almost entirely explained by population exposure per unit emissions (intake fraction), which itself was related to atmospheric conditions and the population size at various distances from the power plant. Variability in damages per kilowatt‐hour was highly correlated with SO₂ emissions, related to fuel and control technology characteristics, but was also correlated with atmospheric conditions and population size at various distances. Our findings emphasize that control strategies that consider variability in damages across facilities would yield more efficient outcomes.     

Observing Unobservables: Identifying Information Asymmetries With a Consumer Credit Field Experiment

Information asymmetries are important in theory but difficult to identify in practice. We estimate the presence and importance of hidden information and hidden action problems in a consumer credit market using a new field experiment methodology. We randomized 58,000 direct mail offers to former clients of a major South African lender along three dimensions: (i) an initial “offer interest rate” featured on a direct mail solicitation; (ii) a “contract interest rate” that was revealed only after a borrower agreed to the initial offer rate; and (ii) a dynamic repayment incentive that was also a surprise and extended preferential pricing on future loans to borrowers who remained in good standing. These three randomizations, combined with complete knowledge of the lender's information set, permit identification of specific types of private information problems. Our setup distinguishes hidden information effects from selection on the offer rate (via unobservable risk and anticipated effort), from hidden action effects (via moral hazard in effort) induced by actual contract terms. We find strong evidence of moral hazard and weaker evidence of hidden information problems. A rough estimate suggests that perhaps 13% to 21% of default is due to moral hazard. Asymmetric information thus may help explain the prevalence of credit constraints even in a market that specializes in financing high‐risk borrowers.