Using U.S. state-level data for the period 1973–1994, this study models the relationship between emissions, output and pollution abatement by defining an emissions function, in a manner that is consistent with the residual (emissions) generation mechanism and firms' optimizing behavior. It thus accounts for factors that were previously unaccounted for or addressed only individually. Applications using this comprehensive setting can offer more informed insights for policy-making, something that is particularly useful for developing countries that face the environmental degradation that comes together with the benefits of economic growth. Using nonparametric econometric techniques as well as threshold regression, the empirical results show that there is a positive nonlinear relationship between emissions and output, rejecting an inverted-U type of relationship between the two (the Environmental Kuznets Curve, or EKC). In the absence of abatement the relationship turns around, verifying the arguments in the literature that abatement is one of the driving forces for an EKC to emerge.

Measuring the causal impact of state behavior on outcomes is one of the biggest methodological challenges in the field of political science, for two reasons: behavior is generally endogenous, and the threat of unobserved variables that confound the relationship between behavior and outcomes is pervasive. Matching methods, widely considered to be the state of the art in causal inference in political science, are generally ill-suited to inference in the presence of unobserved confounders. Heckman-style multiple-equation models offer a solution to this problem; however, they rely on functional-form assumptions that can produce substantial bias in estimates of average treatment effects. We describe a category of models, flexible joint likelihood models, that account for both features of the data while avoiding reliance on rigid functional-form assumptions. We then assess these models’ performance in a series of neutral simulations, in which they produce substantial (55% to ${>}$90%) reduction in bias relative to competing models. Finally, we demonstrate their utility in a reanalysis of Simmons’ (2000) classic study of the impact of Article VIII commitment on compliance with the IMF’s currency-restriction regime.

We propose an estimator for the cumulative distribution function G of the sojourn time in a steady-state M/G/∞ queueing system, when the available data consists of the arrival and departure epochs alone, without knowing which arrival corresponds to which departure. The estimator generalizes an estimator proposed in Brown (1970), and is based on a functional relationship between G and the distribution function of the time between a departure and the rth latest arrival preceding it. The estimator is shown to outperform Brown's estimator, especially when the system is heavily loaded.

We consider a failure hazard function,conditional on a time-independent covariate Z,given by $\eta_{\gamma^0}(t)f_{\beta^0}(Z)$ . The baseline hazardfunction $\eta_{\gamma^0}$ and the relative risk $f_{\beta^0}$ both belong to parametricfamilies with $\theta^0=(\beta^0,\gamma^0)^\top\in \mathbb{R}^{m+p}$ . The covariate Z has an unknown density and is measured with an error through anadditive error model U = Z + ε where ε is a random variable, independent from Z, withknown density $f_\varepsilon$ .We observe a n-sample (Xi, Di, Ui), i = 1, ..., n, where X i isthe minimum between the failure time and the censoring time,and D i is the censoring indicator.Using least square criterion and deconvolution methods, we propose a consistent estimator of θ 0 using the observationsn-sample (Xi, Di, Ui), i = 1, ..., n.
We give an upper bound for its riskwhich depends on the smoothness properties of $f_\varepsilon$ and $f_\beta(z)$ as afunction of z, and we derive sufficient conditionsfor the $\sqrt{n}$ -consistency.We give detailed examples consideringvarious type of relative risks $f_{\beta}$ and various types of errordensity $f_\varepsilon$ . In particular, in the Cox model and inthe excess risk model, the estimator of θ 0 is $\sqrt{n}$ -consistent and asymptotically Gaussianregardless of the form of $f_\varepsilon$ .