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Supposing that decisionmakers in any country and at any point in time tolerate a certain fixed level of perceived poverty, differences in poverty aversion are called for to explain observed international and intertemporal variations in poverty statistics. Under the Natural Rate of Subjective Poverty hypothesis advanced in this paper, variations in the degree of poverty aversion are estimable and can be explained by political and socioeconomic factors. The methodology is applied to US data from 1975 to 1998 and across nations using cross-section data from the mid-1990s. Factors such as the political affiliation of government officials, public expenditure, per capita income, and economic growth account for much of the variation in poverty aversion implied by our hypothesis. The relationship between inequality aversion and poverty aversion is also explored, with the aid of a parallel “natural rate” hypothesis for inequality (Lambert et al., 2003). Our findings provide a new framework in which to interpret observed correlations between poverty, inequality, and social welfare.

For equity, societies may wish to eliminate certain forms or manifestations of inequality. Horizontal equity and vertical equity in the income tax are topics which have interested me for some years. Although any shortfall from each of these objectives can be measured in terms of unwanted inequalities, equity per se is a different concept from equality. Equity relates to fairness, justice and other societal norms which give expression to the best aspirations of our collective social conscience. For example, equal access to health care for those in equal need is an accepted norm for horizontal equity in the health field. Vertical equity in this context means treating appropriately differently those who have different needs. When offered the opportunity to be Guest Editor of this volume of Research on Economic Inequality, I decided to define the focus simply as “equity”, without placing any further restriction on topics. The papers which were ultimately included in this volume are the ones, from among those offered, which survived a rigorous refereeing process. Each has its own “take” on the concept of equity, and its link with equality. I hope that you, the reader, will gain from reading all of these contributions and pondering their significance.

“The Elephant in the Corner: A Cautionary Tale About Measurement Error in Treatment Effects Models” by Daniel L. Millimet discusses the current use of the unobserved-outcome framework to estimate population-averaged treatment effects, and it exposes the sensitivity of these estimators to assumption of no measurement error. The Monte Carlo simulation evidence in this chapter indicates that “nonclassical measurement error in the covariates, mean-reverting measurement error in the outcome, and simultaneous measurement errors in the outcome, treatment assignment, and covariates have a dramatic, adverse effect on the performance of the various estimators even with relatively small and infrequent errors” (Millimet article, p. 1–39). To some extent, all the estimators analyzed by Millimet are based on weak functional form assumptions and use semiparametric or nonparametric methods. Millimet's results indicate the need for measurement error models be they parametric or nonparametric models, see Schennach (2007), Hu and Schennach (2008), and Matzkin (2007) for some recent research in nonparametric approaches. Chapter 7 develops a Bayesian estimator that can handle some of the measurement errors discussed in this chapter.

On March 24, 2012, we had one of the greatest surprises of our lives. We walked into the Lod Cook Conference Center on the LSU campus, to find scores of people, including our friend, mentor and major Professor Stanley R. Johnson, and many old colleagues and students. This event, in honor of the 30th volume of Advances in Econometrics, was engineered by our friend Dek Terrell, with the aide of his spouse, Dannielle Lewis, our spouses Nancy Fomby and Melissa Waters, Julianna Richard of LSU, Daniel Millimet of SMU, and our former Emerald Press editor Emma Whitfield. True to our nature we were oblivious to the nine months of planning and work that had gone into preparing the event. These cunning and diabolical people, obviously skilled in hiding the truth and keeping poker faces through it all, had organized a surprise conference! Not a birthday party, not a dinner, but a weekend conference with participants from all over the world! We are amazed, and honored.

The collection of chapters in this 30th volume of Advances in Econometrics provides a well-deserved tribute to Thomas B. Fomby and R. Carter Hill, who have served as editors of the Advances in Econometrics series for 25 and 21 years, respectively. Volume 30 contains a more varied collection of chapters than previous volumes, in essence mirroring the wide variety of econometric topics covered by the series over 30 years. Volume 30 starts with a chapter discussing the history of this series over the last 30 years. The next five chapters can be broadly categorized as focusing on model specification and testing. Following this section are three contributions that examine instrumental variables models in quite different settings. The next four chapters focus on applied macroeconomics topics. The final chapter offers a practical guide to conducting Monte Carlo simulations.

We analyze Lagrange Multiplier (LM) tests for a shift in trend of a univariate time series at an unknown date. We focus on the class of LM statistics based on nonparametric kernel estimates of the long run variance. Extending earlier work for models with nontrending data, we develop a fixed-b asymptotic theory for the statistics. The fixed-b theory suggests that, for a given statistic, kernel, and significance level, there usually exists a bandwidth such that the fixed-b asymptotic critical value is the same for both I(0) and I(1) errors. These “robust” bandwidths are calculated using simulation methods for a selection of well-known kernels. We find when the robust bandwidth is used, the supremum statistic configured with either the Bartlett or Daniell kernel gives LM tests with good power. When testing for a slope change, we obtain the surprising finding that less trimming of potential shift dates leads to higher power, which contrasts the usual relationship between trimming and power. Finite sample simulations indicate that the robust LM statistics have stable size with good power.