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There is a paradox in the normative foundations for chronic and intertemporal poverty measurement. Measures that reflect particular aversion to chronicity of poverty cannot also reflect particular aversion to fluctuations in the level of poverty when poverty is intense, yet good arguments are made in favour of each of these properties. I argue that the paradox may be explained if the poverty analyst implicitly predicts that an individual observed to experience persistent poverty will continue to experience poverty when unobserved. The paradox may then be resolved by separating the normative exercise of evaluation, applying a measure that reflects particular aversion to fluctuations, from a positive exercise of modelling and prediction. This proposal is illustrated by application to panel data from rural Ethiopia, covering the period 1994–2004. Several dynamic models are estimated, and a simple model with household-specific trends is found to give the best predictions of future wellbeing levels. Appropriately normalised measures of intertemporal poverty are applied to the predicted and observed trajectories of wellbeing, and results are found to differ substantially from naïve application of the measures to observed periods only. While similar results are obtained by naïve application of the measures that embody particular aversion to chronicity, separation of the normative and positive exercises maintains conceptual clarity.

Prior research suggests both formal and informal norms influence employee behavior. While increased training is a typical recommendation to strengthen formal norms by increasing adherence to organizational codes of conduct, and therefore improve ethical behavior, there is little empirical evidence that code training actually strengthens formal norms or improves ethics-related behavior. Conversely, prior observations of unethical behavior serve as strong indicators of informal norms. These observations may be unknown to management and therefore difficult to moderate using other means, including with training on a code.

We test the impact of prior observations of unethical behavior and training for a code of conduct on intentions to report unethical behavior in the future, as well as possible mediators of these relationships. We find some support that training on the code increases intention to report and strong support for the notion that prior observations of unethical behavior decrease intentions to report. Responsibility to report and norms against whistle-blowing both mediate the prior observation-to-reporting intentions relationship, but not the training-to-reporting intentions relationship. An interesting by-product of training seems to be that, by increasing awareness of unethical behavior, and therefore the salience of prior observation, training may have indirectly influenced intentions in the opposite direction intended.

A state space representation of a linearized DSGE model implies a VAR in terms of observable variables. The model is said be non-invertible if there exists no linear rotation of the VAR innovations which can recover the economic shocks. Non-invertibility arises when the observed variables fail to perfectly reveal the state variables of the model. The imperfect observation of the state drives a wedge between the VAR innovations and the deep shocks, potentially invalidating conclusions drawn from structural impulse response analysis in the VAR. The principal contribution of this chapter is to show that non-invertibility should not be thought of as an “either/or” proposition – even when a model has a non-invertibility, the wedge between VAR innovations and economic shocks may be small, and structural VARs may nonetheless perform reliably. As an increasingly popular example, so-called “news shocks” generate foresight about changes in future fundamentals – such as productivity, taxes, or government spending – and lead to an unassailable missing state variable problem and hence non-invertible VAR representations. Simulation evidence from a medium scale DSGE model augmented with news shocks about future productivity reveals that structural VAR methods often perform well in practice, in spite of a known non-invertibility. Impulse responses obtained from VARs closely correspond to the theoretical responses from the model, and the estimated VAR responses are successful in discriminating between alternative, nested specifications of the underlying DSGE model. Since the non-invertibility problem is, at its core, one of missing information, conditioning on more information, for example through factor augmented VARs, is shown to either ameliorate or eliminate invertibility problems altogether.

Research on strategic choices available to the firm are often modeled as a limited number of possible decision outcomes and leads to a discrete limited dependent variable. A limited dependent variable can also arise when values of a continuous dependent variable are partially or wholly unobserved. This chapter discusses the methodological issues associated with such phenomena and the appropriate statistical methods developed to allow for consistent and efficient estimation of models that involve a limited dependent variable. The chapter also provides a road map for selecting the appropriate statistical technique and it offers guidelines for consistent interpretation and reporting of the statistical results.

A dynamic oligopoly model of the cigarette industry is developed to study the responses of firms to various antismoking policies and to estimate the implications for the policy efficacy. The structural parameters are estimated using a combination of micro and macro level data and firms’ optimal price and advertising strategies are solved as a Markov Perfect Nash Equilibrium. The simulation results show that tobacco tax increase reduces both the overall smoking rate and the youth smoking rate, while advertising restrictions may increase the youth smoking rate. Firm’s responses strengthen the impact of antismoking policies in the short run.

Nowadays, structural equation modeling is a buzz word in the arena of research in management, social sciences, and other equivalent fields. Although the theoretical base bears its significance in building the measurement and structural models, assessing different goodness-of-fit indices (GOFI) equally retains its importance for model validity and conformity. There are various alternative GOFI available for the researchers and the threshold values of each differ. The present paper discussed all the well-accepted and reported GOFI and their threshold value, which will be a great help to researchers and practitioners who use structural equation modeling in research. The author has also presented the different GOF values and validity results of her current research carried out in an Indian power transmission organization in Odisha, India.