Reliable Inference in Categorical Regression Analysis for Non‐randomly Coarsened Observations

In most surveys, one is confronted with missing or, more generally, coarse data. Traditional methods dealing with these data require strong, untestable and often doubtful assumptions, for example, coarsening at random. But due to the resulting, potentially severe bias, there is a growing interest in approaches that only include tenable knowledge about the coarsening process, leading to imprecise but reliable results. In this spirit, we study regression analysis with a coarse categorical‐dependent variable and precisely observed categorical covariates. Our (profile) likelihood‐based approach can incorporate weak knowledge about the coarsening process and thus offers a synthesis of traditional methods and cautious strategies refraining from any coarsening assumptions. This also allows a discussion of the uncertainty about the coarsening process, besides sampling uncertainty and model uncertainty. Our procedure is illustrated with data of the panel study ‘Labour market and social security' conducted by the Institute for Employment Research, whose questionnaire design produces coarse data.     

Family income and children's education: Using the Norwegian oil boom as a natural experiment

Parental income is positively correlated with children's educational attainment. This paper addresses the causality of this observed link. We have a unique data set for Norwegians born in the period 1967–1969, with a measure of permanent family income in the children's adolescence. This enables us to examine the long-term effect of family income on children's educational attainment. The Norwegian oil shock in the 1970s is used as an instrument, because this – in some regions but not in others – implied a general increase in income unrelated to parents' abilities. This variation in income is used to estimate the causal effect of family income on children's educational attainment. We find no such causal relationship. This result is robust with respect to different specification tests.     

Elasticity estimates of farmed salmon demand in Spain and Italy

In this paper, estimates of the elasticities that characterize the structure of demand for farmed salmon in Spain and Italy are reported. The demand models are specified using a Box-Cox transformation of the variables and a Hausman test is used to determine price endogeneity in the demand equations. The results show short-run unitary own-price elasticity of demand for farmed salmon in both markets, but long run estimates show significant elastic price response. Short run substitution of salmon for other fish species is not observed and, for both Spain and Italy, farmed salmon is characterized as a luxury good. Interestingly, we show that our a priori expectations about own-price elasticities being lower in smaller market areas is confirmed. Finally, the results obtained are compared to other recent results reported in the salmon demand literature.     

Commodity Price Volatility and the Sources of Growth

This paper studies the impact of the growth and volatility of commodity terms of trade (CToT) on economic growth, total factor productivity, physical capital accumulation and human capital acquisition. We use the standard system generalized methods of moments (GMM) approach as well as the dynamic common correlated effects pooled mean group (CCEPMG) methodology for estimation to account for cross‐country heterogeneity, cross‐sectional dependence and feedback effects. Using both annual data for 1970–2007 and 5‐year non‐overlapping observations, we find that while CToT growth enhances real output per capita, CToT volatility exerts a negative impact on economic growth operating mainly through lower accumulation of physical and human capital. Productivity, however, is not affected by either the growth or the volatility of CToT. Our results also indicate that the negative growth effects of CToT volatility offset the positive impact of commodity booms. Therefore, we argue that volatility, rather than abundance per se, drives the ‘resource curse’ paradox. Copyright © 2014 John Wiley & Sons, Ltd.     

Locally Most Powerful and Other Rank Tests for Independence – with a Contaminated Weighted Alternative

Several authors in the literature have attempted the quantification of the concept of stochastic dependence for bivariate distribution. Two weighted rank tests for testing independence against a weighted contamination alternative is proposed and their distributional properties are studied. We also derived a locally most powerful rank test for the alternative setting. The rank tests proposed are shown to be asymptotic locally most powerful for specific distributions.     

Dynamic capabilities through continuous improvement infrastructure

We examine the content of continuous improvement strategies and identify infrastructure decision areas that are important for continuous improvement initiatives. We present a framework of infrastructure based on the idea that continuous improvement can serve as a dynamic capability when it includes a comprehensive organizational context. Further, we study continuous improvement initiatives in five companies to investigate the practices used by them in each of the decision areas of our framework. This research adds to the conceptual understanding of continuous improvement and results in grounded propositions about critical areas of infrastructure for continuous improvement.     

Goodness-of-fit tests for parametric nonhomogeneous Markov processes

Tests for parametric nonhomogeneous and homogeneous Markov processes are given. Asymptotic distribution of test statistics is investigated. Tests for various well-known models are discussed as examples.