A simple approach for the analysis of generalizea linear mixed models

A broad class of generalized linear mixed models, e.g. variance components models for binary data, percentages or count data, will be introduced by incorporating additional random effects into the linear predictor of a generalized linear model structure. Parameters are estimated by a combination of quasi-likelihood and iterated MINQUE (minimum norm quadratic unbiased estimation), the latter being numerically equivalent to REML (restricted, or residual, maximum likelihood). First, conditional upon the additional random effects, observations on a working variable and weights are derived by quasi-likelihood, using iteratively re-weighted least squares. Second, a linear mixed model is fitted to the working variable, employing the weights for the residual error terms, by iterated MINQUE. The latter may be regarded as a least squares procedure applied to squared and product terms of error contrasts derived from the working variable. No full distributional assumptions are needed for estimation. The model may be fitted with standardly available software for weighted regression and REML.     

Analysis of a mixed model for ordinal data by iterative re-weighted REML

An estimation procedure will be presented for a class of threshold models for ordinal data. These models may include both fixed and random effects with associated components of variance on an underlying scale. The residual error distribution on the underlying scale may be rendered greater flexibility by introducing additional shape parameters, e.g. a kurtosis parameter or parameters to model heterogeneous residual variances as a function of factors and covariates. The estimation procedure is an extension of an iterative re-weighted restricted maximum likelihood procedure, originally developed for generalized linear mixed models. This procedure will be illustrated with a practical problem involving damage to potato tubers and with data from animal breeding and medical research from the literature.     

Exchange Rate Pass‐Through And The Welfare Effects Of The Euro*

This article explores the implications of the European single currency within a simple sticky price intertemporal model. We focus on the question of how the euro may change the sensitivity of consumer prices in Europe to exchange‐rate changes. Our central conjecture is that the acceptance of the euro will lead European prices to become more insulated from exchange‐rate volatility. We find that this affects both the volatility and levels of macroeconomic aggregates in both the U.S. and Europe. We find that European welfare is enhanced, and the U.S. shares in Europe's good fortune.     

Global food demand,productivity growth,and the scarcity of land and water resources: a spatially explicit mathematical programming approach

In the coming decades, an increasing competition for global land and water resources can be expected, due to rising demand for food and bio‐energy production, biodiversity conservation, and changing production conditions due to climate change. The potential of technological change in agriculture to adapt to these trends is subject to considerable uncertainty. In order to simulate these combined effects in a spatially explicit way, we present a model of agricultural production and its impact on the environment (MAgPIE). MAgPIE is a mathematical programming model covering the most important agricultural crop and livestock production types in 10 economic regions worldwide at a spatial resolution of three by three degrees, i.e., approximately 300 by 300 km at the equator. It takes regional economic conditions as well as spatially explicit data on potential crop yields and land and water constraints into account and derives specific land‐use patterns for each grid cell. Shadow prices for binding constraints can be used to valuate resources for which in many places no markets exist, especially irrigation water. In this article, we describe the model structure and validation. We apply the model to possible future scenarios up to 2055 and derive required rates of technological change (i.e., yield increase) in agricultural production in order to meet future food demand.     

Dynamic Adjustment of Eco‐labeling Schemes and Consumer Choice – the Revision of the EU Energy Label as a Missed Opportunity?

Products with a superior environmental performance, such as a high level of energy efficiency, are typically subject to information asymmetries. Therefore these product attributes are often undervalued in purchase decisions. Signaling, e.g. energy labeling, can overcome these asymmetries, with positive implications for effective consumer decisions, competitive advantage for suppliers of energy‐efficient goods, and for societal goals such as mitigating climate change. However, there is a scarcity of research investigating how energy labels actually influence consumer choice. The recent revision of the European Union energy label provided a unique opportunity to investigate the effectiveness of energy labeling in a quasi field‐experimental setting. We show that the proposed extension of the seven‐point A–G rating scale by adding new classes A+, A++, etc. will result in a lower perceived importance of energy efficiency in consumer decision‐making. Based on a stated preference survey investigating 2244 choices by German consumers, we conclude that the revision actually undermines the label's ability to overcome information asymmetries, hence potentially contributing to market failure. Copyright © 2011 John Wiley & Sons, Ltd and ERP Environment.