Variable Selection and Functional Form Uncertainty in Cross-Country Growth Regressions

Regression analyses of cross-country economic growth data are complicated by two main forms of model uncertainty: the uncertainty in selecting explanatory variables and the uncertainty in specifying the functional form of the regression function. Most discussions in the literature address these problems independently, yet a joint treatment is essential. We perform this joint treatment by extending the linear model to allow for multiple-regime parameter heterogeneity of the type suggested by new growth theory, while addressing the variable selection problem by means of Bayesian model averaging. Controlling for variable selection uncertainty, we confirm the evidence in favor of new growth theory presented in several earlier studies. However, controlling for functional form uncertainty, we find that the effects of many of the explanatory variables identified in the literature are not robust across countries and variable selections

The determinants of football match attendance revisited: Empirical evidence from the Spanish Football League

An attendance equation is estimated using data on individual games played in the Spanish First Division Football League. The specification includes as explanatory factors: economic variables, quality, uncertainty and opportunity costs. We concentrate the analysis on some specification issues such as controlling the effect of unobservables given the panel data structure of the data set, the type of functional form and the potential endogeneity of prices. We obtain the expected effects on attendance for all the variables. The estimated price elasticities are smaller than one in absolute value as usually occurs in this literature but are sensitive to the specification issues.Attendance, price elasticity, panel data

Plant community structure mediates potential methane production and potential iron reduction in wetland mesocosms.

Abstract Wetlands are the largest natural source of methane to the atmosphere, but factors controlling methane emissions from wetlands are a major source of uncertainty in greenhouse gas budgets and projections of future climate change. We conducted a controlled outdoor mesocosm experiment to assess the effects of plant community structure (functional group richness and composition) on potential methane production and potential iron reduction in freshwater emergent marshes. Four plant functional groups (facultative annuals, obligate annuals, reeds, and tussocks) were arranged in a full-factorial design and additional mesocosms were assigned as no-plant controls. Soil samples from the top 10 cm were collected three times during the growing season to determine potential methane production and potential iron reduction (in unamended soils and in soils amended with 200 mM formate). These data were compared to soil organic matter, soil pH, and previously published data on above and belowground plant biomass. We found that functional group richness was less important than the presence of specific functional groups (reeds or tussocks) in mediating potential iron reduction. In our mesocosms, where oxidized iron was abundant and electron donors were limiting, iron reducing bacteria outcompeted methanogens, keeping methane production barely detectable in unamended lab incubations. When the possibility of re-oxidizing iron was eliminated via anaerobic incubations and the electron donor limitation was removed by adding formate, potential methane production increased and followed the same patterns as potential iron reduction. Our findings suggest that in the absence of abundant oxidized iron and/or the presence of abundant electron donors, wetlands dominated by either reeds or tussocks may have increased methane production compared to wetlands dominated by annuals. Depending on functional traits such as plant transport and rhizospheric oxygenation capacities, this could potentially lead to increased methane emissions in some wetlands. Additional research examining the role these plant functional groups play in other aspects of methane dynamics will be useful given the importance of methane as a greenhouse gas

Theory of controlled quantum dynamics

We introduce a general formalism, based on the stochastic formulation of quantum mechanics, to obtain localized quasi-classical wave packets as dynamically controlled systems, for arbitrary anharmonic potentials. The control is in general linear, and it amounts to introduce additional quadratic and linear time-dependent terms to the given potential. In this way one can construct for general systems either coherent packets moving with constant dispersion, or dynamically squeezed packets whose spreading remains bounded for all times. In the standard operatorial framework our scheme corresponds to a suitable generalization of the displacement and scaling operators that generate the coherent and squeezed states of the harmonic oscillator.Comment: LaTeX, A4wide, 28 pages, no figures. To appear in J. Phys. A: Math. Gen., April 199

Safety management theory and the military expeditionary organization: A critical theoretical reflection

Management of safety within organizations has become a key topic within safety science. Theorizing on this subject covers a diverse pallet of concepts such as “resilience” and “safety management systems”. Recent studies indicate that safety management theory has deficiencies. Our interpretation of these deficiencies is that much confusion originates from the issue that crucial meta-theoretical assumptions are mostly implicit or applied inconsistently. In particular, we argue that these meta-theoretical assumptions are of a systems theoretical nature. Therefore, we provide a framework that will be able to explicate and reflect on systems theoretical assumptions. With this framework, we analyze the ability of two frequently used safety management theories to tackle the problem of managing safety of Dutch military expeditionary organizations. This paper will show that inconsistent and implicit application of systems theoretical assumptions in these safety management theories results in problems to tackle such a practical problem adequately. We conclude with a reflection on the pros and cons of our framework. Also, we suggest particular meta-theoretical aspects that seem to be essential for applying safety management theory to organizations

Introducing alternative-based thresholding for defining functional regions of interest in fMRI

In fMRI research, one often aims to examine activation in specific functional regions of interest (fROIs). Current statistical methods tend to localize fROIs inconsistently, focusing on avoiding detection of false activation. Not missing true activation is however equally important in this context. In this study, we explored the potential of an alternative-based thresholding (ABT) procedure, where evidence against the null hypothesis of no effect and evidence against a prespecified alternative hypothesis is measured to control both false positives and false negatives directly. The procedure was validated in the context of localizer tasks on simulated brain images and using a real data set of 100 runs per subject. Voxels categorized as active with ABT can be confidently included in the definition of the fROI, while inactive voxels can be confidently excluded. Additionally, the ABT method complements classic null hypothesis significance testing with valuable information by making a distinction between voxels that show evidence against both the null and alternative and voxels for which the alternative hypothesis cannot be rejected despite lack of evidence against the null

Optimization of Gaussian Random Fields

Many engineering systems are subject to spatially distributed uncertainty, i.e. uncertainty that can be modeled as a random field. Altering the mean or covariance of this uncertainty will in general change the statistical distribution of the system outputs. We present an approach for computing the sensitivity of the statistics of system outputs with respect to the parameters describing the mean and covariance of the distributed uncertainty. This sensitivity information is then incorporated into a gradient-based optimizer to optimize the structure of the distributed uncertainty to achieve desired output statistics. This framework is applied to perform variance optimization for a model problem and to optimize the manufacturing tolerances of a gas turbine compressor blade

Single item stochastic lot sizing problem considering capital flow and business overdraft

This paper introduces capital flow to the single item stochastic lot sizing problem. A retailer can leverage business overdraft to deal with unexpected capital shortage, but needs to pay interest if its available balance goes below zero. A stochastic dynamic programming model maximizing expected final capital increment is formulated to solve the problem to optimality. We then investigate the performance of four controlling policies: ($R, Q$), ($R, S$), ($s, S$) and ($s$, $\overline{Q}$, $S$); for these policies, we adopt simulation-genetic algorithm to obtain approximate values of the controlling parameters. Finally, a simulation-optimization heuristic is also employed to solve this problem. Computational comparisons among these approaches show that policy $(s, S)$ and policy $(s, \overline{Q}, S)$ provide performance close to that of optimal solutions obtained by stochastic dynamic programming, while simulation-optimization heuristic offers advantages in terms of computational efficiency. Our numerical tests also show that capital availability as well as business overdraft interest rate can substantially affect the retailer's optimal lot sizing decisions.Comment: 18 pages, 3 figure