Composition and diversity of Ephemeroptera (Insecta) nymph communities in the middle section of the Jacuí River and some tributaries, southern Brazil

The taxonomic composition and diversity of assemblages of Ephemeroptera nymphs of four lotic environments in the central region of State of Rio Grande do Sul, a subtropical area in southern Brazil, were evaluated. Samplings were done monthly, with a Surber sampler, from June 2001 to May 2002, in the Jacuí River and three of its tributaries. The total number of nymphs collected in the four sampling sites was 11,007 in five families and 19 genera, of these, 11 are new records for the State. The highest diversity occurred in Point 4 (H'=2.41) and the lowest in Point 2 (H'=1.69). Point 4 had the highest environmental stability, conservation of the riparian vegetation and the lowest anthropic impact, while Point 2 presented a large environmental simplification due to a direct anthropic influence (e.g. domestic sewerage, trampling by cattle). The diversity of nymphs observed in the total area is high, compared to the estimated maximum theoretical diversity; a result of the high evenness and richness recorded. Rarefaction curves, calculated for a sample of 1,018 specimens, showed a similar expectation of richness for the four sampling sites. This result seems to be associated with the overall environmental homogeneity of the region caused by long-term alterations (land use and deforestation). In summary, higher diversity of Ephemeroptera nymph assemblages seems to be associated with habitat complexity, a good vegetation cover and a lower anthropic influence.

Firm Size and Geographical Aggregation: An Empirical Appraisal in Industrial Location

This paper assesses empirically the importance of size discrimination and disaggregate data for deciding where to locate a start-up concern. We compare three econometric specifications using Catalan data: a multinomial logit with 4 and 41 alternatives (provinces and comarques, respectively) in which firm size is the main covariate; a conditional logit with 4 and 41 alternatives including attributes of the sites as well as size-site interactions; and a Poisson model on the comarques and the full spatial choice set (942 municipalities) with site-specific variables. Our results suggest that if these two issues are ignored, conclusions may be misleading. We provide evidence that large and small firms behave differently and conclude that Catalan firms tend to choose between comarques rather than between municipalities. Moreover, labour-intensive firms seem more likely to be located in the city of Barcelona. Keywords: Catalonia, industrial location, multinomial response model. JEL: C250, E30, R00, R12

The optimal behaviour of firms facing stochastic costs

This paper aims at assessing the optimal behavior of a firm facing stochastic costs of production. In an imperfectly competitive setting, we evaluate to what extent a firm may decide to locate part of its production in other markets different from which it is actually settled. This decision is taken in a stochastic environment. Portfolio theory is used to derive the optimal solution for the intertemporal profit maximization problem. In such a framework, splitting production between different locations may be optimal when a firm is able to charge different prices in the different local markets.

Likelihood-based approaches to modeling demand for medical care

We review recent likelihood-based approaches to modeling demand for medical care. A semi-nonparametric model along the lines of Cameron and Johansson's Poisson polynomial model, but using a negative binomial baseline model, is introduced. We apply these models, as well a semiparametric Poisson, hurdle semiparametric Poisson, and finite mixtures of negative binomial models to six measures of health care usage taken from the Medical Expenditure Panel survey. We conclude that most of the models lead to statistically similar results, both in terms of information criteria and conditional and unconditional prediction. This suggests that applied researchers may not need to be overly concerned with the choice of which of these models they use to analyze data on health care demand.

Asymmetries in the capacity-inflation trade-off

This paper analyzes the joint dynamics of two key macroeconomic variables for the conduct of monetary policy: inflation and the aggregate capacity utilization rate. An econometric procedure useful for estimating dynamic rational expectation models with unobserved components is developed and applied in this context. The method combines the flexibility of the unobserved components approach, based on the Kalman recursion, with the power of the general method of moments estimation procedure. A 'hyb id' Phillips curve relating inflation to the capacity utilization gap and incorporating forward and backward looking components is estimated. The results show that such a relationship in non-linear: the slope of the Phillips curve depends significantly on the magnitude of the capacity gap. These findings provide support for studying the implications of asymmetricmonetary policy rules.

Petrol consumption and redistributive effects of its taxation in Spain

The objective of this paper is to estimate a petrol consumption function for Spain and to evaluate the redistributive effects of petrol taxation. We use micro data from the Spanish Household Budget Survey of 1990/91 and model petrol consumption taking into account the effect that income changes may have on car ownership levels, as well as the differences that exist between expenditure and consumption. Our results show the importance that household structure, place of residence and income have on petrol consumption. We are able to compute income elasticities of petrol expenditure, both conditional and unconditional on the level of car ownership. Non-conditional elasticities, while always very close to unit values, are lower for higher income households and for those living in rural areas or small cities. When car ownership levels are taken into account, conditional elasticities are obtained that are around one half the value of the non- conditional ones, being fairly stable across income categories and city sizes. As regards the redistributive effects of petrol taxation, we observe that for the lowest income deciles the share of petrol expenditure increases with income, and thus the tax can be regarded as progressive. However, after a certain income level the tax proves to be regressive.

Demand elasticity and market power in the spanish electricity market

In this paper we check whether generator's bid behavior at the Spanish whosale electricity market is consistent with the hypothesis of profit maximization on their residual demands. Using OMEL data, we find the arc-elacticity of the residual demand around the system marginal price. The results suggest thet the larger firms are not actually profit-msximization. We argue how the regulatory environment may drive these results. Finally, we repeat the analysis for the first session of the intra-day market where presumably firms may not have the same incentives as in the day-ahead market.

Learning from Shocks and the Decision to Open

Two claims pervade the literature on the political economy of market reforms: that economic crises cause reforms; and that crises matter because they bring into question the validity of the economic model held to be responsible for them. Economic crises are said to spur a process of learning that is conducive to the abandonment of failing models and to the adoption of successful models. But although these claims have become the conventional wisdom, they have been hardly tested empirically due to the lack of agreement on what constitutes a crisis and to difficulties in measuring learning from them. I propose a model of rational learning from experience and apply it to the decision to open the economy. Using data from 1964 through 1990, I show that learning from the 1982 debt crisis was relevant to the first wave of adoption of an export promotion strategy, but learning was conditional on the high variability of economic outcomes in countries that opened up to trade. Learning was also symbolic in that the sheer number of other countries that liberalized was a more important driver of others’ decisions to follow suit.

Incentives for interdisciplinary research

This paper is a positive analysis of the driving forces in interdisciplinary research. I take the perspective of a research institution that has to decide how to apply its resources among the production of two types of knowledge: specialized or interdisciplinary. Using a prize mechanism of compensation, I show that the choice of interdisciplinarity is compatible with profit maximization when the requirement for the production is sufficiently demanding, and when the new interdisciplinary field is not too neutral. Productive gains due to complementarities of efforts is the main advantage of interdisciplinary organization.

Estimation of dynamic latent variable models using simulated nonparametric moments

Given a model that can be simulated, conditional moments at a trial parameter value can be calculated with high accuracy by applying kernel smoothing methods to a long simulation. With such conditional moments in hand, standard method of moments techniques can be used to estimate the parameter. Since conditional moments are calculated using kernel smoothing rather than simple averaging, it is not necessary that the model be simulable subject to the conditioning information that is used to define the moment conditions. For this reason, the proposed estimator is applicable to general dynamic latent variable models. Monte Carlo results show that the estimator performs well in comparison to other estimators that have been proposed for estimation of general DLV models.

Restriction site-associated DNA sequencing, genotyping error estimation and de novo assembly optimization for population genetic inference.

Restriction site-associated DNA sequencing (RADseq) provides researchers with the ability to record genetic polymorphism across thousands of loci for nonmodel organisms, potentially revolutionizing the field of molecular ecology. However, as with other genotyping methods, RADseq is prone to a number of sources of error that may have consequential effects for population genetic inferences, and these have received only limited attention in terms of the estimation and reporting of genotyping error rates. Here we use individual sample replicates, under the expectation of identical genotypes, to quantify genotyping error in the absence of a reference genome. We then use sample replicates to (i) optimize de novo assembly parameters within the program Stacks, by minimizing error and maximizing the retrieval of informative loci; and (ii) quantify error rates for loci, alleles and single-nucleotide polymorphisms. As an empirical example, we use a double-digest RAD data set of a nonmodel plant species, Berberis alpina, collected from high-altitude mountains in Mexico.

Loss of Cutaneous TSLP-Dependent Immune Responses Skews the Balance of Inflammation from Tumor Protective to Tumor Promoting.

Inflammation can promote or inhibit cancer progression. In this study we have addressed the role of the proinflammatory cytokine thymic stromal lymphopoietin (TSLP) during skin carcinogenesis. Using conditional loss- and gain-of-function mouse models for Notch and Wnt signaling, respectively, we demonstrate that TSLP-mediated inflammation protects against cutaneous carcinogenesis by acting directly on CD4 and CD8 T cells. Genetic ablation of TSLP receptor (TSLPR) perturbs T-cell-mediated protection and results in the accumulation of CD11b(+)Gr1(+) myeloid cells. These promote tumor growth by secreting Wnt ligands and augmenting β-catenin signaling in the neighboring epithelium. Epithelial specific ablation of β-catenin prevents both carcinogenesis and the accumulation of CD11b(+)Gr1(+) myeloid cells, suggesting tumor cells initiate a feed-forward loop that induces protumorigenic inflammation.

Quantifying consistency of Event Related Potentials across subjects based on single-trial topographic analysis

Introduction: Non-invasive brain imaging techniques often contrast experimental conditions across a cohort of participants, obfuscating distinctions in individual performance and brain mechanisms that are better characterised by the inter-trial variability. To overcome such limitations, we developed topographic analysis methods for single-trial EEG data [1]. So far this was typically based on time-frequency analysis of single-electrode data or single independent components. The method's efficacy is demonstrated for event-related responses to environmental sounds, hitherto studied at an average event-related potential (ERP) level. Methods: Nine healthy subjects participated to the experiment. Auditory meaningful sounds of common objects were used for a target detection task [2]. On each block, subjects were asked to discriminate target sounds, which were living or man-made auditory objects. Continuous 64-channel EEG was acquired during the task. Two datasets were considered for each subject including single-trial of the two conditions, living and man-made. The analysis comprised two steps. In the first part, a mixture of Gaussians analysis [3] provided representative topographies for each subject. In the second step, conditional probabilities for each Gaussian provided statistical inference on the structure of these topographies across trials, time, and experimental conditions. Similar analysis was conducted at group-level. Results: Results show that the occurrence of each map is structured in time and consistent across trials both at the single-subject and at group level. Conducting separate analyses of ERPs at single-subject and group levels, we could quantify the consistency of identified topographies and their time course of activation within and across participants as well as experimental conditions. A general agreement was found with previous analysis at average ERP level. Conclusions: This novel approach to single-trial analysis promises to have impact on several domains. In clinical research, it gives the possibility to statistically evaluate single-subject data, an essential tool for analysing patients with specific deficits and impairments and their deviation from normative standards. In cognitive neuroscience, it provides a novel tool for understanding behaviour and brain activity interdependencies at both single-subject and at group levels. In basic neurophysiology, it provides a new representation of ERPs and promises to cast light on the mechanisms of its generation and inter-individual variability.

Three essays on the psychology of investment and financial markets

Préface My thesis consists of three essays where I consider equilibrium asset prices and investment strategies when the market is likely to experience crashes and possibly sharp windfalls. Although each part is written as an independent and self contained article, the papers share a common behavioral approach in representing investors preferences regarding to extremal returns. Investors utility is defined over their relative performance rather than over their final wealth position, a method first proposed by Markowitz (1952b) and by Kahneman and Tversky (1979), that I extend to incorporate preferences over extremal outcomes. With the failure of the traditional expected utility models in reproducing the observed stylized features of financial markets, the Prospect theory of Kahneman and Tversky (1979) offered the first significant alternative to the expected utility paradigm by considering that people focus on gains and losses rather than on final positions. Under this setting, Barberis, Huang, and Santos (2000) and McQueen and Vorkink (2004) were able to build a representative agent optimization model which solution reproduced some of the observed risk premium and excess volatility. The research in behavioral finance is relatively new and its potential still to explore. The three essays composing my thesis propose to use and extend this setting to study investors behavior and investment strategies in a market where crashes and sharp windfalls are likely to occur. In the first paper, the preferences of a representative agent, relative to time varying positive and negative extremal thresholds are modelled and estimated. A new utility function that conciliates between expected utility maximization and tail-related performance measures is proposed. The model estimation shows that the representative agent preferences reveals a significant level of crash aversion and lottery-pursuit. Assuming a single risky asset economy the proposed specification is able to reproduce some of the distributional features exhibited by financial return series. The second part proposes and illustrates a preference-based asset allocation model taking into account investors crash aversion. Using the skewed t distribution, optimal allocations are characterized as a resulting tradeoff between the distribution four moments. The specification highlights the preference for odd moments and the aversion for even moments. Qualitatively, optimal portfolios are analyzed in terms of firm characteristics and in a setting that reflects real-time asset allocation, a systematic over-performance is obtained compared to the aggregate stock market. Finally, in my third article, dynamic option-based investment strategies are derived and illustrated for investors presenting downside loss aversion. The problem is solved in closed form when the stock market exhibits stochastic volatility and jumps. The specification of downside loss averse utility functions allows corresponding terminal wealth profiles to be expressed as options on the stochastic discount factor contingent on the loss aversion level. Therefore dynamic strategies reduce to the replicating portfolio using exchange traded and well selected options, and the risky stock.

Innovation sources and productivity in Catalonian firms: a quantile regression analysis

This paper explores the effects of two main sources of innovation -intramural and external R&D- on the productivity level in a sample of 3,267 Catalonian firms. The data set used is based on the official innovation survey of Catalonia which was a part of the Spanish sample of CIS4, covering the years 2002-2004. We compare empirical results by applying usual OLS and quantile regression techniques both in manufacturing and services industries. In quantile regression, results suggest different patterns at both innovation sources as we move across conditional quantiles. The elasticity of intramural R&D activities on productivity decreased when we move up the high productivity levels both in manufacturing and services sectors, while the effects of external R&D rise in high-technology industries but are more ambiguous in low-technology and knowledge-intensive services. JEL codes: O300, C100, O140. Keywords: Innovation sources, R&D, Productivity, Quantile regression