Extreme bounds of democracy

What determines the emergence and survival of democracy? The authors apply extreme bounds analysis to test the robustness of fifty-nine factors proposed in the literature, evaluating over three million regressions with data from 165 countries from 1976 to 2002. The most robust determinants of the transition to democracy are gross domestic product (GDP) growth (a negative effect), past transitions (a positive effect), and Organisation for Economic Co-operation and Development membership (a positive effect). There is some evidence that fuel exporters and Muslim countries are less likely to see democracy emerge, although the latter finding is driven entirely by oil-producing Muslim countries. Regarding the survival of democracy, the most robust determinants are GDP per capita (a positive effect) and past transitions (a negative effect). There is some evidence that having a former military leader as the chief executive has a negative effect, while having other democracies as neighbors has a reinforcing effect.

Looking for Validity or Testing It? The Perils of Stepwise Regression, Extreme-Scores Analysis, Heteroscedasticity, and Measurement Error

When researchers introduce a new test they have to demonstrate that it is valid, using unbiased designs and suitable statistical procedures. In this article we use Monte Carlo analyses to highlight how incorrect statistical procedures (i.e., stepwise regression, extreme scores analyses) or ignoring regression assumptions (e.g., heteroscedasticity) contribute to wrong validity estimates. Beyond these demonstrations, and as an example, we re-examined the results reported by Warwick, Nettelbeck, and Ward (2010) concerning the validity of the Ability Emotional Intelligence Measure (AEIM). Warwick et al. used the wrong statistical procedures to conclude that the AEIM was incrementally valid beyond intelligence and personality traits in predicting various outcomes. In our re-analysis, we found that the reliability-corrected multiple correlation of their measures with personality and intelligence was up to .69. Using robust statistical procedures and appropriate controls, we also found that the AEIM did not predict incremental variance in GPA, stress, loneliness, or well-being, demonstrating the importance for testing validity instead of looking for it.

A Sensitivity Analysis of Cross-Country Growth Regressions: Is 1990-2010 Different?

Cet article étudie la sensibilité des estimations de certaines variables explicatives de la croissance économique dans des régressions en coupe transversale sur un ensemble de pays. Il applique un modèle modifié de l’analyse de sensibilité de Leamer (1983, 1985). Mes résultats confirment la conclusion de Levine and Renelt (1992), toutefois, je montre que plus de variables sont solidement corrélées à la croissance économique. Entre 1990-2010, je trouve que huit sur vingt cinq variables ont des coefficients significatifs et sont solidement corrélées à la croissance de long terme, notamment, les parts de l’investissement et des dépenses étatiques dans le PIB, la primauté du droit et une variable dichotomique pour les pays subsahariens. Je trouve aussi une preuve empirique solide de l'hypothèse de la convergence conditionnelle, ce qui est cohérent avec le modèle de croissance néoclassique.

User-definable resource bounds analysis for logic programs

We present a static analysis that infers both upper and lower bounds on the usage that a logic program makes of a set of user-definable resources. The inferred bounds will in general be functions of input data sizes. A resource in our approach is a quite general, user-defined notion which associates a basic cost function with elementary operations. The analysis then derives the related (upper- and lower-bound) resource usage functions for all predicates in the program. We also present an assertion language which is used to define both such resources and resourcerelated properties that the system can then check based on the results of the analysis. We have performed some preliminary experiments with some concrete resources such as execution steps, bytes sent or received by an application, number of files left open, number of accesses to a datábase, number of calis to a procedure, number of asserts/retracts, etc. Applications of our analysis include resource consumption verification and debugging (including for mobile code), resource control in parallel/distributed computing, and resource-oriented specialization.

User-definable resource usage bounds analysis for java bytecode

Automatic cost analysis of programs has been traditionally concentrated on a reduced number of resources such as execution steps, time, or memory. However, the increasing relevance of analysis applications such as static debugging and/or certiflcation of user-level properties (including for mobile code) makes it interesting to develop analyses for resource notions that are actually application-dependent. This may include, for example, bytes sent or received by an application, number of files left open, number of SMSs sent or received, number of accesses to a datábase, money spent, energy consumption, etc. We present a fully automated analysis for inferring upper bounds on the usage that a Java bytecode program makes of a set of application programmer-deflnable resources. In our context, a resource is defined by programmer-provided annotations which state the basic consumption that certain program elements make of that resource. From these deflnitions our analysis derives functions which return an upper bound on the usage that the whole program (and individual blocks) make of that resource for any given set of input data sizes. The analysis proposed is independent of the particular resource. We also present some experimental results from a prototype implementation of the approach covering a signiflcant set of interesting resources.

Resource bounds analysis.

We present a generic analysis that infers both upper and lower bounds on the usage that a program makes of a set of user-definable resources. The inferred bounds will in general be functions of input data sizes. A resource in our approach is a quite general, user-defined notion which associates a basic cost function with elementary operations. The analysis then derives the related (upper- and lower- bound) cost functions for all procedures in the program. We also present an assertion language which is used to define both such resources and resource-related properties that the system can then check based on the results of the analysis. We have performed some experiments with some concrete resource-related properties such as execution steps, bits sent or received by an application, number of arithmetic operations performed, number of calls to a procedure, number of transactions, etc. presenting the resource usage functions inferred and the times taken to perform the analysis. Applications of our analysis include resource consumption verification and debugging (including for mobile code), resource control in parallel/distributed computing, and resource-oriented specialization.

The transformed-stationary approach: a generic and simplified methodology for non-stationary extreme value analysis

Statistical approaches to study extreme events require, by definition, long time series of data. In many scientific disciplines, these series are often subject to variations at different temporal scales that affect the frequency and intensity of their extremes. Therefore, the assumption of stationarity is violated and alternative methods to conventional stationary extreme value analysis (EVA) must be adopted. Using the example of environmental variables subject to climate change, in this study we introduce the transformed-stationary (TS) methodology for non-stationary EVA. This approach consists of (i) transforming a non-stationary time series into a stationary one, to which the stationary EVA theory can be applied, and (ii) reverse transforming the result into a non-stationary extreme value distribution. As a transformation, we propose and discuss a simple time-varying normalization of the signal and show that it enables a comprehensive formulation of non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with a constant shape parameter. A validation of the methodology is carried out on time series of significant wave height, residual water level, and river discharge, which show varying degrees of long-term and seasonal variability. The results from the proposed approach are comparable with the results from (a) a stationary EVA on quasi-stationary slices of non-stationary series and (b) the established method for non-stationary EVA. However, the proposed technique comes with advantages in both cases. For example, in contrast to (a), the proposed technique uses the whole time horizon of the series for the estimation of the extremes, allowing for a more accurate estimation of large return levels. Furthermore, with respect to (b), it decouples the detection of non-stationary patterns from the fitting of the extreme value distribution. As a result, the steps of the analysis are simplified and intermediate diagnostics are possible. In particular, the transformation can be carried out by means of simple statistical techniques such as low-pass filters based on the running mean and the standard deviation, and the fitting procedure is a stationary one with a few degrees of freedom and is easy to implement and control. An open-source MAT-LAB toolbox has been developed to cover this methodology, which is available at https://github.com/menta78/tsEva/(Mentaschi et al., 2016).

Analyse des bornes extrêmes et le contrôle des armes à feu : l’effet de la Loi C-68 sur les homicides au Québec

Contexte et objectifs. En 1995, le gouvernement canadien a promulgué la Loi C-68, rendant ainsi obligatoire l’enregistrement de toutes les armes à feu et affermissant les vérifications auprès des futurs propriétaires. Faute de preuves scientifiques crédibles, le potentiel de cette loi à prévenir les homicides est présentement remis en question. Tout en surmontant les biais potentiels retrouvés dans les évaluations antérieures, l’objectif de ce mémoire est d’évaluer l’effet de la Loi C-68 sur les homicides au Québec entre 1974 et 2006. Méthodologie. L’effet de la Loi C-68 est évalué à l’aide d’une analyse des bornes extrêmes. Les effets immédiats et graduels de la Loi sont évalués à l’aide de 372 équations. Brièvement, il s’agit d’analyses de séries chronologiques interrompues où toutes les combinaisons de variables indépendantes sont envisagées afin d’éviter les biais relatifs à une spécification arbitraire des modèles. Résultats. L’introduction de la Loi C-68 est associée à une baisse graduelle des homicides commis à l’aide d’armes longues (carabines et fusils de chasse), sans qu’aucun déplacement tactique ne soit observé. Les homicides commis par des armes à feu à autorisation restreinte ou prohibées semblent influencés par des facteurs différents. Conclusion. Les résultats suggèrent que le contrôle des armes à feu est une mesure efficace pour prévenir les homicides. L’absence de déplacement tactique suggère également que l’arme à feu constitue un important facilitateur et que les homicides ne sont pas tous prémédités. D’autres études sont toutefois nécessaires pour clairement identifier les mécanismes de la Loi responsables de la baisse des homicides.

Using extreme value theory to value stock market returns

Extreme stock price movements are of great concern to both investors and the entire economy. For investors, a single negative return, or a combination of several smaller returns, can possible wipe out so much capital that the firm or portfolio becomes illiquid or insolvent. If enough investors experience this loss, it could shock the entire economy. An example of such a case is the stock market crash of 1987. Furthermore, there has been a lot of recent interest regarding the increasing volatility of stock prices. ^ This study presents an analysis of extreme stock price movements. The data utilized was the daily returns for the Standard and Poor's 500 index from January 3, 1978 to May 31, 2001. Research questions were analyzed using the statistical models provided by extreme value theory. One of the difficulties in examining stock price data is that there is no consensus regarding the correct shape of the distribution function generating the data. An advantage with extreme value theory is that no detailed knowledge of this distribution function is required to apply the asymptotic theory. We focus on the tail of the distribution. ^ Extreme value theory allows us to estimate a tail index, which we use to derive bounds on the returns for very low probabilities on an excess. Such information is useful in evaluating the volatility of stock prices. There are three possible limit laws for the maximum: Gumbel (thick-tailed), Fréchet (thin-tailed) or Weibull (no tail). Results indicated that extreme returns during the time period studied follow a Fréchet distribution. Thus, this study finds that extreme value analysis is a valuable tool for examining stock price movements and can be more efficient than the usual variance in measuring risk. ^

I just ran four million regressions

In this paper I try to move away from the Extreme Bounds method ofidentifying ``robust'' empirical relations in the economic growth literature.Instead of analyzing the extreme bounds of the estimates of the coefficientof a particular variable, I analyze the entire distribution. My claimin this paper is that, if we do this, the picture emerging from theempirical growth literature is not the pessimistic ``Nothing is Robust''that we get with the extreme bound analysis. Instead, we find that asubstantial number of variables can be found to be strongly relatedto growth.

Understanding the processes governing climate variability and change, climate predictability and the probability of extreme events

The purpose of Research Theme 4 (RT4) was to advance understanding of the basic science issues at the heart of the ENSEMBLES project, focusing on the key processes that govern climate variability and change, and that determine the predictability of climate. Particular attention was given to understanding linear and non-linear feedbacks that may lead to climate surprises,and to understanding the factors that govern the probability of extreme events. Improved understanding of these issues will contribute significantly to the quantification and reduction of uncertainty in seasonal to decadal predictions and projections of climate change. RT4 exploited the ENSEMBLES integrations (stream 1) performed in RT2A as well as undertaking its own experimentation to explore key processes within the climate system. It was working at the cutting edge of problems related to climate feedbacks, the interaction between climate variability and climate change � especially how climate change pertains to extreme events, and the predictability of the climate system on a range of time-scales. The statisticalmethodologies developed for extreme event analysis are new and state-of-the-art. The RT4-coordinated experiments, which have been conducted with six different atmospheric GCMs forced by common timeinvariant sea surface temperature (SST) and sea-ice fields (removing some sources of inter-model variability), are designed to help to understand model uncertainty (rather than scenario or initial condition uncertainty) in predictions of the response to greenhouse-gas-induced warming. RT4 links strongly with RT5 on the evaluation of the ENSEMBLES prediction system and feeds back its results to RT1 to guide improvements in the Earth system models and, through its research on predictability, to steer the development of methods for initialising the ensembles

The extreme flood event of Lago Maggiore in September 1993

In September 1993, the Valais and Ticino regions of Switzerland were affected by extreme flooding triggered by heavy precipitation. The meteorological situation leading to this event is studied in the Twentieth Century Reanalysis (20CR) data set. A strong cut-off low development is found to be the driving synoptic-scale atmospheric circulation pattern. The agreement with previous studies highlights the applicability of 20CR for extreme event analysis.

Determinants of government consumption expenditure in developing countries : a panel data analysis

The paper focuses on the recent pattern of government consumption expenditure in developing countries and estimates the determinants which have influenced government expenditure. Using a panel data set for 111 developing countries from 1984 to 2004, this study finds evidence that political and institutional variables as well as governance variables significantly influence government expenditure. Among other results, the paper finds new evidence of Wagner's law which states that peoples' demand for service and willingness to pay is income-elastic hence the expansion of public economy is influenced by the greater economic affluence of a nation (Cameron1978). Corruption is found to be influential in explaining the public expenditure of developing countries. On the contrary, size of the economy and fractionalization are found to have significant negative association with government expenditure. In addition, the study finds evidence that public expenditure significantly shrinks under military dictatorship compared with other form of governance.