Enterprise system investments for competitive advantage: An empirical study of Swiss SMEs

Previous research identifies various reasons companies invest in information technology (IT), often as a means to generate value. To add to the discussion of IT value generation, this study investigates investments in enterprise software systems that support business processes. Managers of more than 500 Swiss small and medium-sized enterprises (SMEs) responded to a survey regarding the levels of their IT investment in enterprise software systems and the perceived utility of those investments. The authors use logistic and ordinary least squares regression to examine whether IT investments in two business processes affect SMEs' performance and competitive advantage. Using cluster analysis, they also develop a firm typology with four distinct groups that differ in their investments in enterprise software systems. These findings offer key implications for both research and managerial practice.

Forecasting day-ahead electricity load using a multiple equation time series approach

The quality of short-term electricity load forecasting is crucial to the operation and trading activities of market participants in an electricity market. In this paper, it is shown that a multiple equation time-series model, which is estimated by repeated application of ordinary least squares, has the potential to match or even outperform more complex nonlinear and nonparametric forecasting models. The key ingredient of the success of this simple model is the effective use of lagged information by allowing for interaction between seasonal patterns and intra-day dependencies. Although the model is built using data for the Queensland region of Australia, the method is completely generic and applicable to any load forecasting problem. The model’s forecasting ability is assessed by means of the mean absolute percentage error (MAPE). For day-ahead forecast, the MAPE returned by the model over a period of 11 years is an impressive 1.36%. The forecast accuracy of the model is compared with a number of benchmarks including three popular alternatives and one industrial standard reported by the Australia Energy Market Operator (AEMO). The performance of the model developed in this paper is superior to all benchmarks and outperforms the AEMO forecasts by about a third in terms of the MAPE criterion.

Sektore automobilistikoaren denborazko prezio-analisia

Lan honek 2007 eta 2013 urteen bitartean autoen prezioak nola aldatzen diren erakusten digu. Horretarako, autoen hainbat ezaugarri hartu dira kontuan, hala nola, modeloa, mota, zilindrada, zilindroen posizioa, potentzia, luzera, zabalera, altuera, kontsumoa, abiadura maximoa, azelerazioa eta maleteroaren kapazitatea. Gaur egungo egoera dela eta, hasiera batean pentsa genezake kotxeen prezioak jaitsi egin direla, baina dituzten aurrerapenak kontuan izanda, logikoena izango zen zenbat eta aldagai garatuagoak izan orduan eta garestiagoak izatea. Beraz, lanaren helburu izango da, emaitza horiek bete diren edo ez frogatzea, eredu ekonometriko bat zehaztuz. Ereduaren estimazioa egiteko erabilitako metodologia Karratu Txikien Arruntak izan dira, baina heterozedastizitatearen arazoa agertu denez, eredu honek ez du bariantza txikiena izango eta beraz estimatzaile berria erabili beharko da, kasu honetan, Karratu Txikienen Zabalduen metodoa erabili da. Urte bakoitzerako egokiagoa den eredua hautatu ondoren eredu orokorra egin dut, horretarako 2007 eta 2013ko datuak begiraturik, bakoitzari dagokion aldagai azaltzaile bat eratu diot, hau da, ezaugarri bakoitza bere urtearekin elkartu dut. Murriztutako eredura heltzerakoan, lortutako emaitzak ikusirik ondorio batera heldu naiz, ondorio hori hasieran planteatutako hipotesiarekin bat datorrela ikusi da, hau da, autoen prezioak proportzionalki jaitsi egin dira, eta ondorioz, autoen ezaugarriak gutxiago baloratzen dira.

Global sea-to-air flux climatology for bromoform, dibromomethane and methyl iodide

Volatile halogenated organic compounds containing bromine and iodine, which are naturally produced in the ocean, are involved in ozone depletion in both the troposphere and stratosphere. Three prominent compounds transporting large amounts of marine halogens into the atmosphere are bromoform (CHBr3), dibromomethane (CH2Br2) and methyl iodide (CH3I). The input of marine halogens to the stratosphere has been estimated from observations and modelling studies using low-resolution oceanic emission scenarios derived from top-down approaches. In order to improve emission inventory estimates, we calculate data-based high resolution global sea-to-air flux estimates of these compounds from surface observations within the HalOcAt (Halocarbons in the Ocean and Atmosphere) database (https://halocat.geomar.de/). Global maps of marine and atmospheric surface concentrations are derived from the data which are divided into coastal, shelf and open ocean regions. Considering physical and biogeochemical characteristics of ocean and atmosphere, the open ocean water and atmosphere data are classified into 21 regions. The available data are interpolated onto a 1 degrees x 1 degrees grid while missing grid values are interpolated with latitudinal and longitudinal dependent regression techniques reflecting the compounds' distributions. With the generated surface concentration climatologies for the ocean and atmosphere, global sea-to-air concentration gradients and sea-to-air fluxes are calculated. Based on these calculations we estimate a total global flux of 1.5/2.5 Gmol Br yr(-1) for CHBr3, 0.78/0.98 Gmol Br yr(-1) for CH2Br2 and 1.24/1.45 Gmol Br yr(-1) for CH3I (robust fit/ordinary least squares regression techniques). Contrary to recent studies, negative fluxes occur in each sea-to-air flux climatology, mainly in the Arctic and Antarctic regions. "Hot spots" for global polybromomethane emissions are located in the equatorial region, whereas methyl iodide emissions are enhanced in the subtropical gyre regions. Inter-annual and seasonal variation is contained within our flux calculations for all three compounds. Compared to earlier studies, our global fluxes are at the lower end of estimates, especially for bromoform. An under-representation of coastal emissions and of extreme events in our estimate might explain the mismatch between our bottom-up emission estimate and top-down approaches.

Regression analysis of spatial data

Many of the most interesting questions ecologists ask lead to analyses of spatial data. Yet, perhaps confused by the large number of statistical models and fitting methods available, many ecologists seem to believe this is best left to specialists. Here, we describe the issues that need consideration when analysing spatial data and illustrate these using simulation studies. Our comparative analysis involves using methods including generalized least squares, spatial filters, wavelet revised models, conditional autoregressive models and generalized additive mixed models to estimate regression coefficients from synthetic but realistic data sets, including some which violate standard regression assumptions. We assess the performance of each method using two measures and using statistical error rates for model selection. Methods that performed well included generalized least squares family of models and a Bayesian implementation of the conditional auto-regressive model. Ordinary least squares also performed adequately in the absence of model selection, but had poorly controlled Type I error rates and so did not show the improvements in performance under model selection when using the above methods. Removing large-scale spatial trends in the response led to poor performance. These are empirical results; hence extrapolation of these findings to other situations should be performed cautiously. Nevertheless, our simulation-based approach provides much stronger evidence for comparative analysis than assessments based on single or small numbers of data sets, and should be considered a necessary foundation for statements of this type in future.

Contributions to the theory of maximum entropy estimation for ill-posed models

As técnicas estatísticas são fundamentais em ciência e a análise de regressão linear é, quiçá, uma das metodologias mais usadas. É bem conhecido da literatura que, sob determinadas condições, a regressão linear é uma ferramenta estatística poderosíssima. Infelizmente, na prática, algumas dessas condições raramente são satisfeitas e os modelos de regressão tornam-se mal-postos, inviabilizando, assim, a aplicação dos tradicionais métodos de estimação. Este trabalho apresenta algumas contribuições para a teoria de máxima entropia na estimação de modelos mal-postos, em particular na estimação de modelos de regressão linear com pequenas amostras, afetados por colinearidade e outliers. A investigação é desenvolvida em três vertentes, nomeadamente na estimação de eficiência técnica com fronteiras de produção condicionadas a estados contingentes, na estimação do parâmetro ridge em regressão ridge e, por último, em novos desenvolvimentos na estimação com máxima entropia. Na estimação de eficiência técnica com fronteiras de produção condicionadas a estados contingentes, o trabalho desenvolvido evidencia um melhor desempenho dos estimadores de máxima entropia em relação ao estimador de máxima verosimilhança. Este bom desempenho é notório em modelos com poucas observações por estado e em modelos com um grande número de estados, os quais são comummente afetados por colinearidade. Espera-se que a utilização de estimadores de máxima entropia contribua para o tão desejado aumento de trabalho empírico com estas fronteiras de produção. Em regressão ridge o maior desafio é a estimação do parâmetro ridge. Embora existam inúmeros procedimentos disponíveis na literatura, a verdade é que não existe nenhum que supere todos os outros. Neste trabalho é proposto um novo estimador do parâmetro ridge, que combina a análise do traço ridge e a estimação com máxima entropia. Os resultados obtidos nos estudos de simulação sugerem que este novo estimador é um dos melhores procedimentos existentes na literatura para a estimação do parâmetro ridge. O estimador de máxima entropia de Leuven é baseado no método dos mínimos quadrados, na entropia de Shannon e em conceitos da eletrodinâmica quântica. Este estimador suplanta a principal crítica apontada ao estimador de máxima entropia generalizada, uma vez que prescinde dos suportes para os parâmetros e erros do modelo de regressão. Neste trabalho são apresentadas novas contribuições para a teoria de máxima entropia na estimação de modelos mal-postos, tendo por base o estimador de máxima entropia de Leuven, a teoria da informação e a regressão robusta. Os estimadores desenvolvidos revelam um bom desempenho em modelos de regressão linear com pequenas amostras, afetados por colinearidade e outliers. Por último, são apresentados alguns códigos computacionais para estimação com máxima entropia, contribuindo, deste modo, para um aumento dos escassos recursos computacionais atualmente disponíveis.

Portuguese Citizens’ Support for Democracy: 40 Years after the Carnation Revolution

Forty years after the Carnation Revolution, the relatively young Portuguese democracy is experiencing dramatically low levels of public specific support for democracy. This article tests the leverage of demand-side and supply-side accounts to explain differentials in public satisfaction with democracy. Through ordinary least squares regression analyses that draw on the unique data of the ‘Barometer 40 Years of Democracy in Portugal (2014)’, this articles shows that age cohort, identification with extreme parties, evaluation of the country’s political past, and economic performance are strong correlates of citizens’ specific support for democracy

Extremum estimators and stochastic optimization methods

Submitted in partial fulfillment for the Requirements for the Degree of PhD in Mathematics, in the Speciality of Statistics in the Faculdade de Ciências e Tecnologia

O papel das agências de rating e a dívida pública portuguesa

Durante as últimas décadas observou-se o crescimento da importância das avaliações fornecidas pelas agências de rating, sendo este um fator decisivo na tomada de decisão dos investidores. Também os emitentes de dívida são largamente afetados pelas alterações das classificações atribuídas por estas agências. Esta investigação pretende, por um lado, compreender se estas agências têm poder para conseguirem influenciar a evolução da dívida pública e qual o seu papel no mercado financeiro. Por outro, pretende compreender quais os fatores determinantes da dívida pública portuguesa, bem como a realização de uma análise por percentis com o objetivo de lhe atribuir um rating. Para a análise dos fatores que poderão influenciar a dívida pública, a metodologia utilizada é uma regressão linear múltipla estimada através do Método dos Mínimos Quadrados (Ordinary Least Squares – OLS), em que num cenário inicial era composta por onze variáveis independentes, sendo a dívida pública a variável dependente, para um período compreendido entre 1996 e 2013. Foram realizados vários testes ao modelo inicial, com o objetivo de encontrar um modelo que fosse o mais explicativo possível. Conseguimos ainda identificar uma relação inversa entre o rating atribuído por estas agências e a evolução da dívida pública, no sentido em que para períodos em que o rating desce, o crescimento da dívida é mais acentuado. Não nos foi, no entanto, possível atribuir um rating à dívida pública através de uma análise de percentis.

Linking of stress offset score (SOS), work satisfaction, and organizational commitment to intentions to quit

The purpose of this study was to identify the impact of stressors and offsetting satistiers, measured in this study with Stress Offset Score (SOS), on intentions to quit and examine the mediating and moderating effects of three facets of work satisfaction (job satisfaction, pay satisfaction, and satisfaction with supervisor) and two facets of organizational commitment (affective and nonnative commitment) on this relationship. The sample was composed of 2990 employees from 21 public and private organizations. The interaction of each type of work satisfaction and organizational commitment, with SOS, was tested using Ordinary Least Squares (OLS) procedures. Intentions to quit was the dependent variable. The research questions were determine: (1) Does SOS predict intentions to quit? (2) Does work satisfaction mediate the predictive relationship of SOS on intentions to quit? (3) Does organizational commitment mediate the predictive relationship of SOS on intent to quit? (4) Does work satisfaction moderate the predictive relationship of SOS on intentions to quit? and (5) Does organizational commitment moderate the predictive relationship of SOS on intentions to quit? The results indicated that SOS was negatively correlated with intentions to quit. Each of the types of work satisfaction and organizational commitment variables showed a partial mediated relationship with SOS and each relationship was highly significant, while normative commitment explained more of the relationship then other mediators. The study also tested for interactions but no statistical significant relationships where established between any of the interaction terms (e.g., SOSxJob Satisfaction and SOSxAffcctive Commitment) and intentions to quit.

Bootstrap for panel data models with an application to the evaluation of public policies

Le but de cette thèse est d étendre la théorie du bootstrap aux modèles de données de panel. Les données de panel s obtiennent en observant plusieurs unités statistiques sur plusieurs périodes de temps. Leur double dimension individuelle et temporelle permet de contrôler l 'hétérogénéité non observable entre individus et entre les périodes de temps et donc de faire des études plus riches que les séries chronologiques ou les données en coupe instantanée. L 'avantage du bootstrap est de permettre d obtenir une inférence plus précise que celle avec la théorie asymptotique classique ou une inférence impossible en cas de paramètre de nuisance. La méthode consiste à tirer des échantillons aléatoires qui ressemblent le plus possible à l échantillon d analyse. L 'objet statitstique d intérêt est estimé sur chacun de ses échantillons aléatoires et on utilise l ensemble des valeurs estimées pour faire de l inférence. Il existe dans la littérature certaines application du bootstrap aux données de panels sans justi cation théorique rigoureuse ou sous de fortes hypothèses. Cette thèse propose une méthode de bootstrap plus appropriée aux données de panels. Les trois chapitres analysent sa validité et son application. Le premier chapitre postule un modèle simple avec un seul paramètre et s 'attaque aux propriétés théoriques de l estimateur de la moyenne. Nous montrons que le double rééchantillonnage que nous proposons et qui tient compte à la fois de la dimension individuelle et la dimension temporelle est valide avec ces modèles. Le rééchantillonnage seulement dans la dimension individuelle n est pas valide en présence d hétérogénéité temporelle. Le ré-échantillonnage dans la dimension temporelle n est pas valide en présence d'hétérogénéité individuelle. Le deuxième chapitre étend le précédent au modèle panel de régression. linéaire. Trois types de régresseurs sont considérés : les caractéristiques individuelles, les caractéristiques temporelles et les régresseurs qui évoluent dans le temps et par individu. En utilisant un modèle à erreurs composées doubles, l'estimateur des moindres carrés ordinaires et la méthode de bootstrap des résidus, on montre que le rééchantillonnage dans la seule dimension individuelle est valide pour l'inférence sur les coe¢ cients associés aux régresseurs qui changent uniquement par individu. Le rééchantillonnage dans la dimen- sion temporelle est valide seulement pour le sous vecteur des paramètres associés aux régresseurs qui évoluent uniquement dans le temps. Le double rééchantillonnage est quand à lui est valide pour faire de l inférence pour tout le vecteur des paramètres. Le troisième chapitre re-examine l exercice de l estimateur de différence en di¤érence de Bertrand, Duflo et Mullainathan (2004). Cet estimateur est couramment utilisé dans la littérature pour évaluer l impact de certaines poli- tiques publiques. L exercice empirique utilise des données de panel provenant du Current Population Survey sur le salaire des femmes dans les 50 états des Etats-Unis d Amérique de 1979 à 1999. Des variables de pseudo-interventions publiques au niveau des états sont générées et on s attend à ce que les tests arrivent à la conclusion qu il n y a pas d e¤et de ces politiques placebos sur le salaire des femmes. Bertrand, Du o et Mullainathan (2004) montre que la non-prise en compte de l hétérogénéité et de la dépendance temporelle entraîne d importantes distorsions de niveau de test lorsqu'on évalue l'impact de politiques publiques en utilisant des données de panel. Une des solutions préconisées est d utiliser la méthode de bootstrap. La méthode de double ré-échantillonnage développée dans cette thèse permet de corriger le problème de niveau de test et donc d'évaluer correctement l'impact des politiques publiques.

Symptoms and causes of poverty in a rural Vietnamese commune: Does ethnicity matter?

This study uses data from a sample survey of 200 households drawn from a mountainous commune in Vietnam’s North Central Coast region to measure and explain relative poverty. Principal components analysis is used to construct a multidimensional index of poverty outcomes from variables measuring household income and the value of domestic assets. This index of poverty is then regressed on likely causes of poverty including different forms of resource endowment and social exclusion defined by gender and ethnicity. The ordinary least squares estimates indicate that poverty is indeed influenced by ethnicity, partly through its interaction with social capital. However, poverty is most strongly affected by differences in human and social capital. Differences in the amount of livestock and high quality farmland owned also matter. Thai households are poorer than their Kinh counterparts even when endowed with the same levels of human, social, physical and natural capital considered in the study. This empirical result provides a rationale for further research on the causal relationship between ethnicity and poverty outcomes.

A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks

We propose a nonparametric method for estimating derivative financial asset pricing formulae using learning networks. To demonstrate feasibility, we first simulate Black-Scholes option prices and show that learning networks can recover the Black-Scholes formula from a two-year training set of daily options prices, and that the resulting network formula can be used successfully to both price and delta-hedge options out-of-sample. For comparison, we estimate models using four popular methods: ordinary least squares, radial basis functions, multilayer perceptrons, and projection pursuit. To illustrate practical relevance, we also apply our approach to S&P 500 futures options data from 1987 to 1991.

Correcting Measurement Error Bias in Interaction Models with Small Samples

Several methods have been suggested to estimate non-linear models with interaction terms in the presence of measurement error. Structural equation models eliminate measurement error bias, but require large samples. Ordinary least squares regression on summated scales, regression on factor scores and partial least squares are appropriate for small samples but do not correct measurement error bias. Two stage least squares regression does correct measurement error bias but the results strongly depend on the instrumental variable choice. This article discusses the old disattenuated regression method as an alternative for correcting measurement error in small samples. The method is extended to the case of interaction terms and is illustrated on a model that examines the interaction effect of innovation and style of use of budgets on business performance. Alternative reliability estimates that can be used to disattenuate the estimates are discussed. A comparison is made with the alternative methods. Methods that do not correct for measurement error bias perform very similarly and considerably worse than disattenuated regression

Determinants of demand for antenatal care in Colombia

Even though antenatal care is universally regarded as important, determinants of demand for antenatal care have not been widely studied. Evidence concerning which and how socioeconomic conditions influence whether a pregnant woman attends or not at least one antenatal consultation or how these factors affect the absences to antenatal consultations is very limited. In order to generate this evidence, a two-stage analysis was performed with data from the Demographic and Health Survey carried out by Profamilia in Colombia during 2005. The first stage was run as a logit model showing the marginal effects on the probability of attending the first visit and an ordinary least squares model was performed for the second stage. It was found that mothers living in the pacific region as well as young mothers seem to have a lower probability of attending the first visit but these factors are not related to the number of absences to antenatal consultation once the first visit has been achieved. The effect of health insurance was surprising because of the differing effects that the health insurers showed. Some familiar and personal conditions such as willingness to have the last children and number of previous children, demonstrated to be important in the determination of demand. The effect of mother’s educational attainment was proved as important whereas the father’s educational achievement was not. This paper provides some elements for policy making in order to increase the demand inducement of antenatal care, as well as stimulating research on demand for specific issues on health.