The dynamics of spending and absorption of aid : panel data analysis

In September 1999, the International Monetary Fund (IMF) established the Poverty Reduction and Growth Facility (PRGF) to make the reduction of poverty and the enhancement of economic growth the fundamental objectives of lending operations in its poorest member countries. This paper studies the spending and absorption of aid in PRGF-supported programs, verifies whether the use of aid is programmed to be smoothed over time, and analyzes how considerations about macroeconomic stability influence the programmed use of aid. The paper shows that PRGF-supported programs permit countries to utilize all increases in aid within a few years, showing smoothed use of aid inflows over time. Our results reveal that spending is higher than absorption in both the long-run and short-run use of aid, which is a robust finding of the study. Furthermore, the paper demonstrates that the long-run spending exceeds the injected increase of aid inflows in the economy. In addition, the paper finds that the presence of a PRGF-supported program does not influence the actual absorption or spending of aid.

Approaches and Strategy for Cancer Research and Surveillance Data: Integration, Information Pipeline, Data Models, and Informatics Opportunities

Thesis (Ph.D.)--University of Washington, 2016-04

Selecting optimal instantiations of data models - Theory and validation of an ex ante approach

The schema of an information system can significantly impact the ability of end users to efficiently and effectively retrieve the information they need. Obtaining quickly the appropriate data increases the likelihood that an organization will make good decisions and respond adeptly to challenges. This research presents and validates a methodology for evaluating, ex ante, the relative desirability of alternative instantiations of a model of data. In contrast to prior research, each instantiation is based on a different formal theory. This research theorizes that the instantiation that yields the lowest weighted average query complexity for a representative sample of information requests is the most desirable instantiation for end-user queries. The theory was validated by an experiment that compared end-user performance using an instantiation of a data structure based on the relational model of data with performance using the corresponding instantiation of the data structure based on the object-relational model of data. Complexity was measured using three different Halstead metrics: program length, difficulty, and effort. For a representative sample of queries, the average complexity using each instantiation was calculated. As theorized, end users querying the instantiation with the lower average complexity made fewer semantic errors, i.e., were more effective at composing queries. (c) 2005 Elsevier B.V. All rights reserved.

Analysing time series structure with hidden Markov models

This paper consides the problem of extracting the relationships between two time series in a non-linear non-stationary environment with Hidden Markov Models (HMMs). We describe an algorithm which is capable of identifying associations between variables. The method is applied both to synthetic data and real data. We show that HMMs are capable of modelling the oil drilling process and that they outperform existing methods.

Financial constraints in investment:panel data results from Estonia, 1995–1999

To investigate investment behaviour the present study applies panel data techniques, in particular the Arellano-Bond (1991) GMM estimator, based on data on Estonian manufacturing firms from the period 1995-1999. We employ the model of optimal capital accumulation in the presence of convex adjustment costs. The main research findings are that domestic companies seem to be financially more constrained than those where foreign investors are present, and also, smaller firms are more constrained than their larger counterparts.

Development, validation and uncertainty analysis of quantitative structure and activity relationship models for Log P of disinfection by -products

Hydrophobicity as measured by Log P is an important molecular property related to toxicity and carcinogenicity. With increasing public health concerns for the effects of Disinfection By-Products (DBPs), there are considerable benefits in developing Quantitative Structure and Activity Relationship (QSAR) models capable of accurately predicting Log P. In this research, Log P values of 173 DBP compounds in 6 functional classes were used to develop QSAR models, by applying 3 molecular descriptors, namely, Energy of the Lowest Unoccupied Molecular Orbital (ELUMO), Number of Chlorine (NCl) and Number of Carbon (NC) by Multiple Linear Regression (MLR) analysis. The QSAR models developed were validated based on the Organization for Economic Co-operation and Development (OECD) principles. The model Applicability Domain (AD) and mechanistic interpretation were explored. Considering the very complex nature of DBPs, the established QSAR models performed very well with respect to goodness-of-fit, robustness and predictability. The predicted values of Log P of DBPs by the QSAR models were found to be significant with a correlation coefficient R2 from 81% to 98%. The Leverage Approach by Williams Plot was applied to detect and remove outliers, consequently increasing R 2 by approximately 2% to 13% for different DBP classes. The developed QSAR models were statistically validated for their predictive power by the Leave-One-Out (LOO) and Leave-Many-Out (LMO) cross validation methods. Finally, Monte Carlo simulation was used to assess the variations and inherent uncertainties in the QSAR models of Log P and determine the most influential parameters in connection with Log P prediction. The developed QSAR models in this dissertation will have a broad applicability domain because the research data set covered six out of eight common DBP classes, including halogenated alkane, halogenated alkene, halogenated aromatic, halogenated aldehyde, halogenated ketone, and halogenated carboxylic acid, which have been brought to the attention of regulatory agencies in recent years. Furthermore, the QSAR models are suitable to be used for prediction of similar DBP compounds within the same applicability domain. The selection and integration of various methodologies developed in this research may also benefit future research in similar fields.

Chlorine contribution to quantitative structure and activity relationship models of disinfection by -products' quantum chemical descriptors and toxicities

Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: (1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (E LUMO) via QSAR modelling and analysis; (2) to validate the models by using internal and external cross-validation techniques; (3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl ) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: (1) Linear or Multi-linear Regression (MLR); (2) Partial Least Squares (PLS); and (3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: (1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; (2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; (3) E LUMO are shown to correlate highly with the NCl for several classes of DBPs; and (4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.

The mediating effect of innovation on the relationship between corporate reputation and performance in U.S. firms

In recent years, corporate reputation has gained the attention of many scholars in the strategic management and related fields. There is a general consensus that higher corporate reputation is positively related to firm success or performance. However, the link is not always straightforward; as a result, it calls for researchers to dedicate their efforts to investigate the causes and effects of firm reputation and how it is related to performance. In this doctoral dissertation, innovation is suggested as a mediating variable in this relationship. Innovation is a critical factor for firm success and survival. Highly reputed firms are in a more advantageous position to attract critical resources for innovation such as human and financial capital. These firms face constant pressure from external stakeholders, e.g. the general public, or customers, to achieve and remain at high levels of innovativeness. As a result, firms are in constant search, internally or externally, for new technologies expanding their knowledge base. Consequently, these firms engage in firms acquisitions. In the dissertation, the author assesses the effects of domestic versus international acquisitions as well as related versus unrelated acquisitions on the level of innovativeness and performance. Building upon an established measure of firm-level degree of internationalization (DOI), the dissertation proposes a more detailed and enhanced measure for the firm's DOI. It is modeled as an interaction effect between corporate reputation and resources for innovation. More specifically, firms with higher levels of internationalization will have access to resources for innovation, i.e. human and financial capital, at a global scale. Additionally, the distance between firms and higher education institutions, i.e. universities, is considered as another interaction effect for the human capital attraction. The dissertation is built on two theoretical frameworks, the resource-based view of the firm and institutional theory. It studies 211 U.S. firms using a longitudinal panel data structure from 2006 to 2012. It utilizes a linear dynamic panel data estimation methodology for its hypotheses analyses. Results confirm the hypotheses proposed in the study.

Chlorine Contribution to Quantitative Structure and Activity Relationship Models of Disinfection By-Products' Quantum Chemical Descriptors and Toxicities

Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: 1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (ELUMO) via QSAR modelling and analysis; 2) to validate the models by using internal and external cross-validation techniques; 3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: 1) Linear or Multi-linear Regression (MLR); 2) Partial Least Squares (PLS); and 3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: 1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; 2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; 3) ELUMO are shown to correlate highly with the NCl for several classes of DBPs; and 4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.

Determinants of Capital Structure of Swedish limited companies : Testing Trade-off Theory Against Pecking Order Theory

Research question- This thesis investigates the determinants of capital structure of the Swedish companies. In order to do so, the two dominant theories of the corporate structure are studied and their assumptions are tested. Thus, the study researches which one of the two theories is more appealing for the Swedish market. Methodology-The study follows a purely quantitative study, by conducting an econometric analysis. The data are collected from a secondary source and more particularly the "Retriever" database, which contains financial data of the Swedish companies. Findings- The findings indicate that the determinants of the corporate structure for the Swedish market do not differ from other studies which have been conducted in other countries. However, there is a difference when it comes to tax and non-tax shields. The results suggest that in most cases the Pecking Order Theory appears to be more representative for the Swedish market, since most of the coefficient appear to be in favour of it. Moreover, the significance of the effect of the industry for the financial leverage is confirmed.

On the Capital Structure of Real Estate Firms

The leverage and debt maturity choices of real estate companies are interdependent, and are not made separately as is often assumed in the literature. We use three-stage least squares (3SLS) regression analysis to explore this interdependence for a sample of listed U.S. real estate companies and Real Estate Investment Trusts (REITs) traded between 1973 and 2006.We find substantial differences in the nature of the relationship between leverage and maturity for the two firm types. Leverage is a determinant of maturity for non-REITs, whereas maturity is a determinant of leverage for REITs. We also find that the drivers of capital structure choices in real estate companies and REITs clearly reflect the effects of the REIT regulation.

Long-term relationship between energy consumption and GDP in Latin America: an empirical assessment using panel data

The main objetive of this research is to evaluate the long term relationship between energy consumption and GDP for some Latin American countries in the period 1980-2009 -- The estimation has been done through the non-stationary panel approach, using the production function in order to control other sources of GDP variation, such as capital and labor -- In addition to this, a panel unit root tests are used in order to identify the non-stationarity of these variables, followed by the application of panel cointegration test proposed by Pedroni (2004) to avoid a spurious regression (Entorf, 1997; Kao, 1999)

Capital structure, risk and asymmetric information : theory and evidence

This paper proposes a principal-agent model between banks and firms with risk and asymmetric information. A mixed form of finance to firms is assumed. The capital structure of firms is a relevant cause for the final aggregate level of investment in the economy. In the model analyzed, there may be a separating equilibrium, which is not economically efficient, because aggregate investments fall short of the first-best level. Based on European firm-level data, an empirical model is presented which validates the result of the relevance of the capital structure of firms. The relative magnitude of equity in the capital structure makes a real difference to the profits obtained by firms in the economy.

Determinants of listed property trust bond ratings : Australian evidence

Using artificial neural networks (ANN) and ordinal regression (OR) as alternative methods to predict LPT bond ratings, we examine the role that various financial and industry variables have on Listed Property Trust (LPT) bond ratings issued by Standard and Poor’s from 1999-2006. Our study shows that both OR and ANN provide robust alternatives to rating LPT bonds and that there are no significant differences in results between the two full models. OR results show that of the financial variables used in our models, debt coverage and financial leverage ratios have the most profound effect on LPT bond ratings. Further, ANN results show that 73.0% of LPT bond rating is attributable to financial variables and 23.0% to industry-based variables with office LPT sector accounting for 2.6%, retail LPT 10.9% and stapled management structure 13.5%.

Using information criteria to select the correct variance–covariance structure for longitudinal data in ecology

1. Ecological data sets often use clustered measurements or use repeated sampling in a longitudinal design. Choosing the correct covariance structure is an important step in the analysis of such data, as the covariance describes the degree of similarity among the repeated observations. 2. Three methods for choosing the covariance are: the Akaike information criterion (AIC), the quasi-information criterion (QIC), and the deviance information criterion (DIC). We compared the methods using a simulation study and using a data set that explored effects of forest fragmentation on avian species richness over 15 years. 3. The overall success was 80.6% for the AIC, 29.4% for the QIC and 81.6% for the DIC. For the forest fragmentation study the AIC and DIC selected the unstructured covariance, whereas the QIC selected the simpler autoregressive covariance. Graphical diagnostics suggested that the unstructured covariance was probably correct. 4. We recommend using DIC for selecting the correct covariance structure.