Studies on earnings management usually hypothesise that managers manage accruals opportunistically. Few studies however, argue that managers can also use accruals to improve the value relevance of reported earnings to help investors better assess the firm's operating performance. While substantial evidence on managers' opportunistic behaviour on accruals has been documented in the literature, empirical evidence on the informativeness of accruals is scarce and inconclusive. The purpose of this thesis is to examine whether managers use accruals to communicate private information regarding the firm's operating performance, or as reported in the literature, use them for their own benefit. This thesis finds that on average, firms reporting high earnings accompanied by high accruals have significantly negative subsequent period stock returns suggesting that these firms manage their accounting earnings. Focusing on stock dividend issues as an incentive to opportunistically increase accruals, the results are found to be consistent with the earnings management hypothesis. Stock dividend issuing firms are reported to significantly increase accruals in the issue year followed by poor earnings and stock price performances in the subsequent year. Moreover, discretionary accruals of the issuing firms are negatively correlated with both future earnings and abnormal stock returns. This evidence attempts to complement the earnings management literature. The analysis on the incentive to decrease accruals related to share repurchases, however, does not provide sufficient evidence to suggest that managers use their discretion to decrease accruals. To investigate the hypothesis that managers use accruals to convey information regarding their firm's future profitability, this thesis employs the contemporaneous earnings and dividend announcements as the research setting. This choice was made to increase the likelihood of detecting the use of accruals as private information communication while simultaneously mitigating the likelihood of the opportunistic income smoothing hypothesis to explain the results. The evidence strongly indicates that managers use both accruals and dividend increases as their private information communication regarding their firm's future profitability. Dividend increasing firms report positive accruals which are positively correlated with future profitability. This finding contributes to the literature by providing evidence on the accrual signalling hypothesis. Overall, the results of this thesis suggest that, depending on the incentives, managers can use the discretion accorded under the Generally Accepted Accounting Principles (GAAP) in estimating accounting accrual, either to manage accruals opportunistically or to help investors better assess the firms' operating performance.

Stroke is the leading cause of disability worldwide. It often leads to mobility limitations resulting from deficits in muscle performance. While reduced muscle strength and rate of force production have been reported, little is known about the power generating capability of people after stroke and its relationship to mobility. Research in other populations has found that measures of muscle power may have a greater association with activity performance than do measures of muscle force alone. Consequently, in an attempt to optimise power, investigators have focused on identifying ideal parameters within which to train for power. One such parameter is the identification of the loading level at which maximal power is generated. Literature reporting optimal loads from both young athletic and healthy older populations has yielded mixed results, making the applicability to a hemiparetic population difficult. The purpose of this study was to investigate muscle power performance at differing loads and to determine at what load muscle power is best elicited in hemiparetic and age and gender matched control groups. A secondary aim was to ascertain whether there is a relationship between the muscle power values obtained and activities such as gait, stair climbing and standing from a chair. Twenty nine hemiparetic volunteers and twenty nine age and gender matched controls were evaluated. Involved and uninvolved legs of the stroke group and a comparison leg of the control group underwent testing. Leg press muscle power was measured using a modified supine leg press machine at 30%, 50% and 70% of a one-repetition maximum (1-RM) load. Participants were positioned on the leg press machine and asked to push, with a single leg, as hard and as fast as they could. Data was collected via a mounted force platform and a linear transducer connected to a platform on which the participants lay. From these, power was able to be calculated. The activities were timed while being performed as fast as possible. The results showed that peak muscle power values differed significantly between the involved, uninvolved and control legs. Peak leg power in all three leg groups was greatest when pushing against a load of 30% of 1-RM. Involved leg peak power tested at 30% of 1-RM (Mean:240; SD:145 W) was significantly lower (p<0.05) than the uninvolved leg (Mean:506; SD:243 W). Both the involved and uninvolved legs generated significantly lower peak power (p<0.05) than the control leg (Mean:757; SD:292 W). Correlations were found between the involved leg peak power and gait speed and involved leg peak power and stair climbing (r=0.6-0.7, p<0.05). No correlation was found between paretic leg peak power and chair stands. The control group leg peak power demonstrated significant associations with the performance of all three activities.In summary, there were significant differences between the involved and the uninvolved leg in power production after stroke. As well, there are significant differences between the uninvolved leg and the leg of those not affected by stroke. Power was related to a number of activities.

China's rapid economic growth and the development of its domestic stock market have attracted considerable attention from foreign investors. China's economic financial expansion, however, has emerged from an environment of state planning and radical socialist ideology. With a view of providing investors with a better understanding of the risk and return relationship in the Chinese A-share market over the past decade, this thesis adapts several empirical models to the circumstances in China and conducts four empirical analyses. First, in order to rationalize foreign investors' entry into the A-share market, the thesis compares the diversification benefits in three China-related stock markets, namely the A-share, the B-share and the H-share markets in a mean-variance framework using daily, weekly and monthly data respectively. The results suggest that of the three stock markets, the B-share market generates the highest average annual returns while the A-share market has the most significant diversification benefits regardless of whether the analysis is undertaken implementing a traditional mean-variance framework or a downside risk framework. Next, an empirical analysis using the Fama and MacBeth two-pass procedure is undertaken to test the relationship between beta, firm factors and stock returns. Similar to the findings in other stock markets, the results of this analysis show that the static betas for individual stocks fail to capture variation in stock returns in the A-share market. In contrast, the effects of book-to-market and trading volume are significant in the sample period. However, the fact that none of these factors have a persistent role in explaining stock returns suggests a possible change in the investment philosophy of Chinese domestic investors over the past decade. In the third analysis, two global betas are incorporated into the cross-sectional regressions in a bid to examine the integration or segmentation of the A-share market with the world and Hong Kong stock markets. Specifically, both time-varying betas and static betas are used in the analysis. The results suggest that there is no beta effect and the A-share marke t is totally segmented from both the world and Hong Kong stock markets. Finally, when the segmentation and integration status of the A-share market is further examined using the Maximum Likelihood Estimation framework without beta estimation and the assumption of a linear relationship between beta and stock returns, the findings suggest that the A-share market is becoming increasing integrated with the B-share and the Hong Kong stock markets.

China's rapid economic growth and the development of its domestic stock market have attracted considerable attention from foreign investors. China's economic financial expansion, however, has emerged from an environment of state planning and radical socialist ideology. With a view of providing investors with a better understanding of the risk and return relationship in the Chinese A-share market over the past decade, this thesis adapts several empirical models to the circumstances in China and conducts four empirical analyses. First, in order to rationalize foreign investors' entry into the A-share market, the thesis compares the diversification benefits in three China-related stock markets, namely the A-share, the B-share and the H-share markets in a mean-variance framework using daily, weekly and monthly data respectively. The results suggest that of the three stock markets, the B-share market generates the highest average annual returns while the A-share market has the most significant diversification benefits regardless of whether the analysis is undertaken implementing a traditional mean-variance framework or a downside risk framework. Next, an empirical analysis using the Fama and MacBeth two-pass procedure is undertaken to test the relationship between beta, firm factors and stock returns. Similar to the findings in other stock markets, the results of this analysis show that the static betas for individual stocks fail to capture variation in stock returns in the A-share market. In contrast, the effects of book-to-market and trading volume are significant in the sample period. However, the fact that none of these factors have a persistent role in explaining stock returns suggests a possible change in the investment philosophy of Chinese domestic investors over the past decade. In the third analysis, two global betas are incorporated into the cross-sectional regressions in a bid to examine the integration or segmentation of the A-share market with the world and Hong Kong stock markets. Specifically, both time-varying betas and static betas are used in the analysis. The results suggest that there is no beta effect and the A-share marke t is totally segmented from both the world and Hong Kong stock markets. Finally, when the segmentation and integration status of the A-share market is further examined using the Maximum Likelihood Estimation framework without beta estimation and the assumption of a linear relationship between beta and stock returns, the findings suggest that the A-share market is becoming increasing integrated with the B-share and the Hong Kong stock markets.

In recent years, increasing attention has turned toward the effect of health information or health knowledge on nutrient intake. In determining the effect of health information on nutrient demand, researchers face the estimation problem of dealing with the endogeneity of health information knowledge. The standard approach for dealing with this problem is an instrumental variables (IV) procedure. Unfortunately, recent research has demonstrated that the IV procedure may not be reliable in the types of data sets that contain health information and nutrient intakes because the instruments are not sufficiently correlated with the endogenous variables (i.e., instruments are weak). This thesis compares the reliability of the IV procedure (and the Hausman test) with a relatively new procedure, directed graphs, given weak instruments. The goal is to determine if the method of directed graphs performs better in identifying an endogenous variable and also relevant instruments. The performance of the Hausman test and directed graphs are first assessed through conducting a Monte-Carlo sampling experiment containing weak instruments. Because the structure of the model is known in the Monte-Carlo experiment, these results are used as a guideline to determine which procedure would be more reliable in a real world setting. The procedures are then applied to a real-world cross-sectional dataset on nutrient intake. This thesis provides empirical evidence that neither the IV estimator (and Hausman test) or the directed graphs are reliable when instruments are weak, as in a cross-sectional dataset.

The world is experiencing yet another energy- and fuel predicament as oil prices are escalating to new hights. Alternative fuels are being promoted globally as the increasing gasoline prices trigger inflation. Basic food commodities are some of the goods hit by this inflation and the purpose of this thesis is to analyse whether the higher maize and sugar prices are having any effect on the expanding ethanol production. This thesis focuses on the two major crop inputs in ethanol production: maize (in the US) and sugar cane (in Brazil). Econometric tests using cross-sectional data were carried through to find the elasticities of the variables. The crops prices were tested against ethanol output using the log-linear model in several regressions to find a relationship. In addition, the output levels of the crops were tested using the same method. It was found that maize prices and output affects ethanol production. Sugar cane prices do not have any significant impact on ethanol production while sugar cane output has a small, yet significant relationhip with ethanol. Consequently, ethanol’s rise in the fuel market could be a result of increased maize input, rather than sugar. / Dagens värld upplever ännu ett energi- och bränsle predikament när oljepriser eskalerar mot nya höjder. Alternativa bränslen marknadsförs globalt samtidigt som de stigande bensinpriserna stimulerar inflationen. Några av de varor som drabbas av denna inflation är grundläggande livsmedelsprodukter och syftet med denna uppsats är att analysera huruvida de högre priserna på majs och socker påverkar den expanderande etanolproduktionen. Uppsatsen fokuserar på de två stora grödor som används som insatsvaror vid framställningen av etanol: majs (i USA) och sockerrör (i Brasilien). Ekonometriska tester genomfördes för att erhålla variablernas elasticiteter med hjälp av den cross-sectional data som behandlades. Genom log-linear modellen utfördes det ett antal regressioner för att hitta ett samband mellan grödornas priser och etanolproduktionen. Därutöver genomfördes tester för att hitta sambandet mellan grödornas utbud och etanol med hjälp av samma modell. Det upptäcktes att både pris och utbudet av majs påverkar etanolproduktionen. Sockerrörspriser har ingen signifikant inverkan på etanolproduktionen medan utbudet av sockerrör har en signifikant, om än svag, relation till etanol. Följaktligen kan etanols tillväxt i  bränslemarknaden tolkas som ett resultat av en stigande majsinsats snarare än sockerinstats vid etanolframställningen.

Cross-sectional surveys are often used in epidemiological studies to identify subjects with a disease. When estimating the survival function from onset of disease, this sampling mechanism introduces bias, which must be accounted for. If the onset times of the disease are assumed to be coming from a stationary Poisson process, this bias, which is caused by the sampling of prevalent rather than incident cases, is termed length-bias. A one-sample Kolomogorov-Smirnov type of goodness-of-fit test for right-censored length-biased data is proposed and investigated with Weibull, log-normal and log-logistic models. Algorithms detailing how to efficiently generate right-censored length-biased survival data of these parametric forms are given. Simulation is employed to assess the effects of sample size and censoring on the power of the test. Finally, the test is used to evaluate the goodness-of-fit using length-biased survival data of patients with dementia from the Canadian Study of Health and Aging.

Introduction. Wrist (Colles') and forearm fractures commonly occur when a person falls on the outstretched forearm and the force exceeds bone strength. There is lack of experimental evidence testing the available force prediction models and assessing factors that determine forearm forces during a fall.<p> Objective. The primary objective was to compare experimentally measured force peaks (F1max-E and F2max-E) to the force peaks that were predicted by an engineering based force prediction model (F1max-M and F2max-M), at heights greater than 5cm. The second objective was to describe the relationships between the experimentally measured peak forces and forearm bone and muscle strength properties, body mass, and stature as a function of fall height.<p> Methods. Using 3D motion tracking, we assessed the first (F1max) and second (F2max) peak forces from 10 young adults (5 male; 5 female) who volunteered to fall from heights up to 25cm onto a foam covered force plate. Peripheral QCT was used to determine the bone strength index (BSIc), strength-strain index (SSIp), and muscle cross sectional area (MCSA) of each participant. Two 2x8 between-within factorial ANOVAs determined the difference between the experimental and model force peaks, with post hoc analyses at all fall heights. Pearson's correlation was used to determine the relationship between the pQCT-derived bone and muscle strength indices and the force peaks.<p> Results. There was no significant differences between F1max-E and F1max-M across all fall heights, but the model significantly over-predicted the F2max-E across all fall heights. After controlling F1max-E and F2max-E for body mass, the force peaks appeared to be weakly related to the anthropometric as well as bone and muscle strength outcomes (r=0.2-0.7, p>0.05). The relationship between bone and muscle strength outcomes appeared to have a tendency to get stronger at higher fall heights.<p> Conclusion. The model predicted experimental F1max, but not experimental F2max. This study presents preliminary pilot results. Larger sample size is needed to confirm whether incorporating bone and muscle strength estimates into fall force prediction models could enhance forearm fracture risk assessments.

Introduction. Wrist (Colles') and forearm fractures commonly occur when a person falls on the outstretched forearm and the force exceeds bone strength. There is lack of experimental evidence testing the available force prediction models and assessing factors that determine forearm forces during a fall.<p> Objective. The primary objective was to compare experimentally measured force peaks (F1max-E and F2max-E) to the force peaks that were predicted by an engineering based force prediction model (F1max-M and F2max-M), at heights greater than 5cm. The second objective was to describe the relationships between the experimentally measured peak forces and forearm bone and muscle strength properties, body mass, and stature as a function of fall height.<p> Methods. Using 3D motion tracking, we assessed the first (F1max) and second (F2max) peak forces from 10 young adults (5 male; 5 female) who volunteered to fall from heights up to 25cm onto a foam covered force plate. Peripheral QCT was used to determine the bone strength index (BSIc), strength-strain index (SSIp), and muscle cross sectional area (MCSA) of each participant. Two 2x8 between-within factorial ANOVAs determined the difference between the experimental and model force peaks, with post hoc analyses at all fall heights. Pearson's correlation was used to determine the relationship between the pQCT-derived bone and muscle strength indices and the force peaks.<p> Results. There was no significant differences between F1max-E and F1max-M across all fall heights, but the model significantly over-predicted the F2max-E across all fall heights. After controlling F1max-E and F2max-E for body mass, the force peaks appeared to be weakly related to the anthropometric as well as bone and muscle strength outcomes (r=0.2-0.7, p>0.05). The relationship between bone and muscle strength outcomes appeared to have a tendency to get stronger at higher fall heights.<p> Conclusion. The model predicted experimental F1max, but not experimental F2max. This study presents preliminary pilot results. Larger sample size is needed to confirm whether incorporating bone and muscle strength estimates into fall force prediction models could enhance forearm fracture risk assessments.

Cross-sectional surveys are often used in epidemiological studies to identify subjects with a disease. When estimating the survival function from onset of disease, this sampling mechanism introduces bias, which must be accounted for. If the onset times of the disease are assumed to be coming from a stationary Poisson process, this bias, which is caused by the sampling of prevalent rather than incident cases, is termed length-bias. A one-sample Kolomogorov-Smirnov type of goodness-of-fit test for right-censored length-biased data is proposed and investigated with Weibull, log-normal and log-logistic models. Algorithms detailing how to efficiently generate right-censored length-biased survival data of these parametric forms are given. Simulation is employed to assess the effects of sample size and censoring on the power of the test. Finally, the test is used to evaluate the goodness-of-fit using length-biased survival data of patients with dementia from the Canadian Study of Health and Aging.