There seems to be a need for further exploration in this area for the purpose of clarifying which Rorschach indices are indicators of intelligence. R. W. W%, D%, F+%, A%, H, H%, M, and N have each been selected for statistical analysis on the basis of one or more of these three factors: (1) Rorschach, Klopfer and Kelly, and Beck have stated that the index is an indicator of intelligence, (2) extensive definition of the index implies some relation to intelligence, (3) past literature indicates that the index correlates positively with intelligence.

Over the last years, the growing availability of large datasets and the improvements in the computational speed of computers have further fostered the research in the fields of both macroeconomic modeling and forecasting analysis. A primary focus of these research areas is to improve the models performance by exploiting the informational content of several time series. Increasing the dimension of macro models is indeed crucial for a detailed structural understanding of the economic environment, as well as for an accurate forecasting analysis. As consequence, a new generation of large-scale macro models, based on the micro-foundations of a fully specified dynamic stochastic general equilibrium set-up, has became one of the most flourishing research areas of interest both in central banks and academia. At the same time, there has been a revival of forecasting methods dealing with many predictors, such as the factor models. The central idea of factor models is to exploit co-movements among variables through a parsimonious econometric structure. Few underlying common shocks or factors explain most of the co-variations among variables. The unexplained component of series movements is on the other hand due to pure idiosyncratic dynamics. The generality of their framework allows factor models to be suitable for describing a broad variety of models in a macroeconomic and a financial context. The revival of factor models, over the recent years, comes from important developments achieved by Stock and Watson (2002) and Forni, Hallin, Lippi and Reichlin (2000). These authors find the conditions under which some data averages become collinear to the space spanned by the factors when, the cross section dimension, becomes large. Moreover, their factor specifications allow the idiosyncratic dynamics to be mildly cross-correlated (an effect referred to as the 'approximate factor structure' by Chamberlain and Rothschild, 1983), a situation empirically verified in many applications. These findings have relevant implications. The most important being that the use of a large number of series is no longer representative of a dimensional constraint. On the other hand, it does help to identify the factor space. This new generation of factor models has been applied in several areas of macroeconomics and finance as well as for policy evaluation. It is consequently very likely to become a milestone in the literature of forecasting methods using many predictors. This thesis contributes to the empirical literature on factor models by proposing four original applications. In the first chapter of this thesis, the generalized dynamic factor model of Forni et. al (2002) is employed to explore the predictive content of the asset returns in forecasting Consumer Price Index (CPI) inflation and the growth rate of Industrial Production (IP). The connection between stock markets and economic growth is well known. In the fundamental valuation of equity, the stock price is equal to the discounted future streams of expected dividends. Since the future dividends are related to future growth, a revision of prices, and hence returns, should signal movements in the future growth path. Though other important transmission channels, such as the Tobin's q theory (Tobin, 1969), the wealth effect as well as capital market imperfections, have been widely studied in this literature. I show that an aggregate index, such as the S&P500, could be misleading if used as a proxy for the informative content of the stock market as a whole. Despite the widespread wisdom of considering such index as a leading variable, only part of the assets included in the composition of the index has a leading behaviour with respect to the variables of interest. Its forecasting performance might be poor, leading to sceptical conclusions about the effectiveness of asset prices in forecasting macroeconomic variables. The main idea of the first essay is therefore to analyze the lead-lag structure of the assets composing the S&P500. The classification in leading, lagging and coincident variables is achieved by means of the cross correlation function cleaned of idiosyncratic noise and short run fluctuations. I assume that asset returns follow a factor structure. That is, they are the sum of two parts: a common part driven by few shocks common to all the assets and an idiosyncratic part, which is rather asset specific. The correlation function, computed on the common part of the series, is not affected by the assets' specific dynamics and should provide information only on the series driven by the same common factors. Once the leading series are identified, they are grouped within the economic sector they belong to. The predictive content that such aggregates have in forecasting IP growth and CPI inflation is then explored and compared with the forecasting power of the S&P500 composite index. The forecasting exercise is addressed in the following way: first, in an autoregressive (AR) model I choose the truncation lag that minimizes the Mean Square Forecast Error (MSFE) in 11 years out of sample simulations for 1, 6 and 12 steps ahead, both for the IP growth rate and the CPI inflation. Second, the S&P500 is added as an explanatory variable to the previous AR specification. I repeat the simulation exercise and find that there are very small improvements of the MSFE statistics. Third, averages of stock return leading series, in the respective sector, are added as additional explanatory variables in the benchmark regression. Remarkable improvements are achieved with respect to the benchmark specification especially for one year horizon forecast. Significant improvements are also achieved for the shorter forecast horizons, when the leading series of the technology and energy sectors are used. The second chapter of this thesis disentangles the sources of aggregate risk and measures the extent of co-movements in five European stock markets. Based on the static factor model of Stock and Watson (2002), it proposes a new method for measuring the impact of international, national and industry-specific shocks. The process of European economic and monetary integration with the advent of the EMU has been a central issue for investors and policy makers. During these years, the number of studies on the integration and linkages among European stock markets has increased enormously. Given their forward looking nature, stock prices are considered a key variable to use for establishing the developments in the economic and financial markets. Therefore, measuring the extent of co-movements between European stock markets has became, especially over the last years, one of the main concerns both for policy makers, who want to best shape their policy responses, and for investors who need to adapt their hedging strategies to the new political and economic environment. An optimal portfolio allocation strategy is based on a timely identification of the factors affecting asset returns. So far, literature dating back to Solnik (1974) identifies national factors as the main contributors to the co-variations among stock returns, with the industry factors playing a marginal role. The increasing financial and economic integration over the past years, fostered by the decline of trade barriers and a greater policy coordination, should have strongly reduced the importance of national factors and increased the importance of global determinants, such as industry determinants. However, somehow puzzling, recent studies demonstrated that countries sources are still very important and generally more important of the industry ones. This paper tries to cast some light on these conflicting results. The chapter proposes an econometric estimation strategy more flexible and suitable to disentangle and measure the impact of global and country factors. Results point to a declining influence of national determinants and to an increasing influence of the industries ones. The international influences remains the most important driving forces of excess returns. These findings overturn the results in the literature and have important implications for strategic portfolio allocation policies; they need to be revisited and adapted to the changed financial and economic scenario. The third chapter presents a new stylized fact which can be helpful for discriminating among alternative explanations of the U.S. macroeconomic stability. The main finding is that the fall in time series volatility is associated with a sizable decline, of the order of 30% on average, in the predictive accuracy of several widely used forecasting models, included the factor models proposed by Stock and Watson (2002). This pattern is not limited to the measures of inflation but also extends to several indicators of real economic activity and interest rates. The generalized fall in predictive ability after the mid-1980s is particularly pronounced for forecast horizons beyond one quarter. Furthermore, this empirical regularity is not simply specific to a single method, rather it is a common feature of all models including those used by public and private institutions. In particular, the forecasts for output and inflation of the Fed's Green book and the Survey of Professional Forecasters (SPF) are significantly more accurate than a random walk only before 1985. After this date, in contrast, the hypothesis of equal predictive ability between naive random walk forecasts and the predictions of those institutions is not rejected for all horizons, the only exception being the current quarter. The results of this chapter may also be of interest for the empirical literature on asymmetric information. Romer and Romer (2000), for instance, consider a sample ending in the early 1990s and find that the Fed produced more accurate forecasts of inflation and output compared to several commercial providers. The results imply that the informational advantage of the Fed and those private forecasters is in fact limited to the 1970s and the beginning of the 1980s. In contrast, during the last two decades no forecasting model is better than "tossing a coin" beyond the first quarter horizon, thereby implying that on average uninformed economic agents can effectively anticipate future macroeconomics developments. On the other hand, econometric models and economists' judgement are quite helpful for the forecasts over the very short horizon, that is relevant for conjunctural analysis. Moreover, the literature on forecasting methods, recently surveyed by Stock and Watson (2005), has devoted a great deal of attention towards identifying the best model for predicting inflation and output. The majority of studies however are based on full-sample periods. The main findings in the chapter reveal that most of the full sample predictability of U.S. macroeconomic series arises from the years before 1985. Long time series appear to attach a far larger weight on the earlier sub-sample, which is characterized by a larger volatility of inflation and output. Results also suggest that some caution should be used in evaluating the performance of alternative forecasting models on the basis of a pool of different sub-periods as full sample analysis are likely to miss parameter instability. The fourth chapter performs a detailed forecast comparison between the static factor model of Stock and Watson (2002) (SW) and the dynamic factor model of Forni et. al. (2005) (FHLR). It is not the first work in performing such an evaluation. Boivin and Ng (2005) focus on a very similar problem, while Stock and Watson (2005) compare the performances of a larger class of predictors. The SW and FHLR methods essentially differ in the computation of the forecast of the common component. In particular, they differ in the estimation of the factor space and in the way projections onto this space are performed. In SW, the factors are estimated by static Principal Components (PC) of the sample covariance matrix and the forecast of the common component is simply the projection of the predicted variable on the factors. FHLR propose efficiency improvements in two directions. First, they estimate the common factors based on Generalized Principal Components (GPC) in which observations are weighted according to their signal to noise ratio. Second, they impose the constraints implied by the dynamic factors structure when the variables of interest are projected on the common factors. Specifically, they take into account the leading and lagging relations across series by means of principal components in the frequency domain. This allows for an efficient aggregation of variables that may be out of phase. Whether these efficiency improvements are helpful to forecast in a finite sample is however an empirical question. Literature has not yet reached a consensus. On the one hand, Stock and Watson (2005) show that both methods perform similarly (although they focus on the weighting of the idiosyncratic and not on the dynamic restrictions), while Boivin and Ng (2005) show that SW's method largely outperforms the FHLR's and, in particular, conjecture that the dynamic restrictions implied by the method are harmful for the forecast accuracy of the model. This chapter tries to shed some new light on these conflicting results. It focuses on the Industrial Production index (IP) and the Consumer Price Index (CPI) and bases the evaluation on a simulated out-of sample forecasting exercise. The data set, borrowed from Stock and Watson (2002), consists of 146 monthly observations for the US economy. The data spans from 1959 to 1999. In order to isolate and evaluate specific characteristics of the methods, a procedure, where the two non-parametric approaches are nested in a common framework, is designed. In addition, for both versions of the factor model forecasts, the chapter studies the contribution of the idiosyncratic component to the forecast. Other non-core aspects of the model are also investigated: robustness with respect to the choice of the number of factors and variable transformations. Finally, the chapter performs a sub-sample performances of the factor based forecasts. The purpose of this exercise is to design an experiment for assessing the contribution of the core characteristics of different models to the forecasting performance and discussing auxiliary issues. Hopefully this may also serve as a guide for practitioners in the field. As in Stock and Watson (2005), results show that efficiency improvements due to the weighting of the idiosyncratic components do not lead to significant more accurate forecasts, but, in contrast to Boivin and Ng (2005), it is shown that the dynamic restrictions imposed by the procedure of Forni et al. (2005) are not harmful for predictability. The main conclusion is that the two methods have a similar performance and produce highly collinear forecasts.

Grizzly and polar bears are both species at the top of the food chain in their respective ecosystems, and as such are indicative of the overall health of the ecosystem. Presently there is little data regarding the stress status of these animals. The development of reliable indicators of stress is important as both species face rapid environmental change. Polar bears from Hudson’s Bay (Ontario, Canada) and grizzly bears from Alberta, Canada, were anaesthetized and blood samples retrieved. Samples were assayed for changes in serum-based indicators of stress. Serum cortisol levels, the predominant corticosteroid in mammals and a commonly used indicator of stress, was measured to evaluate its potential as a chronic stress indicator in bears. The induction time of the cortisol response to stressor exposure is rapid and will be influenced by the stress relating to capture. Hence, serum levels of heat shock proteins (hsps), specifically the 60 (hsp60) and 70 kilodalton (hsp70) families of hsps were also measured to evaluate their reliability as a stress indicator in bears. Traditionally, heat shock proteins have been measured in tissues; however recent studies have indicated their presence in serum in response to chronic stress. In addition, the study examined the feasibility of using corticosteroid-binding globulin (CBG), a serum protein that binds cortisol, as a stress indicator in bears. CBG regulates the availability of cortisol to the tissues (only unbound cortisol elicits a response) but unlike cortisol is not rapidly regulated by acute stress. Bear CBG was isolated and a specific anti-bear CBG antibody was generated. The development of an enzyme-linked immunoadsorbant assay (ELISA) using this bear anti-CBG has the potential to be a useful tool to determine longer-term stress response in bears. Known life-history variables were correlated to the observed levels of serum indicators to elucidate which environmental factors impact bears. The length of sea ice coverage was the strongest determinant of serum cortisol and hsp70 levels in polar bears; the longer ice cover reflects more feeding time and this is reduced through climatic warming. This suggests that fasting associated metabolic changes may be impacting serum cortisol response and hsp70 levels in polar bears. For grizzly bears the proportion of protected homerange had the strongest correlation with stress indicators. This suggests that human impact on the environment, including resource extraction and landscape changes, result in altered levels of serum cortisol and hsp70 levels. Hsp60 was not observed to vary significantly in the face of changing environmental variables, and as such no correlation could be made between serum hsp60 levels and environmental variables in bears. Serum hsp70 was observed to change significantly in response to environmental variables in both polar and grizzly bears. These data along with the changes in cortisol and other health based indicators have the potential to make hsp70 a useful indicator of altered health status in bears. This study is the first attempt to integrate the usefulness of a suite of serum indicators of stress as a tool for detecting the health status of bears. The lack of a control group for comparison to wild population limits the utility of the observed variables as a tool to detect stressed states in bears. However, as these serum indicators are also modulated by the animals health life-history, including food limitation, the monitoring of these serum stress indicators, along with other indicators of fed and fasted states, may give a better picture of the health status of the animal related to nutrient availability.

Grizzly and polar bears are both species at the top of the food chain in their respective ecosystems, and as such are indicative of the overall health of the ecosystem. Presently there is little data regarding the stress status of these animals. The development of reliable indicators of stress is important as both species face rapid environmental change. Polar bears from Hudson’s Bay (Ontario, Canada) and grizzly bears from Alberta, Canada, were anaesthetized and blood samples retrieved. Samples were assayed for changes in serum-based indicators of stress. Serum cortisol levels, the predominant corticosteroid in mammals and a commonly used indicator of stress, was measured to evaluate its potential as a chronic stress indicator in bears. The induction time of the cortisol response to stressor exposure is rapid and will be influenced by the stress relating to capture. Hence, serum levels of heat shock proteins (hsps), specifically the 60 (hsp60) and 70 kilodalton (hsp70) families of hsps were also measured to evaluate their reliability as a stress indicator in bears. Traditionally, heat shock proteins have been measured in tissues; however recent studies have indicated their presence in serum in response to chronic stress. In addition, the study examined the feasibility of using corticosteroid-binding globulin (CBG), a serum protein that binds cortisol, as a stress indicator in bears. CBG regulates the availability of cortisol to the tissues (only unbound cortisol elicits a response) but unlike cortisol is not rapidly regulated by acute stress. Bear CBG was isolated and a specific anti-bear CBG antibody was generated. The development of an enzyme-linked immunoadsorbant assay (ELISA) using this bear anti-CBG has the potential to be a useful tool to determine longer-term stress response in bears. Known life-history variables were correlated to the observed levels of serum indicators to elucidate which environmental factors impact bears. The length of sea ice coverage was the strongest determinant of serum cortisol and hsp70 levels in polar bears; the longer ice cover reflects more feeding time and this is reduced through climatic warming. This suggests that fasting associated metabolic changes may be impacting serum cortisol response and hsp70 levels in polar bears. For grizzly bears the proportion of protected homerange had the strongest correlation with stress indicators. This suggests that human impact on the environment, including resource extraction and landscape changes, result in altered levels of serum cortisol and hsp70 levels. Hsp60 was not observed to vary significantly in the face of changing environmental variables, and as such no correlation could be made between serum hsp60 levels and environmental variables in bears. Serum hsp70 was observed to change significantly in response to environmental variables in both polar and grizzly bears. These data along with the changes in cortisol and other health based indicators have the potential to make hsp70 a useful indicator of altered health status in bears. This study is the first attempt to integrate the usefulness of a suite of serum indicators of stress as a tool for detecting the health status of bears. The lack of a control group for comparison to wild population limits the utility of the observed variables as a tool to detect stressed states in bears. However, as these serum indicators are also modulated by the animals health life-history, including food limitation, the monitoring of these serum stress indicators, along with other indicators of fed and fasted states, may give a better picture of the health status of the animal related to nutrient availability.

Includes bibliographical references (p. 37-41).

published_or_final_version / Medical Sciences / Master / Master of Medical Sciences

No description available.

No description available.

Over the past decades, there has been increase in the consumption of the fresh tomato in the United States; this has been attributed to the nutritional benefits of fresh tomato, its widespread use in cooking and its availability throughout the year. In a Food and Agricultural Organization report, the United States was ranked as one of the largest producers of the fresh tomato in the world. In spite of its large production capacity, large quantities of the tomatoes are still being imported to the United States annually from Mexico. Series of multistate outbreaks of Salmonella infection have been associated with consumption of the fresh tomatoes; traceback of the tomatoes implicated in salmonellosis has been traced to tomatoes grown domestically. However, a survey conducted by U.S. Department of Agriculture on both domestic and imported tomatoes determined that imported fresh tomato was Salmonella positive. The purpose of this study was to determine the microbiological quality of fresh tomatoes imported from Mexico to the United States. The study consisted of sampling surfaces of cleaned tomatoes in Mexico prior to packing and shipping to the United States, and sampling of the tomato wash water at the end of the work shift at a Mexican tomato packinghouse. Four tomatoes were randomly sampled prior to packing, and they were rinsed with Universal Preenrichment Broth (UPB), this was repeated 10 times per working shift, with 2 shifts per day. 102 l of tomato wash water were collected and sampled with the aid of Modified Moore’s Swab (MMS) and membrane filter. The tomato wash water was collected at the end of shift twice daily. Both fruit and wash water samplings were repeated 3 times during the tomato harvesting season. Both the tomato UPB rinsates and the membrane filter were assayed for the E. coli and enterococci populations. Additionally, the tomato UPB rinsates and MMS were assayed for the presence of Salmonella. The results of the microbiological analysis on the UPB rinsates showed that no Salmonella was present, E. coli was not detectable (< 1.0), and the mean populations of enterococci were log 3.8, 2.6, and 1.0 CFU/g in sampling trials 1, 2, and 3 respectively. In the tomato wash water, no Salmonella was present, and no E. coli and enterococci were detected. Therefore, it was concluded that the microbiological quality of the tomatoes that were sampled and tested were high, this was due to the fact that all the samples collected tested negative to Salmonella analysis, and no E. coli was detected in any of the samples.

Thesis (M.Env.St.)--University of Adelaide, Mawson Graduate Centre for Environmental Studies, 1999. / Bibliography: leaves 102-108.