This paper investigated the effects of panic buying induced by the Covid-19 pandemic on groceries' Consumer Price Index (CPI) within Sweden. The years of interest are mainly 2020 to 2021. However, the thesis also looks into years before the pandemic, specifically 2018-2019. The use of years before the pandemic, is to understand better how the CPI of selected groceries behaves in ordinary years and see if changes in CPI from 2020 to 2021 were out of the ordinary. The paper discusses different economic behaviours, specifically looking into how times of crisis affect consumer behaviour. To better understand how CPI for the chosen groceries behaves, graphs were created to show how the CPI values of the goods change over the years. Secondly, two regression analyses were performed in STATA to test whether there are any correlation between panic buying and changes in the CPI values for the chosen groceries. The results given from the graphs indicated that there could be some relationship between panic buying and CPI changes. However, once the regression analyses had been performed, the results showed no correlation between panic buying and changes in CPI for the chosen groceries. This result is not unexpected, as there can be a variety of reasons behind why panic buying did not induce any abnormal changes in CPI for the goods. These reasons are discussed further later on in the thesis as well.

<p> The paper indicates, by a review of the early growth centre literature and the later spatial analysis literature, how little is known, particularly in quantitative terms, about the spatial impacts of growth centres. A regression model is then presented by which several aspects of the spatial impacts of growth centres in Ontario are investigated. Generally, it was found that growth was polarised around a set of designated growth centres and this growth diffused away from the growth centres quite gradually. The exceptions were for large centres, growing slowly, where growth rates increased sharply as distance to growth centres increased and for small centres , growing rapidly, where growth rates decreased rapidly with distance from growth centres. </p> <p> From the regression model, a further model was derived which was used to investigate the extent of spread effects from growth centres in Ontario. The approximate mean maximum distance of the diffusion of spread effects from growth centres was found to be 163 miles. This could have important implications for the spacing of growth centres and government policies relating to growth centres. </p> <p> The analysis also investigates the relationship between growth rates and population size and this was found to be non-linear. Generally, for small centres, population. size and growth rates were negatively related: for intermediate-sized centres the relationship was positive; and for large centres the relationship was again negative. </p> / Thesis / Master of Arts (MA)

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With the rapid advancement of hardware and software technologies, machine learning has been pushed to the forefront of business value generating technologies. More and more businesses start to invest in machine learning to keep up with those that have already benefited from it. A local paper processing business is looking to improve upon the estimation of each order's runtime on the machines by leveraging the machine learning technologies. Traditionally, the predictions are done by experienced planners, but the actual runtimes do not always match the predictions. This thesis conducted an investigation about whether a machine learning model could be built to produce better estimations on behalf of the local business. By following a well-defined machine learning workflow in combination with Microsoft's AutoML model builder and data processing techniques, the result shows that predictions made by the machine learning model are able to perform better than the human made ones within an accepted margin.

Purpose – This report aims to address the methods used to obtain multi-regional input-output tables (MRIO-tables). Method – The research focuses on three gravity model of trade methods: simple gravity model estimation, doubly constrained gravity model estimation, and gravity model estimation with calibrated error function minimization. These methods are used for estimating and modelling multiregional trade flows, specifically in the context of MRIO-tables. These methods will be denoted as method 1, method 2 and method 3. Through a comparative analysis, the study focuses on the strengths and limitations of these methods and provides valuable insights for policymakers and researchers in the field. The findings contribute to a better understanding of the differences between the methods and their effectiveness in accurately representing MRIO-tables. Findings – This study evaluates three methods (mentioned above) for estimating multiregional trade flows, highlighting their performance. Method 1 and 2, exhibited similarities in their approach to estimating trade flows, both surpassing the performance of method 3 across various evaluation metrics. According to the results, method 1 and 2 are better than method 3 in accurately estimating multiregional trade flows. Limitations – This work had some limitations, the research focused on one specific product and how they flow between and across the regions as well as the total quantity of this product, i.e., the margins.

The dissertation examines advancements in the methods and techniques used in the field of econometrics. These advancements include: (i) a re-examination of the underlying statistical foundations of statistical models with binary dependent variables. (ii) using feed-forward backpropagation artificial neural networks for modeling dichotomous choice processes, and (iii) the estimation of unconditional demand elasticities using the flexible multistage demand system with asymmetric partitions and fixed effects across time. The first paper re-examines the underlying statistical foundations of statistical models with binary dependent variables using the probabilistic reduction approach. This re-examination leads to the development of the Bernoulli Regression Model, a family of statistical models arising from conditional Bernoulli distributions. The paper provides guidelines for specifying and estimating a Bernoulli Regression Model, as well as, methods for generating and simulating conditional binary choice processes. Finally, the Multinomial Regression Model is presented as a direct extension. The second paper empirically compares the out-of-sample predictive capabilities of artificial neural networks to binary logit and probit models. To facilitate this comparison, the statistical foundations of dichotomous choice models and feed-forward backpropagation artificial neural networks (FFBANNs) are re-evaluated. Using contingent valuation survey data, the paper shows that FFBANNs provide an alternative to the binary logit and probit models with linear index functions. Direct comparisons between the models showed that the FFBANNs performed marginally better than the logit and probit models for a number of within-sample and out-of-sample performance measures, but in the majority of cases these differences were not statistically significant. In addition, guidelines for modeling contingent valuation survey data and techniques for estimating median WTP measures using FFBANNs are examined. The third paper estimates a set of unconditional price and expenditure elasticities for 49 different processed food categories using scanner data and the flexible and symmetric translog (FAST) multistage demand system. Due to the use of panel data and the presence of heterogeneity across time, temporal fixed effects were incorporated into the model. Overall, estimated price elasticities are larger, in absolute terms, than previous estimates. The use of disaggregated product groupings, scanner data, and the estimation of unconditional elasticities likely accounts for these differences. / Ph. D.

This thesis deals with the development, implementation and application of statistical modeling techniques which can be employed in the analysis of credit ratings. Credit ratings are one of the most widely used measures of credit risk and are relevant for a wide array of financial market participants, from investors, as part of their investment decision process, to regulators and legislators as a means of measuring and limiting risk. The majority of credit ratings is produced by the "Big Three" credit rating agencies Standard & Poors', Moody's and Fitch. Especially in the light of the 2007-2009 financial crisis, these rating agencies have been strongly criticized for failing to assess risk accurately and for the lack of transparency in their rating methodology. However, they continue to maintain a powerful role as financial market participants and have a huge impact on the cost of funding. These points of criticism call for the development of modeling techniques that can 1) facilitate an understanding of the factors that drive the rating agencies' evaluations, 2) generate insights into the rating patterns that these agencies exhibit. This dissertation consists of three research articles. The first one focuses on variable selection and assessment of variable importance in accounting-based models of credit risk. The credit risk measure employed in the study is derived from credit ratings assigned by ratings agencies Standard & Poors' and Moody's. To deal with the lack of theoretical foundation specific to this type of models, state-of-the-art statistical methods are employed. Different models are compared based on a predictive criterion and model uncertainty is accounted for in a Bayesian setting. Parsimonious models are identified after applying the proposed techniques. The second paper proposes the class of multivariate ordinal regression models for the modeling of credit ratings. The model class is motivated by the fact that correlated ordinal data arises naturally in the context of credit ratings. From a methodological point of view, we extend existing model specifications in several directions by allowing, among others, for a flexible covariate dependent correlation structure between the continuous variables underlying the ordinal credit ratings. The estimation of the proposed models is performed using composite likelihood methods. Insights into the heterogeneity among the "Big Three" are gained when applying this model class to the multiple credit ratings dataset. A comprehensive simulation study on the performance of the estimators is provided. The third research paper deals with the implementation and application of the model class introduced in the second article. In order to make the class of multivariate ordinal regression models more accessible, the R package mvord and the complementary paper included in this dissertation have been developed. The mvord package is available on the "Comprehensive R Archive Network" (CRAN) for free download and enhances the available ready-to-use statistical software for the analysis of correlated ordinal data. In the creation of the package a strong emphasis has been put on developing a user-friendly and flexible design. The user-friendly design allows end users to estimate in an easy way sophisticated models from the implemented model class. The end users the package appeals to are practitioners and researchers who deal with correlated ordinal data in various areas of application, ranging from credit risk to medicine or psychology.