In the COVID-19 stock market industries reacted and were affected in different ways. This paper will use Standard Industrial Classification (SIC) codes to look at how sectors and selected industries fared after a whole year in a pandemic. This will be accomplished by comparing 2019 stock returns to 2020 stock returns with a t-test and estimating the effect of COVID-19 positive case and death increases using a pooled OLS regression. All SIC sectors A-J were analyzed as well as 18 selected industries such as food stores, real estate, oil and gas extraction, health services, and communications. Results show a significant variation in the monthly returns of 2019 and 2020. Regression results show that there is a small but positive correlation of sector and industry returns to COVID-19 positive case and death increases. This contrary result can confirm the short influential window of COVID-19 outcomes on the stock market as shown in related research. This also confirms that regardless of the continued escalation of the pandemic, the stock market follows sentiment, not substance. This paper will contribute to the existing literature by conducting a yearlong event study of the United States' sectors and industries during the COVID-19 pandemic.

Background: The emerging cryptocurrency market becomes more and more recognized around the globe. Therefore, it has become of great interest to policymakers, institutional investors, and individual investors. The new encrypted blockchain technology offers individual investors contemporary opportunities to invest contrary to traditional means. However, the volatile market presents instabilities and uncertainty for market participants creating a research gap for academics to investigate what poses these difficulties.  Purpose: The objective of the study is to investigate the determinants that affect individual investors' adoption intention of Bitcoin. By incorporating theories to understand investment behaviors and attitudes.  Methodology: The thesis utilized a quantitative methodology and collected data through an online questionnaire with the help of a Likert-scale instrument. The survey participants ended with a number of 114 respondents that are characterized as young adult investors. Interpretation and evaluation of the results were analyzed through an OLS linear regression with the help of a software program, Minitab.  Findings: Theresearchquestionwasansweredtoasatisfactorylevel,whereresultsattested to past works of literatures. The study found that consumer characteristic is a driving cause for individual investors' adoption intention of Bitcoin. To elaborate, subjective norms of individuals navigate their attitude towards Bitcoin, and investors’ peers’ opinions and acceptance play a crucial role in their engagement in the market. The herding trend was the most significant variable that contributed to investors’ adoption intention. The results also showed a significant correlation toward the technology acceptance model. Nonetheless, the study lacked empirical evidence to support market characteristics steering private investors’ adoption intention.  Implications & Future Research suggestions: The main implications of the study were factors that regarded data collection and methods. Due to a time limitation, the survey was not available for a longer period of time, a longitudinal study could be of interest whilst incorporating more consumer characteristics into the analysis. In addition, future scholars ought to focus on market characteristics and how they influence varying cryptocurrencies such as Ethereum and Tether alongside Bitcoin. To conclude, a larger scope of the study could bring about more significant results and interesting findings.

The level of labor productivity differs among the European Union countries, especially when you compare a developing country to a more developed country in the EU. This is an issue because the achievement of high labor productivity is a necessary stipulation for a developing economy to realize economic growth and more economic development. On the other hand, the more individuals in an economy with access to the internet (internet connectivity) depicts how developed the economy is in terms of information and communication technology (ICT). Accordingly, the purpose of this paper is to ascertain whether there is a positive relationship between countries having high internet connectivity and labor productivity in the EU. In doing so, Political and entrepreneurial decision-makers can use these findings to decide how much attention or budget to put on the ICT sector to improve labor productivity. To understand the factors that affect labor productivity, Adam Smith and Karl Marx’s theory on labor productivity is used to gain a better understanding. A panel data analysis using a fixed-effect model and pooled OLS regression model is applied in the study to predict the relationship. The result of the study indicates that internet connectivity does not have a significant impact on Labour productivity, or there was not enough evidence showing that they are positively correlated with each other.

No description available.

Esta tese propõe o uso da geoinformação a serviço da demografia tomando-se a distribuição da população na Amazônia Brasileira como objeto de estudo. Foram desenvolvidos métodos baseados em dados de sensoriamento remoto e técnicas de análise espacial para representar a população em superfícies de densidade. O objetivo geral foi verificar a utilidade dos dados de sensoriamento remoto para compor a base do processo de redistribuição da população, em comparação com métodos tradicionais. Especificamente, foram utilizadas imagens de luzes noturnas do sistema DMSP/OLS para a Amazônia Legal, e imagens dos sistemas CCD-CBERS1 e TM-Landsat, para a escala municipal. Inicialmente, um mosaico de imagens de luzes noturnas DMSP/OLS foi avaliado quanto à capacidade de detectar presença e atividade humana na região. As relações lineares entre pixels de luzes DMSP e população urbana, área urbanizada e consumo de energia elétrica obtidas, motivaram o desenvolvimento de um produto de luzes noturnas mais recente e adequado para estudos de distribuição de população. A análise deste mosaico indicou o potencial e as restrições da informação de luzes noturnas para estimar a evolução dos valores de população urbana. Um novo método para interpolar e redistribuir população utilizando informações de luzes noturnas, denominado DMSPop_M foi apresentado. A superfície de densidade populacional resultante mostrou-se uma opção intermediária entre as superfícies obtidas através das técnicas tradicionais para interpolar população, e a representação através dos setores censitários. Para a escala municipal, outro método multivariado para distribuir população foi desenvolvido para Marabá - PA. Dados provenientes da classificação digital de imagens CBERS e Landsat e outros dados geográficos foram selecionados como variáveis indicadoras da presença de população. Através de técnicas de pertinência e inferência Fuzzy a população dos setores censitários foi redistribuída em superfícies de densidade populacional. A superfície que melhor representou a distribuição populacional foi aquela obtida através de média simples das variáveis. O método desenvolvido poderá ser aplicado para modelos mais robustos em que as superfícies resultantes refletirão a relação entre variáveis proposta. Os dados de sensoriamento remoto foram fundamentais para incluir a heterogeneidade espacial nos métodos desenvolvidos. Finalmente, este trabalho contribui para que estudos de modelagem, e planejamento para a região Amazônica possam incluir adequadamente a dimensão humana no que se refere à distribuição e representação de sua densidade populacional. / This thesis presents the use of geoinformation as a valuable tool for demographic studies where the subject of interest is the population distribution over the Brazilian Amazon. Population density surfaces based on remote sensing data and spatial analysis techniques are developed as an alternative approach to represent population distribution. Remote sensing data is hypothesized as the basis for disaggregation methods to distribute population in regular cells. Specifically, imagery from DMSP/OLS night-time lights is used at global scale and from CCD-CBERS1 with TM-Landsat imagery at municipal scale. First, DMSP/OLS data is evaluated about the relations between night-time lights and human activities in Amazônia. Significative linear correlations between night-time lights and urban population, electrical power consumption and urban area are obtained. Thus, alternative techniques to generate a night-time lights mosaic are proposed and a new mosaic is generated to be used as a reference of urban population distribution. The analysis of this new DMSP mosaic reveals its potential and restrictions to estimate and to monitor the annual evolution of urban settlements. A new interpolation method to generate population density surface is developed using night-time lights data. The resultant surface, comparing to the usual interpolation methods for population is considered an intermediary option between the traditional techniques and the surface representing population data from a higher scale (the census sector). At municipal scale, a multivariate method to distribute population inside the municipal boundaries is developed for Marabá, state of Pará. CBERS and Landsat imagery and other geographical data are selected as indicator variables of human presence. These variables are converted to Fuzzy memberships and related to each other towards average and Fuzzy operators. The census sector population is redistributed in the population density surfaces. The best surface is obtained from simple average of the indicator variables. The proposed method can support different models and its population density will reproduce the consistence of them. Finally, this thesis contributes to represent the population distribution at global and municipal scale at the Brazilian Amazon. The methods and results obtained here will be helpful for any environmental modeling study in the Amazon region that cares for the local population that has been living there all along.

Rörelsekapitalhantering (WCM) är en viktig del i små och medelstora företags (SMF) i syfte att generera nytt kapital samt effektivisera verksamheten. Företagen i Sverige består till större delen av SMF vilket gör det viktigt att undersöka hur företagen hanterar sitt kapital. Syftet för studien var att studera WCM i form av CCC och WCR med företagsspecifika, branschspecifikaoch makrospecifika faktorer. Detta testades i en OLS-modell med studiens beroende variabler CCC och WCR mot de oberoende variablerna lönsamhet, tillväxtmöjligheter, storlek, kassaflöde och branschtillhörighet. Studien undersökte även hur en finanskris påverkar rörelsekapitalet i form av ett t2-test. Denna studie innefattade 4240 företag under perioden 2008 till 2015 där 2008 till 2009 beskrivs som krisperiod. Resultatet för studien visar ett positivt signifikant samband mellan CCC och kassaflöde samt ett negativt signifikant samband medlönsamhet, storlek och branschtillhörighet. Vidare påvisar studien ett positivt signifikant samband mellan WCR och lönsamhet samt storlek medans tillväxtmöjligheter, kassaflöde och branschtillhörighet har negativt signifikant samband med WCR. Företagen har en längre CCC samt en högre WCR under krisperioder. / Working capital management (WCM) is an important part of small and medium-sized enterprises (SMEs) in order to generate new capital and streamline operations. The companies that consist in Sweden is to the majority SMEs, which makes it important to investigate how companies handle their capital. The purpose of the study was to analyse WCM thru CCC and WCR with company-specific, industry-specific and macro-specific factors. This was tested in an OLS model with this studies dependent variables CCC and WCR against the independent variables profitability, growth opportunities, size, cash flow and industry affiliation. The study also examined how a financial crisis affects working capital with a t2 test. This study included 4240 companies in the period 2008 to 2015, where 2008 to 2009 is described as a crisis period. The result for the study shows a positive significant correlation between CCC and cash flow, as well as a negatively significant correlation with profitability, size and industry affiliation. Furthermore, the study shows a positive significant correlation between WCR and profitability, and the size of the growth potential, cash flow and industry affiliationare negatively significant in relation to the WCR. The companies have a longer CCC and a higher WCR during crisis periods.

Esta tese propõe o uso da geoinformação a serviço da demografia tomando-se a distribuição da população na Amazônia Brasileira como objeto de estudo. Foram desenvolvidos métodos baseados em dados de sensoriamento remoto e técnicas de análise espacial para representar a população em superfícies de densidade. O objetivo geral foi verificar a utilidade dos dados de sensoriamento remoto para compor a base do processo de redistribuição da população, em comparação com métodos tradicionais. Especificamente, foram utilizadas imagens de luzes noturnas do sistema DMSP/OLS para a Amazônia Legal, e imagens dos sistemas CCD-CBERS1 e TM-Landsat, para a escala municipal. Inicialmente, um mosaico de imagens de luzes noturnas DMSP/OLS foi avaliado quanto à capacidade de detectar presença e atividade humana na região. As relações lineares entre pixels de luzes DMSP e população urbana, área urbanizada e consumo de energia elétrica obtidas, motivaram o desenvolvimento de um produto de luzes noturnas mais recente e adequado para estudos de distribuição de população. A análise deste mosaico indicou o potencial e as restrições da informação de luzes noturnas para estimar a evolução dos valores de população urbana. Um novo método para interpolar e redistribuir população utilizando informações de luzes noturnas, denominado DMSPop_M foi apresentado. A superfície de densidade populacional resultante mostrou-se uma opção intermediária entre as superfícies obtidas através das técnicas tradicionais para interpolar população, e a representação através dos setores censitários. Para a escala municipal, outro método multivariado para distribuir população foi desenvolvido para Marabá - PA. Dados provenientes da classificação digital de imagens CBERS e Landsat e outros dados geográficos foram selecionados como variáveis indicadoras da presença de população. Através de técnicas de pertinência e inferência Fuzzy a população dos setores censitários foi redistribuída em superfícies de densidade populacional. A superfície que melhor representou a distribuição populacional foi aquela obtida através de média simples das variáveis. O método desenvolvido poderá ser aplicado para modelos mais robustos em que as superfícies resultantes refletirão a relação entre variáveis proposta. Os dados de sensoriamento remoto foram fundamentais para incluir a heterogeneidade espacial nos métodos desenvolvidos. Finalmente, este trabalho contribui para que estudos de modelagem, e planejamento para a região Amazônica possam incluir adequadamente a dimensão humana no que se refere à distribuição e representação de sua densidade populacional. / This thesis presents the use of geoinformation as a valuable tool for demographic studies where the subject of interest is the population distribution over the Brazilian Amazon. Population density surfaces based on remote sensing data and spatial analysis techniques are developed as an alternative approach to represent population distribution. Remote sensing data is hypothesized as the basis for disaggregation methods to distribute population in regular cells. Specifically, imagery from DMSP/OLS night-time lights is used at global scale and from CCD-CBERS1 with TM-Landsat imagery at municipal scale. First, DMSP/OLS data is evaluated about the relations between night-time lights and human activities in Amazônia. Significative linear correlations between night-time lights and urban population, electrical power consumption and urban area are obtained. Thus, alternative techniques to generate a night-time lights mosaic are proposed and a new mosaic is generated to be used as a reference of urban population distribution. The analysis of this new DMSP mosaic reveals its potential and restrictions to estimate and to monitor the annual evolution of urban settlements. A new interpolation method to generate population density surface is developed using night-time lights data. The resultant surface, comparing to the usual interpolation methods for population is considered an intermediary option between the traditional techniques and the surface representing population data from a higher scale (the census sector). At municipal scale, a multivariate method to distribute population inside the municipal boundaries is developed for Marabá, state of Pará. CBERS and Landsat imagery and other geographical data are selected as indicator variables of human presence. These variables are converted to Fuzzy memberships and related to each other towards average and Fuzzy operators. The census sector population is redistributed in the population density surfaces. The best surface is obtained from simple average of the indicator variables. The proposed method can support different models and its population density will reproduce the consistence of them. Finally, this thesis contributes to represent the population distribution at global and municipal scale at the Brazilian Amazon. The methods and results obtained here will be helpful for any environmental modeling study in the Amazon region that cares for the local population that has been living there all along.

In 2022, food prices increased rapidly, prompting this investigation into how the price shock has impacted consumer behavior. Previous studies have found that price shocks affect low- income households with children the most. A switch to more calorie-dense food and a decreased consumption of snacks. This study aims to estimate the price elasticity of various food products and to identify how the elasticities differ among various types of households. The elasticities are calculated using OLS regression on receipt data from Coop. Results show that household variables, including income level, presence of children, shopping location, and organic product preferences, significantly impact the price elasticity of food products. Low-income households without children, living outside major cities and their suburbs, have a higher price elasticity, meaning these shoppers are more sensitive to price changes. The same tendencies were observed for all products even though the exact parameters could only be proven for a third of the products. The findings have important implications for understanding how price changes affect consumer behavior and can inform food policy decisions. / Under 2022 ökade livsmedelspriserna kraftigt, vilket inspirerade denna undersökning av hur prischocken har påverkat konsumenternas beteende. Tidigare studier visar att prischocker har störst påverkan på låginkomsthushåll med barn. Även en övergång till mer kaloririk mat och en minskad konsumption av snacks har observerats. Syftet med denna studie är att uppskatta priselasticiteten för olika livsmedel och identifiera hur elasticiteten skiljer sig åt mellan olika typer av hushåll. Elasticiteterna beräknas med hjälp av en OLS-regression på kvittodata från Coop. Resultaten visar att hushållsvariabler, inklusive inkomstnivå, barn i hushållet, varans inköpsplats och val av ekologiska produkter, påverkar priselasticiteten för livsmedel markant. Hushåll med låg inkomst utan barn, som bor utanför större städer och deras förorter, har en högre priselasticitet, vilket innebär att dessa kunder är mer priskänsliga. Samma tendenser observerades för alla produkter även om de exakta parametrarna endast kunde bevisas för en tredjedel av produkterna. Resultaten har viktiga implikationer för förståelsen av hur prisförändringar påverkar konsumentbeteenden och kan även informera livsmedelspolitiska beslut.

Att estimera slutpriset av en bostadsförsäljning är en komplex uppgift där mäklartexter som beskriver bostäder är en vital del av försäljningen. Denna rapport undersöker om det går att använda mäklartexter för att generera mer träffsäkra estimeringar med maskininlärningsmodeller. Två olika maskininlärningsmodeller implementerades som resultat av en litteraturstudie och utvärderades mot Boolis existerande OLS-modell. De implementerade modellerna är OLS-BERT och Elman regressionsnätverk. OLS-BERT visade en generell förbättring jämfört med Boolis OLS-modell, i synnerhet av F-statistik där mätvärdet sjönk med 99,8 procent. P-värdet i T-statistik för “vista” (utsikten) har sjunkit från 37,7 till 1 procent. Elman regressionsnätverket sänkte Boolis OLS-modells MAPE från 58,5 till 6,62 procent. Modellerna utvärderades med åtta olika mått varav de för studiens viktigaste är MAPE, MAE, F-statistik och T-statistik. Genom att bryta ut attribut ur mäklartexter kan modellen förklara signifikansen hos indata, samt få något mer träffsäkra estimeringar av slutpriset av en bostadsförsäljning. Resultaten visar att det är en intressant metod som med fördel kan vidare utforskas. / Estimating the price of home sales is a complex task, where broker texts describing the housing is a vital part of the sales. This study explore the possibility to use broker texts to generate more accurate estimations using machine learning models. Two different machine learning models were implemented as a result of a literature study and evaluated against Booli’s existing OLS-model. The implemented machine learning models are OLS-BERT and an Elman regression network. OLS-BERT showed a general improvement compared to Booli’s OLS-model, in particular the F-statistic were 99.8 percent lower than Booli’s OLS-model. The p-value in T-statistic for “vista” was 37.7 percent with Booli’s OLS-model and 1 percent with OLS-BERT. The Elman regression network lowered the MAPE of Booli’s OLS-model from 58.5 to 6.62 percent. All models were evaluated using eight different measures, of which the most important for this study is MAPE, MAE, F-statistic, and T-statistic. The conclusion is that by mining attributes from broker texts the models can explain the significance of the input and generate somewhat more accurate estimations of the home sales price of sale. The results show that this is an interesting method that should be further explored.

ヾ本研究之目的有三：１．說明如何利用驗證性因素分析法來克服探索性因素分析法 所無法解決的困難。２．說明如何將模式之限制設定更加一般化，以適合模式之不同 假設。３．利用矩陣語言設計程式，說明當研究者汲有套裝程式可用或已有的套裝程 式無法滿足研究上的需要時，可以矩陣語言加以克服。ゝ本研究之文獻回顧包括兩個 部分：１．估計方法之文獻回顧，旨在說明多群因素分析法和工具變項因素分析法的 估計程序和其優缺點。２．反覆算法之文獻回顧，說明可用於或曾用於驗證性因素分 析法之反覆算法及其優缺點。ゞ研究方法：本研究以類似非線性迴歸中之普通最小平 方法、一般化最小平方法和最大概似法導出配適函數，依此，再利用反覆算法估計參 數及參數之標準差。々研究內容：本研究之主要內有五，１．比較驗證性因素分析法 和探索性因素分析法在設定假設上的不同。２．說明如何確認模式。３．說明如何在 不設定限制和設定限制下估計參數及參數標準差。４．如何建構檢定統計量以檢定模 式之配適結果。５．利用ＯＬＳ、ＧＬＳ、ＭＬ三種方法來做實證。ぁ研究結果：本 研究之結果主要有四，１．不論利用ＯＬＳ、ＧＬＳ或ＭＬ來估計模式參數時，皆需 要大樣本較合適。２．利用ＯＬＳ、ＧＬＳ和ＭＬ來估計模式參數時，皆要基於常態 性的假設。３．模式利用等式和不等式來設定參數的限制，使得模式更加一般化。４ ．利用矩陣語言所設計的程式可用於實證上。