This study uses a random coefficient estimation procedure to analyze the U.S. gasoline market from 1960-1995 with three main objectives: (1) provide an empirical methodology that can estimate a gasoline demand function capable of performing well in prediction; (2) evaluate the elasticities of the models presented to determine which model is more accurate at capturing supply shocks that impacted gasoline demand; and (3) evaluate the behavior of the elasticites of the beta coefficients. This research will show that the variation from historical economic patterns was a result of supply shocks. I argue that when the OLS model of the gasoline market developed by William H. Greene is used supply shocks are not well captured because the coefficients are fixed. If the random coefficient model developed by P.A.V.B. Swamy is introduced, the coefficients vary over time, and thereby, enable supply shocks to be included in the model and more accurate forecasts are produced, as well as, meaningful time patterns in the beta coefficients. / Master of Arts

Dans ce mémoire, une nouvelle approche neuronale TLS EXIN est proposée pour estimer la position d'un récepteurGPS. L'idée générale de cette approche est d'avoir une méthode plus robuste pour le calcul de la position.Le pseudorange alone est l'une des techniques les plus simples et les plus utilisées pour le positionnement GPS.Cette technique nécessite la résolution d'un système surdéterminé d'équations linéaires Ax+/-b. En général, lesmoindres carrés ordinaires (OLS) et les moindres carrés pondérés (WLS) sont les méthodes couramment utiliséespour estimer la position d'un récepteur grâce à leur rapidité et leur robustesse, mais la structure particulière de lamatrice de données A et les bruits affectant ses entrées ne sont pas considérés. Au contraire, cette thèse a pourobjectif d'analyser ces problèmes et d'étudier le comportement des méthodes des moindres carrés (LS) enprésence d'une matrice de données A bruitée.L'approche des moindres carrés totaux (TLS) prend en compte le bruit dans la matrice de données A ainsi que dansle vecteur d'observation b. Cette dernière est une technique moins robuste que OLS et plus sensible auchangement des données, elle est en général résolue par une méthode directe. La méthode TLS EXIN basée surles réseaux de neurones est un algorithme itératif (flux gradient) pour résoudre le problème TLS. Elle donne unmeilleur résultat parce qu'elle peut exploiter les informations d'état initial provenant des époques précédentes et, encas de conditions initiales nulles, donne une estimation précise même en cas de problème singulier.Pour réaliser des comparaisons entre les différentes méthodes des moindres carrés (LS), deux jeux de données ontété collectés. Le premier jeu de données est issu du réseau TERIA et comporte des données collectées depuisdifférentes stations de référence situées dans toute la France. Le deuxième jeu de données est le résultat d'unecampagne de mesures utilisant un appareil GPS (Ublox NL-6002U).Grâce à ces données réelles, un nombre de conditionnement bas a été estimé. Dans ce cas, toutes les méthodesLS donnent des estimations équivalentes, et le choix du meilleur algorithme (OLS, et surtout, WLS) est privilégiépour leur rapidité de calcul. Cependant, le pire scénario qui puisse se produire a été étudié (dans le cas d'unsatellite éloigné), et ont été observés des mauvais conditionnements du problème de GPS (nombre deconditionnement élevé). Cette situation extrême justifie l'utilisation du réseau neuronal TLS EXIN. Les résultatsobtenus confirment cette approche, même pour un nombre de conditionnement élevé.____________________________________________________________________________________________ / In this thesis, the neural approach TLS EXIN was proposed in a new way in order to estimate the position of a GPSreceiver. The general idea of this approach is to develop a more robust method for calculating the position.The ¿pseudorange alone¿ method is one of the simplest techniques and most widely used for estimating the GPSpositioning and it results in solving an overdetermined system of linear equations Ax¿b. In general, the ordinaryleast squares (OLS) and weighted least squares (WLS) are the commonly used methods to estimate the position ofa receiver for their quickness and robustness, but the particular structure of the data matrix A and the noise affectingits entries are not considered. This thesis, instead, aims to address these problems and study the behavior of leastsquares (LS) methods in the presence of a noisy data matrix A.The approach of total least squares (TLS) takes into account the noise in A and b. It is a less robust technique thanOLS and more sensitive to data changes. It is general solved as a direct method. Instead, the neural network TLSEXIN, which is an iterative algorithm (gradient flow) for solving TLS, works better both because it may exploit theinitial condition information derived from the previous epochs, and, in case of null initial conditions, yields anaccurate estimate even in case of close-to-degenerate data matrix.To perform tests between different methods of least squares, two sets of data were collected. The first one comesfrom the TERIA network and includes data collected from different reference stations located throughout France.While the second one is the result of a measurement campaign using GPS (Ublox NL-6002U).Using the real data, a low condition number has been estimated: in this case all LS methods yield equivalentestimates: as a consequence, OLS and WLS are to be preferred for their low computational cost. However, theworst scenario has been investigated which may occur in case of a distant satellite, resulting in the ill-conditioning ofthe GPS problem. This extreme situation justifies the use of the TLS EXIN neural network. The results obtainedconfirm of this approach even for very large condition numbers.

This study examines the spatial impact various socio-demographic and housing factors might have in the foreclosure lis pendens rate within various Hillsborough County, Florida tracts as well as comparing those results with past research. Hopefully the techniques used in this study can be implemented elsewhere in order to better study the foreclosure crisis. The methods used within this research were chosen carefully in order to best understand what is being observed. One method is OLS regression which helps see the impact of each variable and if that impact has a negative or positive effect on the rate of foreclosure. Bivariate Maps were created to spatially examine each variable when compared to the foreclosure rate as well as Effect plots from regression in order to see how the true relationship of a variable affects the foreclosure rate.

SEB Merchant Banking provides to its institutional customers a true market neutral product called Dynamic Manager Alpha (DMA). The DMA is constructed by a long position in an exceptionally well performing mutual fund and a beta adjusted short position in an appropriate index. The key to making the product market neutral is adjusting with the correct beta, since the beta changes, it is very important to have a good model for predicting beta in the future. This master thesis begins with describing what beta is in a CAPM sense. It then continues with recognizing the so called “Two Beta Trap”, which separates two kinds of beta. The first is in CAPM sense, with a market portfolio represented by the whole market. The second is a “best fit” beta where the market portfolio is the index which explains as much as possible of the fund returns. It is this second way of calculating beta that is used in this thesis and therefore beta can be viewed upon like a hedgeratio. The purpose of this thesis is to predict the future beta for mutual funds with as high accuracy as possible. The starting point has been historic OLS (Ordinary Least Squares) estimation of beta. From earlier studies and own studies in this thesis a lot of different techniques for predicting beta has been tested. For example the eriodicity in the returns, the interval length, different regression methods as LAD (Least Absolute Deviation) and IRLS (Iteratively Reweighted Least Squares). Also different adjustments to beta have been tested for better catching the momentum in beta and general mean reverting tendencies. The results of the studies show that when possible, returns calculated with daily compounding is not favorable. For daily but especially weekly returns, LAD and IRLS are superior to OLS in predicting beta. Adjusting techniques have a positive effect in predicting beta, especially for weekly returns. Monthly returns seem to be most stable and have the smallest prediction errors, but with the right model and adjustment, betas with weekly returns have almost as good characteristics. Since the prediction model needs to have a fast response to market changes, returns calculated with short compounding is favorable. It is therefore very encouraging that the results from this thesis have showed great improvements in prediction of beta for returns calculated with weekly compounding.

<p>SEB Merchant Banking provides to its institutional customers a true market neutral product called Dynamic Manager Alpha (DMA). The DMA is constructed by a long position in an exceptionally well performing mutual fund and a beta adjusted short position in an appropriate index. The key to making the product market neutral is adjusting with the correct beta, since the beta changes, it is very important to have a good model for predicting beta in the future.</p><p>This master thesis begins with describing what beta is in a CAPM sense. It then continues with recognizing the so called “Two Beta Trap”, which separates two kinds of beta. The first is in CAPM sense, with a market portfolio represented by the whole market. The second is a “best fit” beta where the market portfolio is the index which explains as much as possible of the fund returns. It is this second way of calculating beta that is used in this thesis and therefore beta can be viewed upon like a hedgeratio.</p><p>The purpose of this thesis is to predict the future beta for mutual funds with as high accuracy as possible. The starting point has been historic OLS (Ordinary Least Squares) estimation of beta. From earlier studies and own studies in this thesis a lot of different techniques for predicting beta has been tested. For example the eriodicity in the returns, the interval length, different regression methods as LAD (Least Absolute Deviation) and IRLS (Iteratively Reweighted Least Squares). Also different adjustments to beta have been tested for better catching the momentum in beta and general mean reverting tendencies.</p><p>The results of the studies show that when possible, returns calculated with daily compounding is not favorable. For daily but especially weekly returns, LAD and IRLS are superior to OLS in predicting beta. Adjusting techniques have a positive effect in predicting beta, especially for weekly returns. Monthly returns seem to be most stable and have the smallest prediction errors, but with the right model and adjustment, betas with weekly returns have almost as good characteristics. Since the prediction model needs to have a fast response to market changes, returns calculated with short compounding is favorable. It is therefore very encouraging that the results from this thesis have showed great improvements in prediction of beta for returns calculated with weekly compounding.</p>

Denna uppsats undersöker sambandet mellan några makroekonomiska faktorer och de svenska storbankernas kreditförluster.  Att hitta indikatorer som kan ge tidiga signaler om kommande bankproblem är av stor vikt inte bara för banker utan för hela samhället. Anledningen är att till skillnad från de flesta andra branscher kan problem i banker störa samhällsviktiga funktioner. De kan få globala spridningseffekter och miljoner arbetstillfällen kan snabbt vara i fara när exempelvis betalningsfunktioner slutar fungera. Uppsatsen fokuserar på den verksamhet som bankerna har i Sverige. Bankernas kvartalsrapporter används som underlag och tidsperioden som ingår i uppsatsen är 2004 till 2014. Det betyder att konsekvenserna av den bankkris som startade i USA 2008 och som fick globala följdeffekter syns i underlagen för uppsatsen. Arbetslöshet, BNP, hushållens förtroende indikator, konsumentpris index och reporäntan är de makroekonomiska faktorer som används i uppsatsen. Underlagen för faktorerna är hämtade från SCB och Riksbanken. Den empiriska analys som används för att statistiskt bearbeta underlagen är regressionsmodellen OLS, minsta kvadrat metod.  Med hjälp av denna metod kan man fastlägga att det finns ett statistiskt signifikant samband mellan makroekonomiska faktorer och kreditförluster hos de svenska storbankerna.  I synnerhet kan man peka på ett negativt samband mellan BNP och kreditförluster. Dessutom kan man peka på positivt samband mellan arbetslöshet och kreditförluster.

When the Fair Housing Act of 1968 was passed 47 years ago, the United States was in the midst of the civil rights movement and fair housing was identified as a pillar of equality. While, progress has been made, there is much work that needs to be done in order to achieve integration. As a country, the United States is a highly segregated country. It is important to understand the factors that contribute to this and it is important to understand the relationships that exists between them in order to attempt to solve the problem. While the legal barriers to integration have been lifted choices continue to be limited to families of color that lack the resources to live in desirable neighborhoods. The ultimate goal of this study is to examine the relationship between the impact of individual indicators and housing patterns in the greater Tucson/Pima county region. An affordability index, the DEYA index, was created to determine where affordability is at its highest. The index includes different weights for foreclosure, Pima County spending on affordable housing, the existence of Pima County general obligations bond affordable housing projects, land value and inclusion in the community land trust. Once this was determined a regression analysis was used to determine the relationship between affordability and individual factors that may be affecting integration. The indicators used were broken down into 3 categories: the categories were education, housing and neighborhoods and employment and economic health.

Tidigare studier visar att ökad handel spelar en tydlig roll för ett lands ekonomiska tillväxt. Genom att träda in på den globala marknaden öppnas många möjligheter för ökad handel och nya arbetstillfällen. Utrikeshandeln är betydelsefull för små öppna ekonomier som till exempel Kenya och bör utgöra en stor del av landets BNP. I och med detta är det viktigt att studera vilka faktorer som påverkar ett lands utrikeshandel. Syftet med uppsatsen är att undersöka vilka faktorer som påverkar Kenyas export. Analysen visar att handelspartnernas BNP har en betydande påverkan på Kenyas export. Det geografiska avståndet har en negativ påverkan på Kenyas utrikeshandel. De regionala handelsavtalen har som förväntat en positiv påverkan på exporten.

Hirš, P. Determinants of house prices in and around Brno. Diploma thesis. Brno: Mendel University, 2015. The aim of this thesis theoretically and empirically analyzes determinants which substantially affect the price of apartments in and around Brno. For the analysis of the prices of apartments are used selling prices of apartments from a database of a real estate. Data are processed by using the method of least squares (OLS) through program Gretl. The work also describes the current state of the property market and its functioning, including familiarization with the works of other authors. In conclusion, the results of the analysis of selling prices of apartments are evaluated including a comparison with existing works dealing with this topic.

Background: With the first green bond being issued in 2008 as a joint venture between World Bank Organization and the Swedish bank SEB the financial instrument has made an impact on the financial markets. With a high demand for sustainable investments in Sweden partly due to policies a premium for the green bonds is to be expected at least according to theory. The real estate market has adapted to the increased demand for green investments by moving more towards green bonds, and rightfully so as it is one of the largest polluters seen by sector. In result, it is also one of the largest issuers of bonds which creates an excellent opportunity to research the industry as there is plenty of data available. Purpose: This report will examine the premium of green bonds in the Swedish real estate market. Furthermore, it will also examine the effects of Covid-19 and to what extent this pandemic had an impact on green bonds. Method: The thesis examines the Option Adjusted Spread (OAS) of 166 bonds of 9 different companies from the start of 2016 to December 2020 within the Swedish real estate market. Control variables such as Company risk, Market risk and Macroeconomic variables were used in an OLS regression to estimate the premium. The effect of the Covid-19 pandemic was also examined. Conclusion: After analyzing 53 green and 113 conventional bonds no significant results were found on how premium differs between green and conventional bonds. However more general findings were found that suggest bonds become more sought during the Covid-19. It was further found that the green bond market is rapidly growing and may in a few years be in a better position to be examined.