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Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and Management ; During the last few elections that were held in Portugal, there have been very low percentages of voter turnout. This will obviously impact the result of those elections and can maybe be related to the general disenchantment of the population regarding the country's recent political environment. This study aims to contribute to a better understanding of the patterns in the abstention rate of the last elections in Portugal. Sociological and economic variables such as age, unemployment rate, education level and many others will be used in trying to find out if they influence the abstention rate. It is logical to assume that the abstention rate in a certain municipality will be related to the abstention in neighboring municipalities. Therefore, the study also investigates if there is spatial autocorrelation in the abstention rates. Modeling a phenomenon like this with a simple linear regression model, estimated by Ordinary Least Squares (OLS), will render less efficient and biased results because of the spatial correlation of the observations and possible spatial clustering of values. Spatial regression methods have been proposed to overcome these drawbacks, particularly the Geographically Weighted Regression (GWR). This method will take into account possible local influences, allowing the coefficients of the model to vary depending on the geographic location, possibly obtaining a more appropriate fit. Many different OLS and GWR models were investigated by considering different combinations of explanatory variables and diagnosing their results through statistical tests and goodness-of-fit measures. Results show that indeed the data exhibits a non-random spatial pattern, and that a GWR model is a better approach in modeling abstention rates, when compared to an OLS model. Hence, the percentage of voter turnout in a municipality is likely to be better modelled taking into account its geographic location.

Introduction -- Exploratory spatial data analysis -- Models dealing with spatial dependence -- Advanced models dealing with spatial dependence -- Models dealing with spatial heterogeneity -- Models dealing with both spatial dependence and spatial heterogeneity -- Advanced spatial regression models -- Practical considerations for spatial data analysis.

The article analyses the employment characteristics. The employment rate was studied in selected regions of Europe, and subsequently, for selected variables: total population employed, women employed and men employed, classic econometric models were constructed and the necessity of including the spatial factor in the process of modelling was verified. The demographic variables and GDP per capita were chosen as explaining variables of the model. It was analysed whether including a spatial approach in the models would improve their quality. Two basic spatial models were taken into consideration: the spatial error model and the spatial lag model, the former of which turned out to be the right tool for the analyses.

This paper applies a spatial economic regression model to analyze the relation between deforestation in the period from 1989 to 1994 and access to roads and markets, ecological conditions, land tenure, and zoning policies in Santa Cruz, Bolivia. The data come from a Geographic Information System (GIS) data base compiled by the Natural Resources Department of the Santa Cruz Government. Locations closer to roads and the City of Santa Cruz and that have more fertile soils have a greater probability of being deforested. The same applies to colonization areas. National parks and forest concessions seem to protect forests. Areas with rainfall levels optimal for soybeans have higher deforestation than drier or wetter areas.

Spatial econometric models become increasingly popular in various subfields of political science. However, the necessity to specify the underlying network of dependencies, denoted by $\boldsymbol{W}$, prior to estimation is a prevalent source of criticism since the true dependence structure is rarely known and theories mostly provide insufficient guidance. The present study investigates the effects of thisnetwork uncertaintywhich is a special case of model uncertainty that arises from uncertainty about the correct specification of $\boldsymbol{W}$. It advocates Bayesian model averaging (BMA) as a superior approach to this problem, located at the intersection of theory and empirics. Conducting Monte Carlo experiments, I demonstrate that, while the effect estimates are robust toward a misspecification in the functional form of $\boldsymbol{W}$, uncertainty in the neighborhood definition can bias the effect estimates derived from spatial autoregressive models. In contrast to alternative techniques, BMA directly addresses network uncertainty, correctly identifies the true network structure in the set of feasible alternatives, and provides unbiased effect estimates. Two replication studies from different subfields of the discipline illustrate the benefits of this approach for applied research.

The extended Hodrick-Prescott (HP) method was developed by Polasek (2011) for a class of data smoother based on second order smoothness. This paper develops a new extended HP smoothing model that can be applied for spatial smoothing problems. In Bayesian smoothing we need a linear regression model with a strong prior based on differencing matrices for the smoothness parameter and a weak prior for the regression part. We define a Bayesian spatial smoothing model with neighbors for each observation and we define a smoothness prior similar to the HP filter in time series. This opens a new approach to model-based smoothers for time series and spatial models based on MCMC. We apply it to the NUTS-2 regions of the European Union for regional GDP and GDP per capita, where the fixed effects are removed by an extended HP smoothing model.

Dissertation presented as partial requirement for obtaining the Master's degree in Statistics and Information Management, with a specialization in Information Analysis and Management ; Sharing economy market, such as Uber and Airbnb, have been growing rapidly in the last few years, providing extra income to agents from the supply side, and low costs to those in demand side. Although its adoption provided benefits for stakeholders and to the global economy of the areas in which they are inserted, several authors and politicians have been referencing the negative externalities brought with it, such as an increase in rents and real estate prices and a decrease in hotels' revenue. However, most of the externalities pointed out, were not based on any empirical analysis. The aim of this study is to analyze Airbnb market within Lisbon municipality, focusing mainly the modelling spatial variation of Airbnb listings' price. For this purpose, it was employed an ordinary least square (OLS) model and a geographical weighted regression (GWR) model to identify the main factors affecting the Airbnb listings' price. The results showed that the GWR model performs better than the OLS model, and it allows assessing the local impact of the explanatory variables on the Airbnb listings' price. In conclusion, it was found that the price of the two types of Airbnb listings (entire home/apartments and private/shared rooms) are not affected by the same factors and that statistically significant differences varied across parishes within Lisbon municipality. Perhaps, there is a need to test if it is plausible to apply a unique regulatory policy considering Airbnb and Lisbon market as an aggregated concept or by Airbnb listing type and Lisbon parishes.