EMU as the only functioning single currency area has been criticised as a non-optimal currency area since the Treaty on European Union was signed. Despite this, it has been seen as, probably, the most complete economic project that has ever been conducted by any group of governments. Through Dynamic Factor model and Panel VAR method, we are focusing on the issues of business cycle synchronisation, effectiveness of ECB monetary policy and the euro fiscal dividend, thus to advances the current studies on EMU through assessing whether it can be a sustainable system. For example, whether economic fluctuations can be effectively managed by implementing a single ECB monetary policy and financial market can be relied upon as a monitoring and enforcing device to discipline fiscal behaviour of Eurozone countries. Overall, we concluded that EMU could be more sustainable if it was just formed by its core members, leaving the periphery outside the single currency area. However, since the EU has recently conducted many rescue measures to save the Eurozone, we are unlikely to see those troubled countries to quit EMU, at least, at the present time. The sustainability of the current EMU can be improved if more intra-trade can be promoted to enhance business cycle convergence; hence, it will be more likely to have a union-wide appropriate monetary policy. This will also reduce the requirement of depending upon using fiscal measures to compensate the loss of monetary sovereignty. Moreover, fiscal activities can also be better monitored/enforced since the financial market has begun to adequately adjust the long-term interest rates on Eurozone government bonds according to the development in those countries fiscal stance.

In this thesis, we use the dynamic factor model in state space, proposed by Stock and Watson (1989), to estimate the fluctuations of common factor by using lots of macroeconomic variables. Besides, with the combination of two stage dynamic factor analysis model which is proposed by Aruba et. al (2010), we want to discuss the possibility for the correlation of economic fluctuations across countries to change with different time periods. The thesis verifies the following three conclusions: First, the correlations of the economic fluctuations across countries are significant due to the regional economics. Second, the global or regional common shocks will increase the correlations of the economic fluctuations across countries. Finally, developed countries and emerging countries response differently during the Financial Tsunami from 2008 to 2009.

Submitted by Guilherme Branco Gomes (guilherme.branco.gomes@gmail.com) on 2018-04-17T00:19:25Z No. of bitstreams: 1 dissertacao Guilherme Branco Gomes versao final.pdf: 2137139 bytes, checksum: cead1d1fa55323ea0f81e275c713796e (MD5) / Approved for entry into archive by GILSON ROCHA MIRANDA (gilson.miranda@fgv.br) on 2018-04-18T19:53:58Z (GMT) No. of bitstreams: 1 dissertacao Guilherme Branco Gomes versao final.pdf: 2137139 bytes, checksum: cead1d1fa55323ea0f81e275c713796e (MD5) / Made available in DSpace on 2018-05-08T17:43:40Z (GMT). No. of bitstreams: 1 dissertacao Guilherme Branco Gomes versao final.pdf: 2137139 bytes, checksum: cead1d1fa55323ea0f81e275c713796e (MD5) Previous issue date: 2018-03-19 / This work compares dynamic factor model’s forecasts for Brazilian GDP. Our approach takes into account mixed frequencies and can handle missing data. We implement three models: the first is based on the Principal Components Analysis methodology; the second employs a two-step estimation method with quarterly inputs; the last is similar to the former but uses monthly series. A real-time out-of-sample exercise is proposed to assess the performance of these models. A dataset is created for each day within 27 quarters - from the fourth quarter of 2010 up to the second quarter of 2017. For recent periods, the nowcasts estimated by both two-step procedures perform better than the average predictions of Focus Survey, a bulletin organized by the Brazilian Central Bank. We also show evidence that the average of GDP forecasts from this survey may be biased / Esse trabalho compara previsões para o PIB brasileiro utilizando modelos de fatores dinâmicos. Nossa abordagem leva em consideração frequências mistas e lida com dados incompletos na base (missing data). Nós implementamos três modelos: o primeiro é baseado na metodologia de componentes principais; o segundo emprega uma estimação por dois estágio com variáveis trimestrais; o último é similar ao anterior mas utiliza series mensais. Um exercício em tempo real, fora da amostra, é proposto para comparar o desempenho desses modelos. Uma base de dados é criada para cada dia dentro de 27 trimestres - do quarto trimestre de 2010 até o segundo de 2017. Para períodos recentes, os nowcasts estimados para ambos os procedimentos de dois estágios se mostram melhores do que a média de previsão da pesquisa Focus, um boletim organizado pelo Banco Central do Brasil. Nós também mostramos evidências que a média das previsões do PIB dessa pesquisa pode ser viesada

EMU as the only functioning single currency area has been criticised as a non-optimal currency area since the Treaty on European Union was signed. Despite this, it has been seen as, probably, the most complete economic project that has ever been conducted by any group of governments. Through Dynamic Factor model and Panel VAR method, we are focusing on the issues of business cycle synchronisation, effectiveness of ECB monetary policy and the euro fiscal dividend, thus to advances the current studies on EMU through assessing whether it can be a sustainable system. For example, whether economic fluctuations can be effectively managed by implementing a single ECB monetary policy and financial market can be relied upon as a monitoring and enforcing device to discipline fiscal behaviour of Eurozone countries. Overall, we concluded that EMU could be more sustainable if it was just formed by its core members, leaving the periphery outside the single currency area. However, since the EU has recently conducted many rescue measures to save the Eurozone, we are unlikely to see those troubled countries to quit EMU, at least, at the present time. The sustainability of the current EMU can be improved if more intra-trade can be promoted to enhance business cycle convergence; hence, it will be more likely to have a union-wide appropriate monetary policy. This will also reduce the requirement of depending upon using fiscal measures to compensate the loss of monetary sovereignty. Moreover, fiscal activities can also be better monitored/enforced since the financial market has begun to adequately adjust the long-term interest rates on Eurozone government bonds according to the development in those countries fiscal stance.

Most macroeconomic activity series such as Swedish GDP growth are collected quarterly while an important proportion of time series are recorded at a higher frequency. Thus, policy and business decision makers are often confront with the problems of forecasting and assessing current business and economy state via incomplete statistical data due to publication lags. In this paper, we survey a few general methods and examine different models for mixed frequency issues. We mainly compare mixed data sampling regression (MIDAS) and state space dynamic factor model (SS-DFM) by the comparison experiments forecasting Swedish GDP growth with various economic indicators. We find that single-indicator MIDAS is a wise choice when the explanatory variable is coincident with the target series; that an AR term enables MIDAS more promising since it considers autoregressive behaviour of the target series and makes the dynamic construction more flexible; that SS-DFM and M-MIDAS are the most outstanding models and M-MIDAS dominates undoubtedly at short horizons up to 6 months, whereas SS-DFM is more reliable at long predictive horizons. And finally we conclude that there is no perfect winner because each model can dominate in a special situation.

The thesis consists of four papers that address likelihood-based unit root tests for panel data with cross-sectional dependence arising from common factors. In the first three papers, we derive Lagrange multiplier (LM)-type tests for common and idiosyncratic unit roots in the exact factor models based on the likelihood function of the differenced data. Also derived are the asymptotic distributions of these test statistics. The finite sample properties of these tests are compared by simulation with other commonly used unit root tests. The results show that our LM-type tests have better size and local power properties. In the fourth paper, we estimate the spaces spanned by the common factors and the spaces spanned by the idiosyncratic components of the static factor model by using the quasi-maximum likelihood (ML) method and compare it with the widely used method of principal components (PC). Next, by simulation, we compare the size and power properties of established tests for idiosyncratic unit roots, using both the ML and PC methods. Simulation results show that the idiosyncratic unit root tests based on the likelihood-based residuals generally have better size and higher size-adjusted power, especially when the cross-sectional dimension is small and the time series dimension is large.

Market risk is the risk of capital loss due to unexpected changes in market prices. One risk measure used to estimate market risk is Value at Risk (VaR). The common historical simulation methodology of VaR forecasting usually does not capture the time-varying volatilities associated with financial data. Therefore, dynamic factor models (DFM) are employed to improve VaR forecasting. The paper’s main focus is to use different volatility model specifications in the DFM to evaluate which is the most appropriate for VaR forecasting. The volatility models considered are the Constant Conditional Correlation (CCC-) GARCH, the Dynamic Conditional Correlation (DCC-) GARCH, and the corrected Dynamic Conditional Correlation (cDCC-) GARCH. The method is applied to an empirical dataset consisting of Swedish large-cap stocks between 2017-2021 where two different portfolios are used, the equally- and the value-weighted portfolio. The data purposefully includes the COVID-19 pandemic such that the models can be compared during less- and more volatile periods. The method is further evaluated in a simulation study where randomized portfolio weights are used. It is found that the VaR forecasts produced by the three different model specifications are similar throughout the entire sample. Therefore the most restricted volatility model (CCC-GARCH) is recommended.

The gross domestic product (GDP) is an essential measure of the state of economic activity and serves as a crucial tool for policymakers, investors, or businesses. However, the official GDP estimate in the Czech Republic is only available with a lag of approximately 60 days, and the Czech National Bank (CNB) announces its GDP forecast once in each quarter. This thesis focuses on predicting GDP growth in the current quarter, referred to as nowcasting. I employ several methods to nowcast the real GDP growth in the Czech Republic in a pseudo-real-time setting and compare their performance. Additionally, I investigate the possibility of creating an ensemble model by using a weighted average of several nowcasting models. The results suggest that the Dynamic Factor Model (DFM) performs best in the GDP nowcasting task, and its predictive accuracy is comparable with the official CNB nowcast. Furthermore, the model averaging process yields accuracy close to the best individual model while addressing model uncertainty. The GDP nowcast of the DFM will be made available to the public in real-time on a website and updated with a daily frequency.

Regarding quantifying uncertainties in prediction, two projects with different perspectives and application backgrounds are presented in this dissertation. The goal of the first project is to predict the corporate default risks based on large-scale time-to-event and covariate data in the context of controlling credit risks. Specifically, we propose a competing risks model to incorporate exits of companies due to default and other reasons. Because of the stochastic and dynamic nature of the corporate risks, we incorporate both company-level and market-level covariate processes into the event intensities. We propose a parsimonious Markovian time series model and a dynamic factor model (DFM) to efficiently capture the mean and correlation structure of the high-dimensional covariate dynamics. For estimating parameters in the DFM, we derive an expectation maximization (EM) algorithm in explicit forms under necessary constraints. For multi-period default risks, we consider both the corporate-level and the market-level predictions. We also develop prediction interval (PI) procedures that synthetically take uncertainties in the future observation, parameter estimation, and the future covariate processes into account. In the second project, to quantify the uncertainties in the maximum likelihood (ML) estimators and compute the exact tolerance interval (TI) factors regarding the nominal confidence level, we propose algorithms for two-sided control-the-center and control-both-tails TI for complete or Type II censored data following the (log)-location-scale family of distributions. Our approaches are based on pivotal properties of ML estimators of parameters for the (log)-location-scale family and utilize the Monte-Carlo simulations. While for Type I censored data, only approximate pivotal quantities exist. An adjusted procedure is developed to compute the approximate factors. The observed CP is shown to be asymptotically accurate by our simulation study. Our proposed methods are illustrated using real-data examples. / Ph. D.

De manière générale, la qualité d’un circuit de distribution peut-elle influencer le capital d’une marque ?C’est la principale question que nous traitons dans cette recherche. De nombreuses recherches ont étépubliées sur les déterminants du capital marque. Cependant, peu de choses ont été dites sur le rôle de ladistribution. De plus, une grande partie des recherches ont considéré l’image du point de vente comme unconcept global ou unidimensionnel. Or, la majorité des recherches affirme que l’image du point de vente estun construit multidimensionnel.Le but de cette recherche est d’étudier la relation entre la qualité de la distribution et le capital de la marquequi y est référencée. Nous développons un modèle qui met en relation les dimensions du magasin (l’imageprix, la variété de l’assortiment, la qualité de MDD, la qualité des produits, la qualité de service etl’accessibilité du magasin) et le capital marque, mesuré à l’aide des constantes qui, sont considéréescomme la mesure de l’utilité incrémentale de la marque. Nous utilisons les variables de contrôle telles que lacatégorie de produits. Nous élaborons un modèle factoriel dynamique en utilisant les données de panel sur4500 ménages, 12 magasins appartenant à de chaines différentes en France sur une période de cinq ans etdemi (2004-2009). Les résultats montrent que les effets de l’image du magasin sur le capital marque varientselon l’enseigne, le format de magasin, les catégories de produits, les marques et les caractéristiques desconsommateurs.D’un point de vue théorique, cette recherche permet d’identifier les dimensions les plus pertinentes del’image d’un point de vente ainsi que leurs conditions d’efficacité. D’un point de vue méthodologique, nousutilisons un modèle factoriel dynamique qui n’a pas encore été utilisé sur la mesure du capital marque. D’unpoint de vue managérial, cette recherche permettra aux responsables de marques de mieux apprécierl’influence d’un magasin sur la valeur de leurs marques. / Does a store format quality can generally influence brand equity? This is the main question we address inthis research. Numerous studies have been published on brand equity drivers. However, little has been saidabout the role of distribution. In addition, much research has conceptualized store image as a global or onedimensionalconcept. However, according to the research majority, store image is a multidimensionalconstruct.The purpose of this research is to investigate the relationship between distribution quality and brand equity.We develop a model that connects store image dimensions (price image, assortment variety, private labelquality, product quality, service quality, and location) and brand equity, measured thanks to the interceptswhich are considered as a brand incremental utility measure. The model controls for the variables such asthe product category. We adopt a dynamic factor model using panel data on 4500 households, 12 storesbelonging to different chains in France over a period of five years and a half (2004-2009). The results showthat store image effects on the brand equity depend on the store name, store format, product categories,brands and consumer characteristics.From a theoretical perspective, this research identifies the most relevant store image dimensions as well astheir efficiency conditions. From a methodological point of view, we use a dynamic factor model that has notyet been used on brand equity measurement. From a managerial standpoint, this research may help brandmanagers to better assess the store impact on their brands value.