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.

O objetivo deste trabalho é a construção de um indicador coincidente (IC) para a atividade econômica do segmento de Comércio Varejista dentro da economia do estado do Rio Grande do Sul. A utilização de variáveis que apresentem comportamento comum entre si e altamente correlacionado com as Vendas do Varejo é o ponto de partida para a investigação a seguir. A metodologia adotada é a dos modelos de Fator Dinâmico de Stock e Watson (1988, 1989, 1991, 1993). Os resultados encontrados apontam um indicador final satisfatório, em termos de MAPE (erro absoluto percentual médio, em inglês) com relação às séries de Vendas do Varejo, especificamente a série da FEE/Fecomércio-RS – que tratava-se de uma pesquisa censitária. Em termos de fundamentos econômicos o IC é composto por variáveis que sabidamente afetam o consumo de bens: renda, crédito e confiança do consumidor. Um destaque está para o alto peso das variáveis de Crédito e Sentimento sobre a Situação Presente, o que sugere que o fluxo de renda futuro e a confiança para assumir endividamentos longos são determinantes para o segmento. Considerando um horizonte maior para a ampliação dessa pesquisa, postula-se que a construção de novos dados regionalizados para crédito, renda pela PNAD Contínua, séries mais longas para índices de confiança, e indicadores sobre estoques no Comércio podem melhorar os resultados encontrados. / The following research intends to build a coincident indicator to the Retail sector’s economic activity within the regional economy of Rio Grande do Sul. The starting point to reach this objective is to use variables which present common cyclical behavior with each other, as well as with Retail Trade indices. The investigation will be carried on using Stock and Watson’s (1988, 1989, 1991, 1993) Dynamic Factor models methodology. The research returned a result a final index which can be classified as satisfactory, when evaluated by the mean absolute percentage error with Retail Trade indices, specially the FEE/Fecomércio-RS’ index – which was a censitary research. The built Coincident Indicator is composed by variables correlated with consumption, according to the economic theory: income, credit and consumer’s confidence. Variables such as Consumer Credit and Consumer’s Sentiment towards Present Economic Situation presented high weight in the indicator, which suggests that future income flows and confidence to take long term debt are crucial for the sector’s economic activity. Considering an expanded horizon for this research, one believes that new regional data for Household Credit, income through PNAD Contínua, a longer sample for Consumer’s Confidence indices, and indicators measuring stock levels in Retail might improve the coincident indicator.

O objetivo deste trabalho é a construção de um indicador coincidente (IC) para a atividade econômica do segmento de Comércio Varejista dentro da economia do estado do Rio Grande do Sul. A utilização de variáveis que apresentem comportamento comum entre si e altamente correlacionado com as Vendas do Varejo é o ponto de partida para a investigação a seguir. A metodologia adotada é a dos modelos de Fator Dinâmico de Stock e Watson (1988, 1989, 1991, 1993). Os resultados encontrados apontam um indicador final satisfatório, em termos de MAPE (erro absoluto percentual médio, em inglês) com relação às séries de Vendas do Varejo, especificamente a série da FEE/Fecomércio-RS – que tratava-se de uma pesquisa censitária. Em termos de fundamentos econômicos o IC é composto por variáveis que sabidamente afetam o consumo de bens: renda, crédito e confiança do consumidor. Um destaque está para o alto peso das variáveis de Crédito e Sentimento sobre a Situação Presente, o que sugere que o fluxo de renda futuro e a confiança para assumir endividamentos longos são determinantes para o segmento. Considerando um horizonte maior para a ampliação dessa pesquisa, postula-se que a construção de novos dados regionalizados para crédito, renda pela PNAD Contínua, séries mais longas para índices de confiança, e indicadores sobre estoques no Comércio podem melhorar os resultados encontrados. / The following research intends to build a coincident indicator to the Retail sector’s economic activity within the regional economy of Rio Grande do Sul. The starting point to reach this objective is to use variables which present common cyclical behavior with each other, as well as with Retail Trade indices. The investigation will be carried on using Stock and Watson’s (1988, 1989, 1991, 1993) Dynamic Factor models methodology. The research returned a result a final index which can be classified as satisfactory, when evaluated by the mean absolute percentage error with Retail Trade indices, specially the FEE/Fecomércio-RS’ index – which was a censitary research. The built Coincident Indicator is composed by variables correlated with consumption, according to the economic theory: income, credit and consumer’s confidence. Variables such as Consumer Credit and Consumer’s Sentiment towards Present Economic Situation presented high weight in the indicator, which suggests that future income flows and confidence to take long term debt are crucial for the sector’s economic activity. Considering an expanded horizon for this research, one believes that new regional data for Household Credit, income through PNAD Contínua, a longer sample for Consumer’s Confidence indices, and indicators measuring stock levels in Retail might improve the coincident indicator.

O objetivo deste trabalho é a construção de um indicador coincidente (IC) para a atividade econômica do segmento de Comércio Varejista dentro da economia do estado do Rio Grande do Sul. A utilização de variáveis que apresentem comportamento comum entre si e altamente correlacionado com as Vendas do Varejo é o ponto de partida para a investigação a seguir. A metodologia adotada é a dos modelos de Fator Dinâmico de Stock e Watson (1988, 1989, 1991, 1993). Os resultados encontrados apontam um indicador final satisfatório, em termos de MAPE (erro absoluto percentual médio, em inglês) com relação às séries de Vendas do Varejo, especificamente a série da FEE/Fecomércio-RS – que tratava-se de uma pesquisa censitária. Em termos de fundamentos econômicos o IC é composto por variáveis que sabidamente afetam o consumo de bens: renda, crédito e confiança do consumidor. Um destaque está para o alto peso das variáveis de Crédito e Sentimento sobre a Situação Presente, o que sugere que o fluxo de renda futuro e a confiança para assumir endividamentos longos são determinantes para o segmento. Considerando um horizonte maior para a ampliação dessa pesquisa, postula-se que a construção de novos dados regionalizados para crédito, renda pela PNAD Contínua, séries mais longas para índices de confiança, e indicadores sobre estoques no Comércio podem melhorar os resultados encontrados. / The following research intends to build a coincident indicator to the Retail sector’s economic activity within the regional economy of Rio Grande do Sul. The starting point to reach this objective is to use variables which present common cyclical behavior with each other, as well as with Retail Trade indices. The investigation will be carried on using Stock and Watson’s (1988, 1989, 1991, 1993) Dynamic Factor models methodology. The research returned a result a final index which can be classified as satisfactory, when evaluated by the mean absolute percentage error with Retail Trade indices, specially the FEE/Fecomércio-RS’ index – which was a censitary research. The built Coincident Indicator is composed by variables correlated with consumption, according to the economic theory: income, credit and consumer’s confidence. Variables such as Consumer Credit and Consumer’s Sentiment towards Present Economic Situation presented high weight in the indicator, which suggests that future income flows and confidence to take long term debt are crucial for the sector’s economic activity. Considering an expanded horizon for this research, one believes that new regional data for Household Credit, income through PNAD Contínua, a longer sample for Consumer’s Confidence indices, and indicators measuring stock levels in Retail might improve the coincident indicator.

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.

No description available.

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.

In the first essay, I study the power of predictive regressions in a world of forecastable returns and find it to be quite poor. Using a simple model, I investigate the properties of short- and long-horizon regressions. The mechanisms biasing coefficients in short-horizon regressions differ from those affecting longer horizons. Further, I demonstrate that R\(^2s\) are biased and give an estimable bias correction. A calibration exercise shows sample lengths will be insufficient to determine what predicts asset returns until beyond the year 2100. The problem is not isolated to highly persistent predictors; even modestly persistent predictors have difficulties. Further, long-horizon regressions have inferior power relative to their single-period counterparts. These results present a predicament. If return predictability exists, then our ability to identify its source using predictive regressions alone is exceedingly poor. The second essay, written with James Stock and Mark Watson, considers the estimation of approximate dynamic factor models when there is temporal instability in the factors, factor loadings, and errors. We demonstrate that estimators for the factors and for the number of those factors are consistent for their population values even when affected by these instabilities. Further, we characterize the inferential theory in our framework for the estimated factors and for diffusion index forecasts and factor-augmented vector autoregressions that make use of the estimated factors. These results illustrate the broad robustness factor models have against temporal instability. In the third essay, co-author Peter Tufano and I consider the complex accounting rules, explicit fund sponsor supports, and government actions, that grant US money market mutual fund investors an implicit put option allowing them to redeem their shares at a fixed price of $1.00, regardless of the portfolio's market value. We describe the institutional features that generate these options, identify their writers, and estimate their premia. Using a hypothetical MMMF, we find that currently, non-redeeming shareholders, fund sponsors, and the government collectively bear annual premia of 22 to 44 basis points to give MMMF shareholders the right to redeem their shares at $1.00 rather than at the market value of the fund portfolio. These premia rose dramatically during the financial crisis, with the put value potentially being over 50 basis points.

Submitted by Natasha Lewin (natgaertner@hotmail.com) on 2014-05-21T13:47:19Z No. of bitstreams: 1 Dissertação_Natasha_Gaertner.pdf: 1016109 bytes, checksum: 3be3ae578302aaf3f6975eea7891ef1a (MD5) / Approved for entry into archive by Vitor Souza (vitor.souza@fgv.br) on 2014-05-28T20:14:42Z (GMT) No. of bitstreams: 1 Dissertação_Natasha_Gaertner.pdf: 1016109 bytes, checksum: 3be3ae578302aaf3f6975eea7891ef1a (MD5) / Made available in DSpace on 2014-06-02T20:29:12Z (GMT). No. of bitstreams: 1 Dissertação_Natasha_Gaertner.pdf: 1016109 bytes, checksum: 3be3ae578302aaf3f6975eea7891ef1a (MD5) Previous issue date: 2013-11-27 / Este trabalho analisa a importância dos fatores comuns na evolução recente dos preços dos metais no período entre 1995 e 2013. Para isso, estimam-se modelos cointegrados de VAR e também um modelo de fator dinâmico bayesiano. Dado o efeito da financeirização das commodities, DFM pode capturar efeitos dinâmicos comuns a todas as commodities. Além disso, os dados em painel são aplicados para usar toda a heterogeneidade entre as commodities durante o período de análise. Nossos resultados mostram que a taxa de juros, taxa efetiva do dólar americano e também os dados de consumo têm efeito permanente nos preços das commodities. Observa-se ainda a existência de um fator dinâmico comum significativo para a maioria dos preços das commodities metálicas, que tornou-se recentemente mais importante na evolução dos preços das commodities. / This study analyses the importance of common factors in metal prices movements for the period 1995-2013. For this purpose, cointegrated VAR models and also a Bayesian dynamic factor model are estimated. Given the effect of the financialization of commodities, DFM can capture dynamic effects common to all commodities. Furthermore, panel data is applied in order to use all heterogeneity between commodities over the period. Our estimation results show that interest rate, US dollar effective rate and also consumption data have permanent effect in the commodity prices. Also, there exists one common significant dynamic factor for most metal commodity prices and that this common factor has recently become increasingly important in driving commodity prices.