Partial Least Squares is both a regression method and a tool for variable selection, that is especially appropriate for models based on numerous (possibly correlated) variables. While being a well established modeling tool in chemometrics, this thesis adapts PLS to financial data to predict the movements of the business cycle represented by the OECD Composite Leading Indicators. High-dimensional data is used, and a model with automated variable selection through a genetic algorithm is developed to forecast different economic regions with good results in out-of-sample tests. / Partial Least Squares är både en regressionsmetod och ett verktyg för variabelselektion som är specielltlämpligt för modeller baserade på en stor mängd (möjligtvis korrelerade) variabler.Medan det är en väletablerad modelleringsmetod inom kemimetri, anpassar den häruppsatsen PLS till finansiell data för att förutspå rörelserna av konjunkturen,representerad av OECD's Composite Leading Indicator. Högdimensionella dataanvänds och en model med automatiserad variabelselektion via en genetiskalgoritm utvecklas för att göra en prognos av olika ekonomiska regioner medgoda resultat i out-of-sample-tester

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Submitted by Maicon Juliano Schmidt (maicons) on 2015-07-06T14:25:22Z No. of bitstreams: 1 Gustavo Jose Schuck.pdf: 1176134 bytes, checksum: 0a06b537ba55c68822649108c9e9d315 (MD5) / Made available in DSpace on 2015-07-06T14:25:23Z (GMT). No. of bitstreams: 1 Gustavo Jose Schuck.pdf: 1176134 bytes, checksum: 0a06b537ba55c68822649108c9e9d315 (MD5) Previous issue date: 2012-07-26 / Nenhuma / O interesse e, especialmente, a necessidade da atual economia global em entender o presente e antecipar o futuro, mesmo que no curto prazo, torna o estudo da previsão cíclica e, consequentemente, dos indicadores antecedentes de extrema importância. Dessa forma, este trabalho tem como objetivo a criação de três indicadores antecedentes compostos para a Produção Física da Agroindústria no Brasil, com horizonte de previsão entre 2 e 4 meses, 5 a 8 meses e 9 a 12 meses, respectivamente nomeados de Curto, Médio e Longo prazo. Para tanto, foi feito um levantamento do estado atual da arte, principalmente da produzida para o Brasil. Introdutoriamente, é apresentado o conceito de ciclo e indicadores antecedentes, como a justificativa e importância desse tema. Então, é feito um levantamento da literatura sobre ciclos, abordando publicações seminais, como Burns e Mitchell (1946), e a atual discussão entre ciclos econômicos e ciclos de crescimento. Após, abordo o conceito de indicadores antecedentes, sua origem, principais métodos utilizados e trabalhos atuais sobre o tema. Por fim, é construída uma metodologia, baseada no modelo proposto em OECD (2008) com adição de modelos VAR, Causalidade de Granger e Probit, sendo testada e avaliada para as informações mensais da Produção Física da Agroindústria no Brasil e outras 421 séries candidatas a antecedentes, no período entre janeiro de 1995 e dezembro de 2011. Conclui-se positivamente no que se refere à possibilidade de criação de indicadores antecedentes compostos, seja de curto, médio ou longo prazo, para Agroindústria brasileira. / The interest and especially the need of today's global economy to understand the present and anticipate the future, even in the short term, makes the study of cyclical forecasting and the leading indicators of extreme importance. Thus, this study aims to create three composite leading indicators for GDP of the Brazilian Agribusiness, with the forecast horizon between 2-4 months, 5-8 months and 9-12 months respectively named short, medium and long term. For this purpose, a survey was made of the current state of the art, mainly produced in Brazil. Introductorily, we present the concept of the cycle and leading indicators, as the justification and importance of this issue. Then, a survey of the literature on business cycles, addressing seminal publications such as Burns and Mitchell (1946), and the current discussion between business cycles and growth cycles. By then, it was mentioned the concept of leading indicators, its origin, the main methods used and current work on the subject. Finally, we built a methodology, based on the model proposed in OECD (2008) with addition of VAR models, Granger Causality and Probit. Being tested and evaluated, for the monthly information of physical production of Agribusiness in Brazil and other 421 series candidates as leading indicators, for period between January 1995 and December 2011. Completing positively to the possibility of creating composite leading indicators, whether short, medium or long term, for Brazilian Agribusiness.

Submitted by Silvana Teresinha Dornelles Studzinski (sstudzinski) on 2016-05-16T18:21:41Z No. of bitstreams: 1 Marcus Vinícius de Souza Almeida Conceição_.pdf: 1027394 bytes, checksum: ec1fa126f0db08777cb3c87ff8bdd7bd (MD5) / Made available in DSpace on 2016-05-16T18:21:41Z (GMT). No. of bitstreams: 1 Marcus Vinícius de Souza Almeida Conceição_.pdf: 1027394 bytes, checksum: ec1fa126f0db08777cb3c87ff8bdd7bd (MD5) Previous issue date: 2016-02-25 / Nenhuma / Este trabalho tem como objetivo, dentro da abordagem dos ciclos de negócios reais, elaborar dois indicadores compostos, antecedente e coincidente, para o complexo metalmecânico brasileiro. Delimita-se e caracteriza-se como complexo metalmecânico as atividades industriais nas seções 24; 25; 26; 27; 28; 29; 30 do CNAE 2.0. A elaboração dos indicadores orientou-se sob adaptação da metodologia proposta pela OCDE 2012 para elaboração de indicadores compostos. Consoante com as rotinas difundidas por esta metodologia foram coletadas e tratadas 1026 séries de tempo, com dados mensais abrangendo o período de 1994 a 2015. Estes dados sendo submetidos a tratamentos estatísticos de filtragem, avaliação, aplicação de filtros X-12, HP, CF, FD, realização de testes Cross-Correlation e utilização de modelos ARIMA e PROBIT. Como resultado, elaborou-se um índice (I-MM) para acompanhar em tempo real o desempenho do complexo metalmecânico e três indicadores antecedentes, estes compreendendo uma probabilidade de ocorrência de recessões e expansões para cenários de curto prazo (IACP-MM) e médio prazo (IAMP1-MM, IAMP2-MM). / This study aims, in the approach to real business cycles, draw two composite indicators, antecedent and coincident to the Brazilian metal-mechanic complex. It delimits and is characterized as metal-mechanic complex industrial activities in sections 24; 25; 26; 27; 28; 29; 30 of CNAE 2.0. The development of indicators was guided through adapting the methodology proposed by the OECD in 2012 for the preparation of composite indicators. According to the routines revealed by this methodology were collected and treated in 1026 time series with monthly data covering the period 1994 to 2015. These data were submitted to statistical treatment filtering and evaluation, application of X-12 filters, HP, CF FD, conducting Cross-Correlation tests using ARIMA models and PROBIT. As a result, produced an index (I-MM) to follow in real time the performance of the metal-mechanic complex and three leading indicators, these comprising a probability of occurrence of an event, which stipulate short-term scenarios (IACP-MM) and medium term (IAMP1-MM, IAMP2-MM)

Submitted by Gabriel Goulart Ferreira (gabrielggfsc@gmail.com) on 2017-08-24T21:23:43Z No. of bitstreams: 1 FGV Dissert - Final.pdf: 1134356 bytes, checksum: bae808529697b0480d6465ae8534ccf7 (MD5) / Approved for entry into archive by GILSON ROCHA MIRANDA (gilson.miranda@fgv.br) on 2017-08-25T13:40:06Z (GMT) No. of bitstreams: 1 FGV Dissert - Final.pdf: 1134356 bytes, checksum: bae808529697b0480d6465ae8534ccf7 (MD5) / Made available in DSpace on 2017-08-31T12:51:18Z (GMT). No. of bitstreams: 1 FGV Dissert - Final.pdf: 1134356 bytes, checksum: bae808529697b0480d6465ae8534ccf7 (MD5) Previous issue date: 2009-05-25 / This paper presents the analysis about the importance of the Consumer Confidence Indexes in the United States and the potential utilization of the Brazil’s similar index, the ICC FGV, to predict and track performance local economy. As practical result, this paper proposes a new alternative model to predict, in the short term, Monthly Industrial Production (PIM), a nationwide survey of industrial activity. / Esta dissertação apresenta análise sobre a importância dos indicadores de Confiança do Consumidor nos EUA e o potencial de utilização do indicador paralelo nacional, o ICC FGV, para previsão e acompanhamento do desempenho da economia brasileira. Como resultado prático, faz-se a proposição de novo modelo alternativo para previsão de curto prazo da PIM, Pesquisa Mensal Industrial do IBGE.

The primary objective of this diploma thesis is to comprehensively present the issue of money laundering not only on a macro level but also in terms of commercial bank and its microeconomic response to it. The main contribution of this diploma thesis is to identify the global indicators, which should be considered when drafting strategies in the fight against the legalization of proceeds from crimes. This diploma thesis addresses the changes caused by current globalization and highlights the dangerous effects it has on evolution of this consequent criminal activity together with evaluation of its potential in the future. Due to the tense situation in Europe caused by the series of terrorist attacks, part of the work is dedicated to the explanation of the relationship between terrorist financing and money laundering.

L’Europe a subi la crise la plus sévère de sa récente histoire à la suite de la crise financière globale de 2008. C’est pourquoi cette thèse a l’objectif d’identifier de façon empirique les déterminants de cette crise dans le cadre de 15 principaux membres de l’UE. Dans ce sens, nous développons d’abord un index de pression fiscale continu, contrairement aux travaux empiriques précédents, afin d’identifier des périodes de crise dans les pays UE-15 de 2003 à 2015. Ensuite, nous utilisons trois différentes techniques d’estimation, à savoir Cartes auto-organisatrices, Logit et Markov. Nos résultats d’estimation démontrent que notre indicateur de crise identifie le timing et la durée de la crise de dette dans chacun des pays de UE-15. Résultats empiriques indiquent également que l’occurrence de la crise de dette dans l’UE-15 est la conséquence de la détérioration de balances macroéconomiques et financières sachant que les variables comme le ratio des prêts non-performants sur les crédits totaux du secteur bancaire, la croissance du PIB, chômage, balance primaire / PIB, le solde ajusté du cycle PIB. De plus, variables démontrant la qualité de gouvernance tel que participation et responsabilisation, qualité de la réglementation, et de l'efficacité gouvernementale, jouent également un rôle important dans l’occurrence et sur la durée de la crise de dette dans le cadre de l’UE-15. Étant donne que les résultats économétriques indiquent l’importance de la détérioration fiscale dans l’occurrence de la crise de dette européenne, nous testons la convergence fiscale des pays membre de l’UE. Les résultats montrent que Portugal, Irlande, Italie, Grèce et Espagne diverge des autres pays de l’UE-15 en termes de dette publique / PIB alors qu’ils convergent, à part la Grèce, avec les autres pays membres de l’UE-15 en termes de déficit budgétaires / PIB. / Europe went through the most severe economic crisis of its recent history following the global financial crisis of 2008. Hence, this thesis aims to empirically identify the determinants of this crisis within the framework of 15 core EU member countries (EU-15). To do so, the study develops a continuous fiscal stress index, contrary to previous empirical studies that tend to use event-based crisis indicators, which identifies the debt crises in the EU-15 and the study employs three different estimation techniques, namely Self-Organizing Map, Multivariate Logit and Panel Markov Regime Switching models. Our estimation results show first that the study identifies correctly the time and the length of the debt crisis in each EU-15-member country by developing a fiscal stress index. Empirical results also indicate, via three different models, that the debt crisis in the EU-15 is the consequence of deterioration of both financial and macroeconomic variables such as nonperforming loans over total loans, GDP growth, unemployment rates, primary balance over GDP, and cyclically adjusted balance over GDP. Besides, variables measuring governance quality, such as voice and accountability, regulatory quality, and government effectiveness, also play a significant role in the emergence and the duration of the debt crisis in the EU-15. As the econometric results clearly indicate the importance of fiscal deterioration on the occurrence of the European debt crisis, this study also aims to test the fiscal convergence among the EU member countries. The results indicate that Portugal, Ireland, Italy, Greece, and Spain diverge from other EU-15 countries in terms of public debt-to-GDP ratio. In addition, results also show that all PIIGS countries except for Greece converge to EU-10 in terms of budget deficit-to-GDP ratio.

Cette étude vise à mettre en lumière les différences et similarités existant entre les principales crises économiques et financières ayant frappé un échantillon de 21 pays avancés depuis 1981. Nous analyserons plus particulièrement la crise des subprimes que nous rapprocherons avec des épisodes antérieurs. Nous étudierons à la fois les années du déclenchement des turbulences (analyse typologique) ainsi que celles les précédant (prévision). Cette analyse sera fondée sur l’utilisation de la méthode CART (Classification And Regression Trees). Cette technique non linéaire et non paramétrique permet de prendre en compte les effets de seuil et les interactions entre variables explicatives de façon à révéler plusieurs contextes distincts explicatifs d’un même événement. Dans le cadre d‘un modèle de prévision, l’analyse des années précédant les crises nous indique que les variables à surveiller sont : la variation et la volatilité du cours de l’once d’or, le déficit du compte courant en pourcentage du PIB et la variation de l’openness ratio et enfin la variation et la volatilité du taux de change. Dans le cadre de l’analyse typologique, l’étude des différentes variétés de crise (année du déclenchement de la crise) nous permettra d’identifier deux principaux types de turbulence d’un point de vue empirique. En premier lieu, nous retiendrons les crises globales caractérisées par un fort ralentissement ou une baisse de l’activité aux Etats-Unis et une faible croissance du PIB dans les pays touchés. D’autre part, nous mettrons en évidence des crises idiosyncratiques propres à un pays donné et caractérisées par une inflation et une volatilité du taux de change élevées. / The aim of this thesis is to analyze, from an empirical point of view, both the different varieties of economic and financial crises (typological analysis) and the context’s characteristics, which could be associated with a likely occurrence of such events. Consequently, we analyze both: years seeing a crisis occurring and years preceding such events (leading contexts analysis, forecasting). This study contributes to the empirical literature by focusing exclusively on the crises in advanced economies over the last 30 years, by considering several theoretical types of crises and by taking into account a large number of both economic and financial explanatory variables. As part of this research, we also analyze stylized facts related to the 2007/2008 subprimes turmoil and our ability to foresee crises from an epistemological perspective. Our empirical results are based on the use of binary classification trees through CART (Classification And Regression Trees) methodology. This nonparametric and nonlinear statistical technique allows us to manage large data set and is suitable to identify threshold effects and complex interactions among variables. Furthermore, this methodology leads to characterize crises (or context preceding a crisis) by several distinct sets of independent variables. Thus, we identify as leading indicators of economic and financial crises: variation and volatility of both gold prices and nominal exchange rates, as well as current account balance (as % of GDP) and change in openness ratio. Regarding the typological analysis, we figure out two main different empirical varieties of crises. First, we highlight « global type » crises characterized by a slowdown in US economic activity (stressing the role and influence of the USA in global economic conditions) and low GDP growth in the countries affected by the turmoil. Second, we find that country-specific high level of both inflation and exchange rates volatility could be considered as evidence of « idiosyncratic type » crises.

Over the last years, the growing availability of large datasets and the improvements in the computational speed of computers have further fostered the research in the fields of both macroeconomic modeling and forecasting analysis. A primary focus of these research areas is to improve the models performance by exploiting the informational content of several time series. Increasing the dimension of macro models is indeed crucial for a detailed structural understanding of the economic environment, as well as for an accurate forecasting analysis. As consequence, a new generation of large-scale macro models, based on the micro-foundations of a fully specified dynamic stochastic general equilibrium set-up, has became one of the most flourishing research areas of interest both in central banks and academia. At the same time, there has been a revival of forecasting methods dealing with many predictors, such as the factor models. The central idea of factor models is to exploit co-movements among variables through a parsimonious econometric structure. Few underlying common shocks or factors explain most of the co-variations among variables. The unexplained component of series movements is on the other hand due to pure idiosyncratic dynamics. The generality of their framework allows factor models to be suitable for describing a broad variety of models in a macroeconomic and a financial context. The revival of factor models, over the recent years, comes from important developments achieved by Stock and Watson (2002) and Forni, Hallin, Lippi and Reichlin (2000). These authors find the conditions under which some data averages become collinear to the space spanned by the factors when, the cross section dimension, becomes large. Moreover, their factor specifications allow the idiosyncratic dynamics to be mildly cross-correlated (an effect referred to as the 'approximate factor structure' by Chamberlain and Rothschild, 1983), a situation empirically verified in many applications. These findings have relevant implications. The most important being that the use of a large number of series is no longer representative of a dimensional constraint. On the other hand, it does help to identify the factor space. This new generation of factor models has been applied in several areas of macroeconomics and finance as well as for policy evaluation. It is consequently very likely to become a milestone in the literature of forecasting methods using many predictors. This thesis contributes to the empirical literature on factor models by proposing four original applications. <p><p>In the first chapter of this thesis, the generalized dynamic factor model of Forni et. al (2002) is employed to explore the predictive content of the asset returns in forecasting Consumer Price Index (CPI) inflation and the growth rate of Industrial Production (IP). The connection between stock markets and economic growth is well known. In the fundamental valuation of equity, the stock price is equal to the discounted future streams of expected dividends. Since the future dividends are related to future growth, a revision of prices, and hence returns, should signal movements in the future growth path. Though other important transmission channels, such as the Tobin's q theory (Tobin, 1969), the wealth effect as well as capital market imperfections, have been widely studied in this literature. I show that an aggregate index, such as the S&P500, could be misleading if used as a proxy for the informative content of the stock market as a whole. Despite the widespread wisdom of considering such index as a leading variable, only part of the assets included in the composition of the index has a leading behaviour with respect to the variables of interest. Its forecasting performance might be poor, leading to sceptical conclusions about the effectiveness of asset prices in forecasting macroeconomic variables. The main idea of the first essay is therefore to analyze the lead-lag structure of the assets composing the S&P500. The classification in leading, lagging and coincident variables is achieved by means of the cross correlation function cleaned of idiosyncratic noise and short run fluctuations. I assume that asset returns follow a factor structure. That is, they are the sum of two parts: a common part driven by few shocks common to all the assets and an idiosyncratic part, which is rather asset specific. The correlation<p>function, computed on the common part of the series, is not affected by the assets' specific dynamics and should provide information only on the series driven by the same common factors. Once the leading series are identified, they are grouped within the economic sector they belong to. The predictive content that such aggregates have in forecasting IP growth and CPI inflation is then explored and compared with the forecasting power of the S&P500 composite index. The forecasting exercise is addressed in the following way: first, in an autoregressive (AR) model I choose the truncation lag that minimizes the Mean Square Forecast Error (MSFE) in 11 years out of sample simulations for 1, 6 and 12 steps ahead, both for the IP growth rate and the CPI inflation. Second, the S&P500 is added as an explanatory variable to the previous AR specification. I repeat the simulation exercise and find that there are very small improvements of the MSFE statistics. Third, averages of stock return leading series, in the respective sector, are added as additional explanatory variables in the benchmark regression. Remarkable improvements are achieved with respect to the benchmark specification especially for one year horizon forecast. Significant improvements are also achieved for the shorter forecast horizons, when the leading series of the technology and energy sectors are used. <p><p>The second chapter of this thesis disentangles the sources of aggregate risk and measures the extent of co-movements in five European stock markets. Based on the static factor model of Stock and Watson (2002), it proposes a new method for measuring the impact of international, national and industry-specific shocks. The process of European economic and monetary integration with the advent of the EMU has been a central issue for investors and policy makers. During these years, the number of studies on the integration and linkages among European stock markets has increased enormously. Given their forward looking nature, stock prices are considered a key variable to use for establishing the developments in the economic and financial markets. Therefore, measuring the extent of co-movements between European stock markets has became, especially over the last years, one of the main concerns both for policy makers, who want to best shape their policy responses, and for investors who need to adapt their hedging strategies to the new political and economic environment. An optimal portfolio allocation strategy is based on a timely identification of the factors affecting asset returns. So far, literature dating back to Solnik (1974) identifies national factors as the main contributors to the co-variations among stock returns, with the industry factors playing a marginal role. The increasing financial and economic integration over the past years, fostered by the decline of trade barriers and a greater policy coordination, should have strongly reduced the importance of national factors and increased the importance of global determinants, such as industry determinants. However, somehow puzzling, recent studies demonstrated that countries sources are still very important and generally more important of the industry ones. This paper tries to cast some light on these conflicting results. The chapter proposes an econometric estimation strategy more flexible and suitable to disentangle and measure the impact of global and country factors. Results point to a declining influence of national determinants and to an increasing influence of the industries ones. The international influences remains the most important driving forces of excess returns. These findings overturn the results in the literature and have important implications for strategic portfolio allocation policies; they need to be revisited and adapted to the changed financial and economic scenario. <p><p>The third chapter presents a new stylized fact which can be helpful for discriminating among alternative explanations of the U.S. macroeconomic stability. The main finding is that the fall in time series volatility is associated with a sizable decline, of the order of 30% on average, in the predictive accuracy of several widely used forecasting models, included the factor models proposed by Stock and Watson (2002). This pattern is not limited to the measures of inflation but also extends to several indicators of real economic activity and interest rates. The generalized fall in predictive ability after the mid-1980s is particularly pronounced for forecast horizons beyond one quarter. Furthermore, this empirical regularity is not simply specific to a single method, rather it is a common feature of all models including those used by public and private institutions. In particular, the forecasts for output and inflation of the Fed's Green book and the Survey of Professional Forecasters (SPF) are significantly more accurate than a random walk only before 1985. After this date, in contrast, the hypothesis of equal predictive ability between naive random walk forecasts and the predictions of those institutions is not rejected for all horizons, the only exception being the current quarter. The results of this chapter may also be of interest for the empirical literature on asymmetric information. Romer and Romer (2000), for instance, consider a sample ending in the early 1990s and find that the Fed produced more accurate forecasts of inflation and output compared to several commercial providers. The results imply that the informational advantage of the Fed and those private forecasters is in fact limited to the 1970s and the beginning of the 1980s. In contrast, during the last two decades no forecasting model is better than "tossing a coin" beyond the first quarter horizon, thereby implying that on average uninformed economic agents can effectively anticipate future macroeconomics developments. On the other hand, econometric models and economists' judgement are quite helpful for the forecasts over the very short horizon, that is relevant for conjunctural analysis. Moreover, the literature on forecasting methods, recently surveyed by Stock and Watson (2005), has devoted a great deal of attention towards identifying the best model for predicting inflation and output. The majority of studies however are based on full-sample periods. The main findings in the chapter reveal that most of the full sample predictability of U.S. macroeconomic series arises from the years before 1985. Long time series appear<p>to attach a far larger weight on the earlier sub-sample, which is characterized by a larger volatility of inflation and output. Results also suggest that some caution should be used in evaluating the performance of alternative forecasting models on the basis of a pool of different sub-periods as full sample analysis are likely to miss parameter instability. <p><p>The fourth chapter performs a detailed forecast comparison between the static factor model of Stock and Watson (2002) (SW) and the dynamic factor model of Forni et. al. (2005) (FHLR). It is not the first work in performing such an evaluation. Boivin and Ng (2005) focus on a very similar problem, while Stock and Watson (2005) compare the performances of a larger class of predictors. The SW and FHLR methods essentially differ in the computation of the forecast of the common component. In particular, they differ in the estimation of the factor space and in the way projections onto this space are performed. In SW, the factors are estimated by static Principal Components (PC) of the sample covariance matrix and the forecast of the common component is simply the projection of the predicted variable on the factors. FHLR propose efficiency improvements in two directions. First, they estimate the common factors based on Generalized Principal Components (GPC) in which observations are weighted according to their signal to noise ratio. Second, they impose the constraints implied by the dynamic factors structure when the variables of interest are projected on the common factors. Specifically, they take into account the leading and lagging relations across series by means of principal components in the frequency domain. This allows for an efficient aggregation of variables that may be out of phase. Whether these efficiency improvements are helpful to forecast in a finite sample is however an empirical question. Literature has not yet reached a consensus. On the one hand, Stock and Watson (2005) show that both methods perform similarly (although they focus on the weighting of the idiosyncratic and not on the dynamic restrictions), while Boivin and Ng (2005) show that SW's method largely outperforms the FHLR's and, in particular, conjecture that the dynamic restrictions implied by the method are harmful for the forecast accuracy of the model. This chapter tries to shed some new light on these conflicting results. It<p>focuses on the Industrial Production index (IP) and the Consumer Price Index (CPI) and bases the evaluation on a simulated out-of sample forecasting exercise. The data set, borrowed from Stock and Watson (2002), consists of 146 monthly observations for the US economy. The data spans from 1959 to 1999. In order to isolate and evaluate specific characteristics of the methods, a procedure, where the<p>two non-parametric approaches are nested in a common framework, is designed. In addition, for both versions of the factor model forecasts, the chapter studies the contribution of the idiosyncratic component to the forecast. Other non-core aspects of the model are also investigated: robustness with respect to the choice of the number of factors and variable transformations. Finally, the chapter performs a sub-sample performances of the factor based forecasts. The purpose of this exercise is to design an experiment for assessing the contribution of the core characteristics of different models to the forecasting performance and discussing auxiliary issues. Hopefully this may also serve as a guide for practitioners in the field. As in Stock and Watson (2005), results show that efficiency improvements due to the weighting of the idiosyncratic components do not lead to significant more accurate forecasts, but, in contrast to Boivin and Ng (2005), it is shown that the dynamic restrictions imposed by the procedure of Forni et al. (2005) are not harmful for predictability. The main conclusion is that the two methods have a similar performance and produce highly collinear forecasts. <p> / Doctorat en sciences économiques, Orientation économie / info:eu-repo/semantics/nonPublished