The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Mario Forni - One of the best experts on this subject based on the ideXlab platform.
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the dynamic eects of monetary policy a structural Factor Model approach
Journal of Monetary Economics, 2010Co-Authors: Mario Forni, Luca GambettiAbstract:We use the structural Factor Model proposed by Forni, Giannone, Lippi and Reichlin (2007) to study the eects of monetary policy. The advantage with respect to the traditional vector autoregression Model is that we can exploit information from a large data set, made up of 112 US monthly macroeconomic series. Monetary policy shocks are identified using a standard recursive scheme, in which the impact eects
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the generalized dynamic Factor Model one sided estimation and forecasting
Journal of the American Statistical Association, 2005Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Lucrezia ReichlinAbstract:This Paper proposes a new forecasting method that exploits information from a large panel of time series. The method is based on the generalized dynamic Factor Model proposed in Forni, Hallin, Lippi, and Reichlin (2000), and takes advantage of the information on the dynamic covariance structure of the whole panel. We first use our previous method to obtain an estimation for the covariance matrices of common and idiosyncratic components. The generalized eigenvectors of this couple of matrices are then used to derive a consistent estimate of the optimal forecast, which is constructed as a linear combination of present and past observations only (one-sided filter). This two-step approach solves the end-of-sample problems caused by two-sided filtering (as in our previous work), while retaining the advantages of an estimator based on dynamic information. Both simulation results and an empirical illustration on the forecast of the Euro area industrial production and inflation, based on a panel of 447 monthly time series show very encouraging results.
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the generalized dynamic Factor Model one sided estimation and forecasting
Journal of the American Statistical Association, 2005Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Lucrezia ReichlinAbstract:This article proposes a new forecasting method that makes use of information from a large panel of time series. Like earlier methods, our method is based on a dynamic Factor Model. We argue that our method improves on a standard principal component predictor in that it fully exploits all the dynamic covariance structure of the panel and also weights the variables according to their estimated signal-to-noise ratio. We provide asymptotic results for our optimal forecast estimator and show that in finite samples, our forecast outperforms the standard principal components predictor.
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the generalized dynamic Factor Model consistency and rates
Journal of Econometrics, 2004Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Lucrezia ReichlinAbstract:Abstract A Factor Model generalizing those proposed by Geweke (in: D.J. Aigner and A.S. Goldberger, Latent Variables in Socio-Economic Models, North-Holland, Amsterdam, 1977), Sargent and Sims (New Methods in Business Research, Federal Reserve Bank of Minneapolis, Minneapolis, 1977), Engle and Watson (J. Amer. Statist. Assoc. 76 (1981) 774) and Stock and Watson (J. Business. Econom. Statist. 20 (2002) 147) has been introduced in Forni et al. (Rev. Econ. Statist. 80 (2000) 540), where consistent (as the number n of series and the number T of observations both tend to infinity along appropriate paths (n,T(n))) estimation methods for the common component are proposed. Rates of convergence associated with these methods are obtained here as functions of the paths (n,T(n)) along which n and T go to infinity. These results show that, under suitable assumptions, consistency requires T(n) to be at least of the same order as n, whereas an optimal rate of n is reached for T(n) of the order of n2. If convergence to the space of common components is considered, consistency holds irrespective of the path (T(n) thus can be arbitrarily slow); the optimal rate is still n , but only requires T(n) to be of the order of n.
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the generalized dynamic Factor Model one sided estimation and forecasting
LEM Papers Series, 2003Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Lucrezia ReichlinAbstract:This paper proposes a new forecasting method that exploits information from a largepanel of time series. The method is based on the generalized dynamic Factor Model proposedin Forni, Hallin, Lippi, and Reichlin (2000), and takes advantage of the information onthe dynamic covariance structure of the whole panel. We first use our previous method toobtain an estimation for the covariance matrices of common and idiosyncratic components.The generalized eigenvectors of this couple of matrices are then used to derive a consistentestimate of the optimal forecast. This two-step approach solves the end-of-sample problemscaused by two-sided filtering (as in our previous work), while retaining the advantages of anestimator based on dynamic information. The relative merits of our method and the oneproposed by Stock and Watson (2002) are discussed.
Lucrezia Reichlin - One of the best experts on this subject based on the ideXlab platform.
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the generalized dynamic Factor Model one sided estimation and forecasting
Journal of the American Statistical Association, 2005Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Lucrezia ReichlinAbstract:This Paper proposes a new forecasting method that exploits information from a large panel of time series. The method is based on the generalized dynamic Factor Model proposed in Forni, Hallin, Lippi, and Reichlin (2000), and takes advantage of the information on the dynamic covariance structure of the whole panel. We first use our previous method to obtain an estimation for the covariance matrices of common and idiosyncratic components. The generalized eigenvectors of this couple of matrices are then used to derive a consistent estimate of the optimal forecast, which is constructed as a linear combination of present and past observations only (one-sided filter). This two-step approach solves the end-of-sample problems caused by two-sided filtering (as in our previous work), while retaining the advantages of an estimator based on dynamic information. Both simulation results and an empirical illustration on the forecast of the Euro area industrial production and inflation, based on a panel of 447 monthly time series show very encouraging results.
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the generalized dynamic Factor Model one sided estimation and forecasting
Journal of the American Statistical Association, 2005Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Lucrezia ReichlinAbstract:This article proposes a new forecasting method that makes use of information from a large panel of time series. Like earlier methods, our method is based on a dynamic Factor Model. We argue that our method improves on a standard principal component predictor in that it fully exploits all the dynamic covariance structure of the panel and also weights the variables according to their estimated signal-to-noise ratio. We provide asymptotic results for our optimal forecast estimator and show that in finite samples, our forecast outperforms the standard principal components predictor.
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the generalized dynamic Factor Model consistency and rates
Journal of Econometrics, 2004Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Lucrezia ReichlinAbstract:Abstract A Factor Model generalizing those proposed by Geweke (in: D.J. Aigner and A.S. Goldberger, Latent Variables in Socio-Economic Models, North-Holland, Amsterdam, 1977), Sargent and Sims (New Methods in Business Research, Federal Reserve Bank of Minneapolis, Minneapolis, 1977), Engle and Watson (J. Amer. Statist. Assoc. 76 (1981) 774) and Stock and Watson (J. Business. Econom. Statist. 20 (2002) 147) has been introduced in Forni et al. (Rev. Econ. Statist. 80 (2000) 540), where consistent (as the number n of series and the number T of observations both tend to infinity along appropriate paths (n,T(n))) estimation methods for the common component are proposed. Rates of convergence associated with these methods are obtained here as functions of the paths (n,T(n)) along which n and T go to infinity. These results show that, under suitable assumptions, consistency requires T(n) to be at least of the same order as n, whereas an optimal rate of n is reached for T(n) of the order of n2. If convergence to the space of common components is considered, consistency holds irrespective of the path (T(n) thus can be arbitrarily slow); the optimal rate is still n , but only requires T(n) to be of the order of n.
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the generalized dynamic Factor Model one sided estimation and forecasting
LEM Papers Series, 2003Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Lucrezia ReichlinAbstract:This paper proposes a new forecasting method that exploits information from a largepanel of time series. The method is based on the generalized dynamic Factor Model proposedin Forni, Hallin, Lippi, and Reichlin (2000), and takes advantage of the information onthe dynamic covariance structure of the whole panel. We first use our previous method toobtain an estimation for the covariance matrices of common and idiosyncratic components.The generalized eigenvectors of this couple of matrices are then used to derive a consistentestimate of the optimal forecast. This two-step approach solves the end-of-sample problemscaused by two-sided filtering (as in our previous work), while retaining the advantages of anestimator based on dynamic information. The relative merits of our method and the oneproposed by Stock and Watson (2002) are discussed.
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the generalized dynamic Factor Model identification and estimation
The Review of Economics and Statistics, 2000Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Lucrezia ReichlinAbstract:This paper proposes a Factor Model with infinite dynamics and nonorthogonal idiosyncratic components. The Model, which we call the generalized dynamic-Factor Model, is novel to the literature and generalizes the static approximate Factor Model of Chamberlain and Rothschild (1983), as well as the exact Factor Model a la Sargent and Sims (1977). We provide identification conditions, propose an estimator of the common components, prove convergence as both time and cross-sectional size go to infinity at appropriate rates, and present simulation results. We use our Model to construct a coincident index for the European Union. Such index is defined as the common component of real GDP within a Model including several macroeconomic variables for each European country.
Marco Lippi - One of the best experts on this subject based on the ideXlab platform.
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the generalized dynamic Factor Model one sided estimation and forecasting
Journal of the American Statistical Association, 2005Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Lucrezia ReichlinAbstract:This Paper proposes a new forecasting method that exploits information from a large panel of time series. The method is based on the generalized dynamic Factor Model proposed in Forni, Hallin, Lippi, and Reichlin (2000), and takes advantage of the information on the dynamic covariance structure of the whole panel. We first use our previous method to obtain an estimation for the covariance matrices of common and idiosyncratic components. The generalized eigenvectors of this couple of matrices are then used to derive a consistent estimate of the optimal forecast, which is constructed as a linear combination of present and past observations only (one-sided filter). This two-step approach solves the end-of-sample problems caused by two-sided filtering (as in our previous work), while retaining the advantages of an estimator based on dynamic information. Both simulation results and an empirical illustration on the forecast of the Euro area industrial production and inflation, based on a panel of 447 monthly time series show very encouraging results.
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the generalized dynamic Factor Model one sided estimation and forecasting
Journal of the American Statistical Association, 2005Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Lucrezia ReichlinAbstract:This article proposes a new forecasting method that makes use of information from a large panel of time series. Like earlier methods, our method is based on a dynamic Factor Model. We argue that our method improves on a standard principal component predictor in that it fully exploits all the dynamic covariance structure of the panel and also weights the variables according to their estimated signal-to-noise ratio. We provide asymptotic results for our optimal forecast estimator and show that in finite samples, our forecast outperforms the standard principal components predictor.
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the generalized dynamic Factor Model consistency and rates
Journal of Econometrics, 2004Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Lucrezia ReichlinAbstract:Abstract A Factor Model generalizing those proposed by Geweke (in: D.J. Aigner and A.S. Goldberger, Latent Variables in Socio-Economic Models, North-Holland, Amsterdam, 1977), Sargent and Sims (New Methods in Business Research, Federal Reserve Bank of Minneapolis, Minneapolis, 1977), Engle and Watson (J. Amer. Statist. Assoc. 76 (1981) 774) and Stock and Watson (J. Business. Econom. Statist. 20 (2002) 147) has been introduced in Forni et al. (Rev. Econ. Statist. 80 (2000) 540), where consistent (as the number n of series and the number T of observations both tend to infinity along appropriate paths (n,T(n))) estimation methods for the common component are proposed. Rates of convergence associated with these methods are obtained here as functions of the paths (n,T(n)) along which n and T go to infinity. These results show that, under suitable assumptions, consistency requires T(n) to be at least of the same order as n, whereas an optimal rate of n is reached for T(n) of the order of n2. If convergence to the space of common components is considered, consistency holds irrespective of the path (T(n) thus can be arbitrarily slow); the optimal rate is still n , but only requires T(n) to be of the order of n.
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the generalized dynamic Factor Model one sided estimation and forecasting
LEM Papers Series, 2003Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Lucrezia ReichlinAbstract:This paper proposes a new forecasting method that exploits information from a largepanel of time series. The method is based on the generalized dynamic Factor Model proposedin Forni, Hallin, Lippi, and Reichlin (2000), and takes advantage of the information onthe dynamic covariance structure of the whole panel. We first use our previous method toobtain an estimation for the covariance matrices of common and idiosyncratic components.The generalized eigenvectors of this couple of matrices are then used to derive a consistentestimate of the optimal forecast. This two-step approach solves the end-of-sample problemscaused by two-sided filtering (as in our previous work), while retaining the advantages of anestimator based on dynamic information. The relative merits of our method and the oneproposed by Stock and Watson (2002) are discussed.
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the generalized dynamic Factor Model representation theory
Econometric Theory, 2001Co-Authors: Mario Forni, Marco LippiAbstract:This paper, along with the companion paper Forni, Hallin, Lippi, and Reichlin (2000, Review of Economics and Statistics 82, 540–554), introduces a new Model—the generalized dynamic Factor Model—for the empirical analysis of financial and macroeconomic data sets characterized by a large number of observations both cross section and over time. This Model provides a generalization of the static approximate Factor Model of Chamberlain (1983, Econometrica 51, 1181–1304) and Chamberlain and Rothschild (1983, Econometrica 51, 1305–1324) by allowing serial correlation within and across individual processes and of the dynamic Factor Model of Sargent and Sims (1977, in C.A. Sims (ed.), New Methods in Business Cycle Research, pp. 45–109) and Geweke (1977, in D.J. Aigner & A.S. Goldberger (eds.), Latent Variables in Socio-Economic Models, pp. 365–383) by allowing for nonorthogonal idiosyncratic terms. Whereas the companion paper concentrates on identification and estimation, here we give a full characterization of the generalized dynamic Factor Model in terms of observable spectral density matrices, thus laying a firm basis for empirical implementation of the Model. Moreover, the common Factors are obtained as limits of linear combinations of dynamic principal components. Thus the paper reconciles two seemingly unrelated statistical constructions.
Thomas A. Widiger - One of the best experts on this subject based on the ideXlab platform.
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five Factor Model personality disorder scales an introduction to a special section on assessment of maladaptive variants of the five Factor Model
Psychological Assessment, 2018Co-Authors: Michael R Bagby, Thomas A. WidigerAbstract:The Five-Factor Model (FFM) is a dimensional Model of general personality structure, consisting of the domains of neuroticism (or emotional instability), extraversion versus introversion, openness (or unconventionality), agreeableness versus antagonism, and conscientiousness (or constraint). The FFM is arguably the most commonly researched dimensional Model of general personality structure. However, a notable limitation of existing measures of the FFM has been a lack of coverage of its maladaptive variants. A series of self-report inventories has been developed to assess for the maladaptive personality traits that define Diagnostic and Statistical Manual of Mental Disorders (fifth edition; DSM-5) Section II personality disorders (American Psychiatric Association [APA], 2013) from the perspective of the FFM. In this paper, we provide an introduction to this Special Section, presenting the rationale and empirical support for these measures and placing them in the historical context of the recent revision to the APA diagnostic manual. This introduction is followed by 5 papers that provide further empirical support for these measures and address current issues within the personality assessment literature. (PsycINFO Database Record
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integrating normal and abnormal personality structure the five Factor Model
Journal of Personality, 2012Co-Authors: Thomas A. Widiger, Paul T. CostaAbstract:It is evident that the conceptualization, diagnosis, and classification of personality disorder (PD) is shifting toward a dimensional Model. The purpose of this special issue of Journal of Personality is to indicate how the Five-Factor Model (FFM) can provide a useful and meaningful basis for an integration of the description and classification of both normal and abnormal personality functioning. This introductory article discusses its empirical support and the potential advantages of understanding personality disorders, including those included within the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders and likely future PDs from the dimensional perspective of the FFM.
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five Factor Model of personality disorder a proposal for dsm v
Annual Review of Clinical Psychology, 2009Co-Authors: Thomas A. Widiger, Stephanie N MullinssweattAbstract:The predominant dimensional Model of general personality structure within psychology is the five-Factor Model (FFM). Research indicates that the personality disorders of the American Psychiatric Association's diagnostic manual can be understood as maladaptive variants of the domains and facets of the FFM. The current review provides a proposal for the classification of personality disorder from the perspective of the FFM. Discussed as well are implications and issues associated with an FFM of personality disorder, including the integration of a psychiatric nomenclature with general personality structure, the inclusion of a domain of openness to experience, the identification of problems in living associated with maladaptive personality traits, the setting of a diagnostic threshold, prototypal matching, feasibility, and clinical utility.
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Five Factor Model of personality disorder: Integrating science and practice
Journal of Research in Personality, 2005Co-Authors: Thomas A. WidigerAbstract:Abstract The National Institute of Mental Health has been encouraging the implementation of “translational” research, or studies that integrate basic science and applied, clinical practice. A potential exemplification of this effort would be the integration of basic science research on personality structure with the psychiatric classification of personality disorders; more specifically, a five-Factor Model of personality disorder. Advantages of a five-Factor Model of personality disorder include the provision of a precise yet comprehensive description of both normal and abnormal personality functioning, the avoidance of the many limitations and problems inherent to categorical diagnoses, and the incorporation of basic science research on general personality functioning into our understanding of personality disorders. An important goal of future research will be to evaluate its potential utility in clinical practice.
Marc Hallin - One of the best experts on this subject based on the ideXlab platform.
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the generalized dynamic Factor Model one sided estimation and forecasting
Journal of the American Statistical Association, 2005Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Lucrezia ReichlinAbstract:This Paper proposes a new forecasting method that exploits information from a large panel of time series. The method is based on the generalized dynamic Factor Model proposed in Forni, Hallin, Lippi, and Reichlin (2000), and takes advantage of the information on the dynamic covariance structure of the whole panel. We first use our previous method to obtain an estimation for the covariance matrices of common and idiosyncratic components. The generalized eigenvectors of this couple of matrices are then used to derive a consistent estimate of the optimal forecast, which is constructed as a linear combination of present and past observations only (one-sided filter). This two-step approach solves the end-of-sample problems caused by two-sided filtering (as in our previous work), while retaining the advantages of an estimator based on dynamic information. Both simulation results and an empirical illustration on the forecast of the Euro area industrial production and inflation, based on a panel of 447 monthly time series show very encouraging results.
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the generalized dynamic Factor Model one sided estimation and forecasting
Journal of the American Statistical Association, 2005Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Lucrezia ReichlinAbstract:This article proposes a new forecasting method that makes use of information from a large panel of time series. Like earlier methods, our method is based on a dynamic Factor Model. We argue that our method improves on a standard principal component predictor in that it fully exploits all the dynamic covariance structure of the panel and also weights the variables according to their estimated signal-to-noise ratio. We provide asymptotic results for our optimal forecast estimator and show that in finite samples, our forecast outperforms the standard principal components predictor.
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the generalized dynamic Factor Model consistency and rates
Journal of Econometrics, 2004Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Lucrezia ReichlinAbstract:Abstract A Factor Model generalizing those proposed by Geweke (in: D.J. Aigner and A.S. Goldberger, Latent Variables in Socio-Economic Models, North-Holland, Amsterdam, 1977), Sargent and Sims (New Methods in Business Research, Federal Reserve Bank of Minneapolis, Minneapolis, 1977), Engle and Watson (J. Amer. Statist. Assoc. 76 (1981) 774) and Stock and Watson (J. Business. Econom. Statist. 20 (2002) 147) has been introduced in Forni et al. (Rev. Econ. Statist. 80 (2000) 540), where consistent (as the number n of series and the number T of observations both tend to infinity along appropriate paths (n,T(n))) estimation methods for the common component are proposed. Rates of convergence associated with these methods are obtained here as functions of the paths (n,T(n)) along which n and T go to infinity. These results show that, under suitable assumptions, consistency requires T(n) to be at least of the same order as n, whereas an optimal rate of n is reached for T(n) of the order of n2. If convergence to the space of common components is considered, consistency holds irrespective of the path (T(n) thus can be arbitrarily slow); the optimal rate is still n , but only requires T(n) to be of the order of n.
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the generalized dynamic Factor Model one sided estimation and forecasting
LEM Papers Series, 2003Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Lucrezia ReichlinAbstract:This paper proposes a new forecasting method that exploits information from a largepanel of time series. The method is based on the generalized dynamic Factor Model proposedin Forni, Hallin, Lippi, and Reichlin (2000), and takes advantage of the information onthe dynamic covariance structure of the whole panel. We first use our previous method toobtain an estimation for the covariance matrices of common and idiosyncratic components.The generalized eigenvectors of this couple of matrices are then used to derive a consistentestimate of the optimal forecast. This two-step approach solves the end-of-sample problemscaused by two-sided filtering (as in our previous work), while retaining the advantages of anestimator based on dynamic information. The relative merits of our method and the oneproposed by Stock and Watson (2002) are discussed.
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the generalized dynamic Factor Model identification and estimation
The Review of Economics and Statistics, 2000Co-Authors: Mario Forni, Marc Hallin, Marco Lippi, Lucrezia ReichlinAbstract:This paper proposes a Factor Model with infinite dynamics and nonorthogonal idiosyncratic components. The Model, which we call the generalized dynamic-Factor Model, is novel to the literature and generalizes the static approximate Factor Model of Chamberlain and Rothschild (1983), as well as the exact Factor Model a la Sargent and Sims (1977). We provide identification conditions, propose an estimator of the common components, prove convergence as both time and cross-sectional size go to infinity at appropriate rates, and present simulation results. We use our Model to construct a coincident index for the European Union. Such index is defined as the common component of real GDP within a Model including several macroeconomic variables for each European country.
