The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Mark W Watson - One of the best experts on this subject based on the ideXlab platform.
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sectoral versus aggregate shocks a Structural Factor analysis of industrial production
Journal of Political Economy, 2011Co-Authors: Andrew T Foerster, Pierredaniel G Sarte, Mark W WatsonAbstract:Using Factor methods, we decompose industrial production (IP) into components arising from aggregate and sector-specific shocks. An approximate Factor model finds that nearly all of IP variability is associated with common Factors. We then use a multisector growth model to adjust for the effects of input-output linkages in the Factor analysis. Thus, a Structural Factor analysis indicates that the Great Moderation was characterized by a fall in the importance of aggregate shocks while the volatility of sectoral shocks was essentially unchanged. Consequently, the role of idiosyncratic shocks increased considerably after the mid-1980s, explaining half of the quarterly variation in IP.
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sectoral vs aggregate shocks a Structural Factor analysis of industrial production
Social Science Research Network, 2008Co-Authors: Andrew T Foerster, Pierredaniel G Sarte, Mark W WatsonAbstract:This paper uses Factor analytic methods to decompose industrial production (IP) into components arising from aggregate shocks and idiosyncratic sector-specific shocks. An approximate Factor model finds that nearly all (90%) of the variability of quarterly growth rates in IP are associated with common Factors. Because common Factors may reflect sectoral shocks that have propagated by way of input-output linkages, we then use a multisector growth model to adjust for the effects of these linkages. In particular, we show that neoclassical multisector models, of the type first introduced by Long and Plosser (1983), produce an approximate Factor model as a reduced form. A Structural Factor analysis then indicates that aggregate shocks continue to be the dominant source of variation in IP, but the importance of sectoral shocks more than doubles after the Great Moderation (to 30%). The increase in the relative importance of these shocks follows from a fall in the contribution of aggregate shocks to IP movements after 1984.
Andrew T Foerster - One of the best experts on this subject based on the ideXlab platform.
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sectoral versus aggregate shocks a Structural Factor analysis of industrial production
Journal of Political Economy, 2011Co-Authors: Andrew T Foerster, Pierredaniel G Sarte, Mark W WatsonAbstract:Using Factor methods, we decompose industrial production (IP) into components arising from aggregate and sector-specific shocks. An approximate Factor model finds that nearly all of IP variability is associated with common Factors. We then use a multisector growth model to adjust for the effects of input-output linkages in the Factor analysis. Thus, a Structural Factor analysis indicates that the Great Moderation was characterized by a fall in the importance of aggregate shocks while the volatility of sectoral shocks was essentially unchanged. Consequently, the role of idiosyncratic shocks increased considerably after the mid-1980s, explaining half of the quarterly variation in IP.
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sectoral vs aggregate shocks a Structural Factor analysis of industrial production
Social Science Research Network, 2008Co-Authors: Andrew T Foerster, Pierredaniel G Sarte, Mark W WatsonAbstract:This paper uses Factor analytic methods to decompose industrial production (IP) into components arising from aggregate shocks and idiosyncratic sector-specific shocks. An approximate Factor model finds that nearly all (90%) of the variability of quarterly growth rates in IP are associated with common Factors. Because common Factors may reflect sectoral shocks that have propagated by way of input-output linkages, we then use a multisector growth model to adjust for the effects of these linkages. In particular, we show that neoclassical multisector models, of the type first introduced by Long and Plosser (1983), produce an approximate Factor model as a reduced form. A Structural Factor analysis then indicates that aggregate shocks continue to be the dominant source of variation in IP, but the importance of sectoral shocks more than doubles after the Great Moderation (to 30%). The increase in the relative importance of these shocks follows from a fall in the contribution of aggregate shocks to IP movements after 1984.
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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opening the black box Structural Factor models with large cross sections
Econometric Theory, 2009Co-Authors: Mario Forni, Domenico Giannone, Marco Lippi, Lucrezia ReichlinAbstract:This paper shows how large-dimensional dynamic Factor models are suitable for Structural analysis. We argue that all identification schemes employed in SVAR analysis can be easily adapted in dynamic Factor models. Moreover, the “problem of fundamentalness”, which is intractable in Structural VARs, can be solved, provided that the impulse-response functions are sufficiently heterogeneous. We provide consistent estimators for the impulse-response functions, as well as (n, T) rates of convergence. An exercise with US macroeconomic data shows that our solution of the fundamentalness problem may have important empirical consequences.
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opening the black box Structural Factor models with large cross sections
Econometric Theory, 2009Co-Authors: Mario Forni, Domenico Giannone, Marco Lippi, Lucrezia ReichlinAbstract:This paper shows how large-dimensional dynamic Factor models are suitable for Structural analysis. We argue that all identification schemes employed in Structural vector autoregression (SVAR) analysis can be easily adapted in dynamic Factor models. Moreover, the problem of fundamentalness, which is intractable in SVARs, can be solved, provided that the impulse-response functions are sufficiently heterogeneous. We provide consistent estimators for the impulse-response functions and for (n, T) rates of convergence. An exercise with U.S. macroeconomic data shows that our solution of the fundamentalness problem may have important empirical consequences. © 2009 Copyright Cambridge University Press 2009.
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The Dynamic Effects of Monetary Policy: A Structural Factor Model Approach
Research Papers in Economics, 2008Co-Authors: Mario Forni, Luca GambettiAbstract:We use the Structural Factor model proposed by Forni, Giannone, Lippi and Reichlin (2007) to study the effects 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 effects on both industrial production and prices are zero. Such a scheme, when applied to a VAR including a suitable selection of our variables, produces puzzling results. Our main findings are the following. (i) The maximal effect on bilateral real exchange rates is observed on impact, so that the “delayed overshooting” or “forward discount” puzzle disappears. (ii) After a contractionary shock prices fall at all horizons, so that the price puzzle is not there. (iii) Monetary policy has a sizable effect on both real and nominal variables. Such results suggest that the Structural Factor model is a promising tool for applied macroeconomics.
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opening the black box Structural Factor models with large cross sections
Center for Economic Research (RECent), 2007Co-Authors: Mario Forni, Domenico Giannone, Marco Lippi, Lucrezia ReichlinAbstract:This paper shows how large-dimensional dynamic Factor models are suitable for Structural analysis. We argue that all identification schemes employed in SVAR analysis can be easily adapted in dynamic Factor models. Moreover, the “problem of fundamentalness”, which is intractable in Structural VARs, can be solved, provided that the impulse-response functions are sufficiently heterogeneous. We provide consistent stimators for the impulse-response functions, as well as (n, T) rates of convergence. An exercise with US macroeconomic data shows that our solution of the fundamentalness problem may have important empirical consequences.
Pierredaniel G Sarte - One of the best experts on this subject based on the ideXlab platform.
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sectoral versus aggregate shocks a Structural Factor analysis of industrial production
Journal of Political Economy, 2011Co-Authors: Andrew T Foerster, Pierredaniel G Sarte, Mark W WatsonAbstract:Using Factor methods, we decompose industrial production (IP) into components arising from aggregate and sector-specific shocks. An approximate Factor model finds that nearly all of IP variability is associated with common Factors. We then use a multisector growth model to adjust for the effects of input-output linkages in the Factor analysis. Thus, a Structural Factor analysis indicates that the Great Moderation was characterized by a fall in the importance of aggregate shocks while the volatility of sectoral shocks was essentially unchanged. Consequently, the role of idiosyncratic shocks increased considerably after the mid-1980s, explaining half of the quarterly variation in IP.
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sectoral vs aggregate shocks a Structural Factor analysis of industrial production
Social Science Research Network, 2008Co-Authors: Andrew T Foerster, Pierredaniel G Sarte, Mark W WatsonAbstract:This paper uses Factor analytic methods to decompose industrial production (IP) into components arising from aggregate shocks and idiosyncratic sector-specific shocks. An approximate Factor model finds that nearly all (90%) of the variability of quarterly growth rates in IP are associated with common Factors. Because common Factors may reflect sectoral shocks that have propagated by way of input-output linkages, we then use a multisector growth model to adjust for the effects of these linkages. In particular, we show that neoclassical multisector models, of the type first introduced by Long and Plosser (1983), produce an approximate Factor model as a reduced form. A Structural Factor analysis then indicates that aggregate shocks continue to be the dominant source of variation in IP, but the importance of sectoral shocks more than doubles after the Great Moderation (to 30%). The increase in the relative importance of these shocks follows from a fall in the contribution of aggregate shocks to IP movements after 1984.
William W Eaton - One of the best experts on this subject based on the ideXlab platform.
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Structural Factor analyses for medically unexplained somatic symptoms of somatization disorder in the epidemiologic catchment area study
Psychological Medicine, 1997Co-Authors: G Liu, Michael R Clark, William W EatonAbstract:Background. Assess the latent structure of the DSM-III somatization symptoms and the stability of symptom patterns over time. Methods. Cross-sectional and longitudinal covariation of symptoms of somatization disorder were investigated using Structural equation models in a population-based data set from the Epidemiologic Catchment Area study. Results. Medically unexplained physical complaints were discovered to cluster into three separate Factors, consistent with the DSM-IV definition of somatization disorder, but one dominant general Factor was defined, consistent with the DSM-III conceptualization. Individual symptom prevalences and Factor structures were different for men and women. The Factor structures remained stable at 1 year follow-up. Variations in the threshold of number of somatization symptoms required for diagnosis affected prevalence, sex ratio and temporal stability of the diagnosis. Conclusions. These population-based results support dimensional models of somatization. Implications for changing the threshold of the categorical diagnosis of somatization disorder and providing better care for these patients are given.
