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OPENING THE BLACK BOX: STRUCTURAL FACTOR MODELS WITH LARGE CROSS SECTIONS
by
Forni, Mario
, Lippi, Marco
, Reichlin, Lucrezia
, Giannone, Domenico
in
Convergence
/ Datasets
/ Dynamic models
/ Econometric factor models
/ Econometric models
/ Econometrics
/ Economic models
/ Eigenvalues
/ Factor analysis
/ Forecasting models
/ Impulse response functions
/ Macroeconomic modeling
/ Macroeconomics
/ Mathematical models
/ Mathematical vectors
/ Matrices
/ Regression analysis
/ Structural analysis
/ Structural models
/ Studies
/ Variables
/ Vector autoregression
/ Vector-autoregressive models
2009
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OPENING THE BLACK BOX: STRUCTURAL FACTOR MODELS WITH LARGE CROSS SECTIONS
by
Forni, Mario
, Lippi, Marco
, Reichlin, Lucrezia
, Giannone, Domenico
in
Convergence
/ Datasets
/ Dynamic models
/ Econometric factor models
/ Econometric models
/ Econometrics
/ Economic models
/ Eigenvalues
/ Factor analysis
/ Forecasting models
/ Impulse response functions
/ Macroeconomic modeling
/ Macroeconomics
/ Mathematical models
/ Mathematical vectors
/ Matrices
/ Regression analysis
/ Structural analysis
/ Structural models
/ Studies
/ Variables
/ Vector autoregression
/ Vector-autoregressive models
2009
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Do you wish to request the book?
OPENING THE BLACK BOX: STRUCTURAL FACTOR MODELS WITH LARGE CROSS SECTIONS
by
Forni, Mario
, Lippi, Marco
, Reichlin, Lucrezia
, Giannone, Domenico
in
Convergence
/ Datasets
/ Dynamic models
/ Econometric factor models
/ Econometric models
/ Econometrics
/ Economic models
/ Eigenvalues
/ Factor analysis
/ Forecasting models
/ Impulse response functions
/ Macroeconomic modeling
/ Macroeconomics
/ Mathematical models
/ Mathematical vectors
/ Matrices
/ Regression analysis
/ Structural analysis
/ Structural models
/ Studies
/ Variables
/ Vector autoregression
/ Vector-autoregressive models
2009
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OPENING THE BLACK BOX: STRUCTURAL FACTOR MODELS WITH LARGE CROSS SECTIONS
Journal Article
OPENING THE BLACK BOX: STRUCTURAL FACTOR MODELS WITH LARGE CROSS SECTIONS
2009
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Overview
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.
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