INSTRUMENTAL VARIABLE ESTIMATION IN A DATA RICH ENVIRONMENT

By Jushan Bai, Serena Ng. Econometric Theory, 2010

195 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4fdcbbe6af9a5b033c624cc0736b3ded19126ff5

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In Economics and Econometrics

Citations in the corpus

EFFICIENT ESTIMATION OF FACTOR MODELS (2011, unknown): “…2005; Forni, Hallin, Lippi, and Reichlin, 2003; Huang, Lee, and Li, 2006), policy analysis (e.g., Favero, Marcellino, and Neglia, 2005; Bernanke, Boivin, and Eliasz, 2005), predictive regressions (e.g., Ludvigson and Ng, 2007), and instrumental variables estimation (cf. Bai and Ng, 2010).”
TESTS FOR PARAMETER INSTABILITY IN DYNAMIC FACTOR MODELS (2013, background): “…forecasting (Stock and Watson, 2002a), factor augmented vector autoregressive (FAVAR) models (Bernanke, Boivin, and Eliasz, 2005; Stock and Watson, 2005), dynamic stochastic general equilibrium (DSGE) models (Boivin and Giannoni, 2006), and handling a large number of instruments (Bai and Ng, 2010).”
Are Poor Cities Cheap for Everyone? Non-Homotheticity and the Cost of Living Across U.S. Cities (2013, background): “31The principal components IV reduces the scale of the optimization problem with minimal sacrifice to identifying variation, noting that linear combinations of valid instruments remain valid instruments – c.f. Bai and Ng (2010).”
DYNAMIC FACTOR MODELS (2018, background): “Kapetanios and Marcellino (2008) and Bai and Ng (2010) consider the use of estimated factors as instrumental variables.”
Principal Components Estimation and Identication of the Factors (2011, background): “In problems with errors-in-variables or endogeneity such as considered in Bai and Ng (2010), one only needs the factors to be strongly correlated with the endogenous regressor to validate the factors as instruments.”
Instrumental variables estimation with many weak instruments using regularized JIVE (2014, background): “See also Gautier and Tsybakov (2011) for a different sparsity-based approach to IV estimation related to BCCH. Related ideas also appear in Bai and Ng (2010), Kapetanios and Marcellino (2010), Kapetanios, Khalaf, and Marcellino (2011), and Caner (2009).”
Estimating High Dimensional Covariance Matrices and its Applications (2011, background): “This issue is discussed in Bai and Ng (2010).”
A regularization approach to the many instruments problem (2012, methodology): “This method found a new ground of application in factor models (see Stock and Watson (2002), Bai and Ng (2002, 2006) and references therein).”
Cross-Sectional Dependence in Panel Data Analysis (2012, background): “Bai and Ng ( 2010 ) investigate a related model, in which the disturbance is cross-sectionally uncorrelated but there is a large number of endogenous regressors driven by a fixed number of unobserved common factors, which are strongly exogenous.”
Instrumental Variable Estimation of Dynamic Linear Panel Data Models with Defactored Regressors and a Multifactor Error Structure (2019, unknown): “Our approach can be regarded as the opposite one employed by Bai and Ng (2010) and Kapetanios and Marcellino (2010).”
Weak Instrument Robust Tests in GMM and the New Keynesian Phillips Curve (2009, methodology): “…to be useful in forecasting inflation (see Stock and Watson 1999) and this is precisely what is needed for instruments to be relevant for the NKPC. Bai and Ng (2008a) showed that the use of estimated factors as instruments does not pose a generated instrument problem when the number of variables…”
Selecting Instrumental Variables in a Data Rich Environment (2009, background): “These authors were concerned with situations when N is large relative to the given T (in their case, T=30) so that the first stage estimation is inefficient.”
Principal components estimation and identification of static factors (2013, background): “In problems with errors-in-variables or endogeneity such as considered in Bai and Ng (2010), one only * Corresponding author.”