Alternative Approaches to Estimation and Inference in Large Multifactor Panels: Small Sample Results with an Application to Modelling of Asset Returns

By George Kapetanios, M. Pesaran. Social Science Research Network, 2005

162 citations recorded by the source corpus.

This paper considers alternative approaches to the analysis of large panel data models in the presence of error cross section dependence. A popular method for modelling such dependence uses a factor error structure. Such models raise new problems for estimation and inference. This paper compares two alternative methods for carrying out estimation and inference in panels with a multifactor error structure. One uses the correlated common effects estimator that proxies the unobserved factors by cross section averages of the observed variables as suggested by Pesaran (2004), and the other uses principal components following the work of Stock and Watson (2002). The paper develops the principal component method and provides small sample evidence on the comparative properties of these estimators by means of extensive Monte Carlo experiments. An empirical application to company returns provides an illustration of the alternative estimation procedures.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/8afafcbd856480965958da6b52b1f1b3fd5f7b1c

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Citations in the corpus

Weak and Strong Cross Section Dependence and Estimation of Large Panels (2011, methodology): “When N is small relative to T , the nature of cross section dependence is unimportant as long as the errors are not correlated with the regressors, in which case the Seemingly Unrelated Regression Equations (SURE) approach can be used (Zellner (1962)).”
Renewable and non-renewable energy consumption and economic activities: Further evidence from OECD countries (2014, background): “Furthermore, this estimator holds under different situations such as serial correlation in errors, unit roots in the variables and possible contemporaneous dependence of the observed regressors with the unobserved factors (Kapetanios and Pesaran, 2007; Pesaran and Tosetti, 2011).”
Two-Stage Instrumental Variable Estimation of Linear Panel Data Models with Interactive Effects (2020, methodology): “…Effects approach of Pesaran (2006), which involves least-squares on a regression model augmented by cross-sectional averages (CA) of observables; and the Principal Components (PC) estimator considered first by Kapetanios and Pesaran (2005) and analysed subsequently by Westerlund and Urbain (2015).”
Random Coefficient Models (2008, background): “Examples of studies that have used the residual factor structure to model cross section dependence include Holtz-Eakin, Newey, and Rosen (1988), Ahn, Lee and Schmidt (2001), Coakley, Fuertes and Smith (2005), Bai and Ng (2004), Kapetanios and Pesaran (2007) , Phillips and Sul (2003), Moon and Perro”
Random Coefficient Panel Data Models (2004, methodology): “� = IN � and repeatedly using the identity ( 30 ) we can write”
Large Panels with Common Factors and Spatial Correlation (2011, background): “…a variety of situa- tions, such as serial correlation in errors, unit roots in the factors and possible contemporaneous dependence of the observed regressors with the unobserved factors (Coakley, Fuertes, and Smith (2006); Kapetanios and Pesaran (2007); Kapetanios, Pesaran, and Yagamata (2010)).”
Sieve estimation of panel data models with cross section dependence (2012, background): “See Bai (2009), Coakley et al. (2002), Greenaway-McGrevy et al. (2008), Harding (2007), Kapetanios and Pesaran (2005), Moon andWeidner (2008), Pesaran (2004, 2006), Pesaran and Tosetti (2007), Phillips and Sul (2003, 2007), among others, for an overview.”
Fair Weather or Foul? The Macroeconomic Effects of El Niño* (2015, background): “Firstly, as Kilian and Kim (2009) argue, the LP estimator tends to have higher variance when the data generating process is well approximated by a VAR because local projections impose less structure on the estimation problem.”
Specification Test for Panel Data Models with Interactive Fixed Effects (2014, unknown): “For example, Pesaran (2006) considers estimation and inference in heterogeneous panel data models with a general multifactor error structure; Bai (2009) proposes a principal component analysis (PCA) estimator and establishes its asymptotic normality for homogeneous panel data models with interactive”