Alternative approaches to estimation and inference in large multifactor panels: Small sample results with an application to modelling of asset returns

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Working Paper ; No. 536

Classification and Topics

• Classification: Wirtschaft ; Hypothesis Testing: General ; Estimation: General ; Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models
• Subject: Cross section dependence, Large panels, Principal components, Common correlated effects, Return equations ; Panel ; Schätztheorie ; Faktorenanalyse ; Stichprobenverfahren ; Kapitalertrag ; Börsenkurs ; Schätzung ; Theorie ; Welt

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Kapetanios, George ; Pesaran, M. Hashem
• Event: Veröffentlichung
• (who): Queen Mary University of London, Department of Economics
• (where): London
• (when): 2005

Further information

• Handle: http://hdl.handle.net/10419/62910
• Last update: 10.03.2025, 11:45 AM CET