Self-Selection Models in Corporate Finance

66 Pages Posted: 8 Nov 2005

See all articles by Kai Li

Kai Li

University of British Columbia (UBC) - Sauder School of Business; China Academy of Financial Research (CAFR)

Nagpurnanand Prabhala

The Johns Hopkins Carey Business School

Date Written: September 2005

Abstract

Corporate finance decisions are not made at random, but are usually deliberate decisions by firms or their managers to self-select into their preferred choices. This chapter reviews econometric models of self-selection. The review is organized into two parts. The first part reviews econometric models of self-selection, focusing on the key assumptions of different models and the types of applications they may be best suited for. Part two reviews empirical applications of selection models in the areas of corporate investment, financing, and financial intermediation. We find that self-selection is a rapidly growing area in corporate finance, partly reflecting its recognition as a pervasive feature of corporate finance decisions, but more importantly, the increasing recognition of selection models as unique tools for understanding, modeling, and testing the role of private information in corporate finance.

Keywords: conditional event studies, endogeneity, Heckman two-step, matching, propensity score, switching regression

JEL Classification: G14, G20, G30

Suggested Citation

Li, Kai and Prabhala, Nagpurnanand, Self-Selection Models in Corporate Finance (September 2005). Robert H. Smith School Research Paper No. RHS 06-020. Available at SSRN: https://ssrn.com/abstract=843105 or http://dx.doi.org/10.2139/ssrn.843105

Kai Li (Contact Author)

University of British Columbia (UBC) - Sauder School of Business ( email )

2053 Main Mall
Vancouver, BC V6T 1Z2
Canada
604-822-8353 (Phone)
604-822-4695 (Fax)

HOME PAGE: http://finance.sauder.ubc.ca/~kaili

China Academy of Financial Research (CAFR)

1954 Huashan Road
Shanghai P.R.China, 200030
China

Nagpurnanand Prabhala

The Johns Hopkins Carey Business School ( email )

100 International Drive
Baltimore, MD 21202
United States
+1 410 234 4532 (Phone)

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