Access the full text.
Sign up today, get DeepDyve free for 14 days.
SE Hein, P Westfall (2004)
Improving tests of abnormal returns by bootstrapping the multivariate regression model with event paramatersJ Financ Economet, 2
C. Jarque, Anil Bera (1980)
Efficient tests for normality, homoscedasticity and serial independence of regression residualsEconomics Letters, 6
Rex Thompson (1985)
Conditioning the Return-Generating Process on Firm-Specific Events: A Discussion of Event Study MethodsJournal of Financial and Quantitative Analysis, 20
Pin-Huang Chou (1998)
Bootstrap Tests for Multivariate Event StudiesReview of Quantitative Finance and Accounting, 23
T. Breusch, A. Pagan (1980)
The Lagrange Multiplier Test and its Applications to Model Specification in EconometricsThe Review of Economic Studies, 47
S. Harrington, David Shrider (2002)
All Events Induce Variance: Analyzing Abnormal Returns When Effects Vary across FirmsJournal of Financial and Quantitative Analysis, 42
Stephen Brown, Jerold Warner (1985)
Using daily stock returns: The case of event studiesJournal of Financial Economics, 14
B. Greenwald (1980)
A general analysis of bias in the estimated standard errors of least squares coefficients
I. Karafiath (1994)
On the Efficiency of Least Squares Regression with Security Abnormal Returns as the Dependent VariableJournal of Financial and Quantitative Analysis, 29
R. Chandra, B. Balachandran (1992)
More Powerful Portfolio Approaches to Regressing Abnormal Returns on Firm‐Specific Variables for Cross‐Sectional StudiesJournal of Finance, 47
H. White (1980)
A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for HeteroskedasticityEconometrica, 48
John Binder (1997)
The Event Study Methodology Since 1969Review of Quantitative Finance and Accounting, 11
Scott Hein, P. Westfall (2004)
Improving Tests of Abnormal Returns by Bootstrapping the Multivariate Regression Model with Event ParamentsCapital Markets: Asset Pricing & Valuation
V. Bernard (1987)
CROSS-SECTIONAL DEPENDENCE AND PROBLEMS IN INFERENCE IN MARKET-BASED ACCOUNTING RESEARCHJournal of Accounting Research, 25
J. MacKinnon, H. White (1985)
Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties☆Journal of Econometrics, 29
Stephan Sefcik, Rex Thompson (1986)
AN APPROACH TO STATISTICAL-INFERENCE IN CROSS-SECTIONAL MODELS WITH SECURITY ABNORMAL RETURNS AS DEPENDENT VARIABLEJournal of Accounting Research, 24
Regression analysis is often used to estimate a linear relationship between security abnormal returns and firm-specific variables. If the abnormal returns are caused by a common event (i.e., there is “event clustering”) the error term of the cross-sectional regression will be heteroskedastic and correlated across observations. The size and power of alternative test statistics for the event clustering case has been evaluated under ideal conditions (Monte Carlo experiments using normally distributed synthetic security returns) by Chandra and Balachandran (J Finance 47:2055–2070, 1992) and Karafiath (J Financ Quant Anal 29(2):279–300, 1994). Harrington and Shrider (J Financ Quant Anal 42(1):229–256, 2007) evaluate cross-sectional regressions using actual (not simulated) stock returns only for the case of cross-sectional independence, i.e., in the absence of clustering. In order to evaluate the event clustering case, random samples of security returns are drawn from the data set provided by the Center for Research in Security Prices (CRSP) and the empirical distributions of alternative test statistics compared. These simulations include a comparison of OLS, WLS, GLS, two heteroskedastic-consistent estimators, and a bootstrap test for GLS. In addition, the Sefcik and Thompson (J Accounting Res 24(2):316–334, 1986) portfolio counterparts to OLS, WLS, and GLS, are evaluated. The main result from these simulations is none of the other estimator shows clear advantages over OLS or WLS. Researchers should be aware, however, that in these simulations the variance of the error term in the cross-sectional regression is unrelated to the explanatory variable.
Review of Quantitative Finance and Accounting – Springer Journals
Published: Dec 6, 2007
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.