Showing papers by "Laura Serlenga published in 2002"

A Panel Data Approach to Testing Anomaly E®ects in Factor Pricing Models ¤

Abstract: There has been a large anomaly literature whererm specic characteristics such as earnings-to-price ratio and book-to-market ratio as well as size help explain cross sectional returns. These anomalies that have been attributed to market ine±ciency could be the result of a misspecication of the underlying factor pricing model. The most popular approach to detecting these anomaly e®ects has been the two pass (TP) cross-sectional regression models, advanced by Black, Jensen and Scholes (1972) and Fama and MacBeth (1973). However, it is well-established that the TP method su®ers from the errors in variables problem, because estimated betas are used in the second stage cross sectional regression. In this paper we address the issue of testing for factor price misspecication via the panel data approach. Perhaps one of the main reasons for the neglect of benets of using panel data technique is that in factor pricing models, all betas are heterogeneous in therst pass time series regression. However, if our interest lies solely in testingthe signicance of therm's characteristics in factor pricing models, we can show how to construct a theoretically coherent example to which panel data techniques dealing with both homogeneous and heterogeneous parameters can be applied. Panel-based anomaly tests have one clear advantage over TP-based tests; they are based on full information maximum likelihood estimates so that they do not su®er from the errors in variable problem and have all the usual asymptotic properties associated with likelihood tests. The empirical illustration shows the importance of Book-to-Market equity and market value in helping explain asset returns even in the three factor models.

Three Alternative Approaches to Test the Permanent Income Hypothesis in Dynamic Panels

Abstract: In this paper we consider three alternative approaches to test the Permanent Income Hypothesis (PIH) in the context of dynamic panels: the aggregate consumption approach, the Euler equation approach and finally Friedman (1957)'s original characteristic tests. Our empirical evidence, using the British Household Panel Survey (BHPS) data, strongly supports the PIH. This analysis can, thus, be considered as supporting the view that empirical tests of PIH, based on aggregate time-series data, might suffer from misspecification or overlook some fundamental characteristics of micro data.

A Panel Data Approach to testing Anomaly Effects in Factor Pricing Models

Abstract: There has been a large anomaly literature where firm specific characteristics such as leverage, past returns, dividend-yield, earnings-to-price ratios and book-to-market ratios as well as size help explain cross sectional returns. These anomalies that have been attributed to market inefficiency could be the result of a mis-specification of the underlying factor pricing model. The most popular approach to detecting these anomaly effects has been the two pass (TP) cross-sectional regression models, advanced by Black, Jensen and Scholes (1972) and Fama and MacBeth (1973). However, it is well-established that the TP method suffers from the errors in variables problem, because estimated betas are used in place of true betas in the second stage cross sectional regression. In this paper we address the issue of testing for factor price misspecification via the panel data approach. It is a salient fact that conventional approaches have completely ignored the benefits of using panel data techniques. Perhaps one of the main reasons for this neglect is that in factor pricing models, all betas are heterogeneous in the first pass time series regression. As a result there is no room for exploiting the panel dimension since there are no homogeneous coefficients to estimate. If our interest lies solely in testing the significance of these characteristics, we can show how to construct a theoretically coherent example to which panel data techniques dealing with both homogeneous and heterogeneous parameters can be applied. Panel-based anomaly tests have one clear advantage over TP-based tests; they are based on full information maximum likelihood estimates so that they do not suffer from the errors in variable problem and have all the usual asymptotic properties associated with likelihood tests. The empirical illustration shows the importance of market to book and market value in helping explain asset returns.