Fama–MacBeth Regression Assumptions and Related Asset Pricing Methods
Summary
The document discusses econometric assumptions and alternatives for applying the Fama–MacBeth procedure to asset returns. Its main methodological point is that the procedure can accommodate cross-sectional correlation among errors, while the cited answer identifies an absence of time-series correlation in the errors as a key assumption. The note does not spell out the full two-stage estimation procedure or address every assumption needed for valid inference.
It places Fama–MacBeth alongside factor-model approaches associated with Fama and French, principal component analysis, and proprietary practitioner models. Examples of academic applications include asset pricing work using arbitrage pricing theory and later factor extensions. The discussion shows that model choice varies across research and industry settings, but does not provide a systematic comparison of performance or establish that one approach is preferred today. Its treatment is brief, so readers should consult methodological sources for details on standard errors and dependence structures.
Key ideas
- The cited response identifies lack of time-series correlation in regression errors as a key Fama–MacBeth assumption.
- The procedure can accommodate cross-sectional error correlation, according to the response.
- Factor models, principal component analysis, and proprietary models are presented as alternatives or related approaches.
- The note offers examples of use but does not provide a comprehensive account of assumptions or comparative evidence.
Tags
Full text
# What are the econometric assumptions in the Fama-MacBeth procedure (1973)? # What are the econometric assumptions in the Fama-MacBeth procedure (1973)? Fama-MacBeth (1973) introduce a two stage cross-sectional regression method (http://en.wikipedia.org/wiki/Fama%E2%80%93MacBeth_regression). - If I was to regress stock prices (or returns) on a conditioning set using the Fama-MacBeth regression method, what are the econometric assumptions behind this model? - Is the model still used today or is another model now preferred? ## Answer by Alexander Didenko (score 5, accepted) https://quant.stackexchange.com/a/10066 2) Alternative to Fama-MacBeth is Fama-French approach. Explanation of difference see, for example, here: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1271935 Fama-French approach was used by Carhart (introduced momentum), Pastor-Stambaugh (introduced liquidity), Fama-French themselves (used it to build 5-factor model), and many other (elsevier or google for "fama french factor model"). Fama-MacBeth approach was used in Chen, Roll, Ross, 1986, which is believed to be quite important paper for APT. There is also PCA approach to modelling asset returns. See, e.g., Luedecke, 1984, Connor and Korajczyk, 1988. In industry, still, scholar models are not used. Practitioners develop their own models, sometimes kept as closely held secret, sometimes - made available to public (BARRA, CSFB, Morgan Stanley, Salomon-Smith-Barney, Bloomberg). Sometimes these models are just overcomplicated versions of scholar models, built with the same approaches. Sometimes they try to use some combination, for example Bloomberg family of models combine PCA with macro- and fundamental approaches. ## Answer by Tom P (score 4) https://quant.stackexchange.com/a/10090 The key assumption is that there is no time-series correlation between the error terms. Fama-MacBeth can deal with cross-sectional correlations. See Samuel Thompson's "Simple formulas for standard errors that cluster by both firm and time" in the Journal of Financial Economics (2011) for a treatment of different regression methods for testing equity pricing models.
Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.