Meng, Ginger and Hu, Gang and Bai, Jushan (2007): Olive: a simple method for estimating betas when factors are measured with error. Published in: The Journal of Financial Research , Vol. XXXIV, No. 1 (2011): pp. 2760.

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Abstract
We propose a simple and intuitive method for estimating betas when factors are measured with error: ordinary least squares instrumental variable estimator (OLIVE). OLIVE performs well when the number of instruments becomes large, while the performance of conventional instrumental variable methods becomes poor or even infeasible. In an empirical application, OLIVE beta estimates improve Rsquared significantly. More importantly, our results help resolve two puzzling findings in the prior literature: first, the sign of average risk premium on the beta for market return changes from negative to positive; second, the estimated value of average zerobeta rate is no longer too high.
Item Type:  MPRA Paper 

Original Title:  Olive: a simple method for estimating betas when factors are measured with error. 
Language:  English 
Keywords:  betas, factor analysis, GMM, FIML, measurement error 
Subjects:  G  Financial Economics > G1  General Financial Markets > G12  Asset Pricing; Trading volume; Bond Interest Rates G  Financial Economics > G1  General Financial Markets > G10  General C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models; Multiple Variables > C33  Models with Panel Data; Longitudinal Data; Spatial Time Series 
Item ID:  33183 
Depositing User:  Jushan Bai 
Date Deposited:  07. Nov 2011 01:32 
Last Modified:  12. Feb 2013 08:54 
References:  Bai, J., 2003, Inferential theory for factor models of large dimensions, Econometrica 71, 135171. Barnes, M. , and A. Hughes, 2002 A quantile regression analysis of the cross section of stock market returns, Working Paper, Federal Reserve Bank of Boston. Black, F., M. Jensen, and M. Scholes, 1972, The Capital Asset Pricing Model: some empirical tests, in M. Jensen, eds.: Studies in the Theory of Capital Markets (Praeger, New York). Campbell, J., A. Lo, and C. MacKinlay, 1997, The econometrics of financial markets (Princeton University Press). Chamberlain, G., and M. Rothschild, 1983, Arbitrage, factor structure and meanvariance analysis in large asset markets, Econometrica 51, 13051324. Chao, J, and N. Swanson, 2005, Consistent Estimation with a Large Number of Weak Instruments, Econometrica, 73, 16731692. Chen, N., R. Roll, and S. Ross, 1986, Economic forces and the stock market, Journal of Business 59, 383403. Cochrane, J., 1996, A crosssectional test of an investmentbase asset pricing model, Journal of Political Economy 104, 572621. Cochrane, J., 2001, Asset pricing (Princeton University Press). Coën, A., and F. Racicotv, 2007, Capital asset pricing models revisited: evidence from errors in variables, Economic Letters 95, 443450. Connor, G., and R. Korajczyk, 1986, Performance measurement with the arbitrage pricing theory, Journal of Financial Economics 15, 373394. Connor, G., and R. Korajczyk, 1991, The attributes, behavior and performance of U.S. mutual funds, Review of Quantitative Finance and Accounting 1, 526. Connor, G., and R. Korajczyk, 1993, A test for the number of factors in an approximate factor model, Journal of Finance 48, 12631291. Davidson, R., and J. MacKinnon, 1993, Estimation and inference in econometrics (Oxford University Press, New York). Donald, S., and W. Newey, 2001, Choosing the number of instruments, Econometrica 69, 11611191. Doran, H., and P. Schmidt, 2006, GMM estimators with improved finite sample properties using principal components of the weighting matrix, with an application to the dynamic panel data model, Journal of Econometrics 133, 387 409. Durbin, J., 1954, Errors in variables, International Statistical Review 22, 2332. Fama, E., and J. MacBeth, 1973, Risk, return and equilibrium: Empirical tests, Journal of Political Economy 81, 607636. Fama, E., and K. French, 1992, The crosssection of expected returns, Journal of Finance 47, 427465. Fama, E., and K. French, 1993, Common risk factors in the returns on stocks and bonds, Journal of Financial Economics 33, 356. Ferson, W., and S. Foerster, 1994, Finite sample properties of the Generalized Methods of Moments tests of conditional asset pricing models, Journal of Financial Economics 36, 2955. Ferson, W., and C. Harvey, 1999, Conditioning variables and the crosssection of stock returns, Journal of Finance 54, 13251360. Ferson, W., S. Sarkissian, and T. Simin, 2008, Asset pricing models with conditional betas and alphas: The effects of data snooping and spurious regression, Journal of Financial and Quantitative Analysis 43, 331354. Fuller, W., 1977, Some properties of a modification of the limited information estimator, Econometrica 45, 939954. Hahn, J., and J. Hausman, 2002, A new specification test for the validity of instrumental variables, Econometrica 70, 163189. Hahn, J., J. Hausman, and G. Kuersteiner, 2004, Estimation with weak instruments: accuracy of higherorder bias and MSE approximations, Econometrics Journal 7, 272306. Hansen, L., J. Heaton, and A. Yaron, 1996, Finitesample properties of some alternative GMM estimators, Journal of Business and Economic Statistics 14, 262280. Hussman, J., 1993, A note on the interpretation of crosssectional evidence against the betaexpected return relationship, Working Paper, University of Michigan. Jagannathan, R., and Z. Wang, 1996, The conditional CAPM and the crosssection of expected returns, Journal of Finance 51, 354. Jagannathan, R., and Z. Wang, 1998, An asymptotic theory for estimating betapricing models using crosssectional regression, Journal of Finance 53, 12851309. Jones, C., 2001, Extracting factors from heteroskedastic asset returns, Journal of Financial Economics 62, 293325. Kim, D., 1995, The errors in the variables problem in the crosssection of expected stock returns, Journal of Finance 50, 16051634. Lettau, M., and S. Ludvigson, 2001a, Consumption, aggregate wealth and expected stock returns, Journal of Finance 56, 815849. Lettau, M., and S. Ludvigson, 2001b, Resurrecting the (C)CAPM: a crosssectional test when risk premia are timevarying, Journal of Political Economy 109,12381287. Miller, M., and M. Scholes, 1972, Rates of return in relation to risk: A reexamination of some recent findings, in M. Jensen, eds.: Studies in the Theory of Capital Markets (Praeger, New York). Newey, W., and R. Smith, 2004, Higher order properties of GMM and generalized empirical likelihood estimators, Econometrica 72, 219255. Ng, S., 2006, Testing crosssection correlation in panel data using spacings, Journal of Business and Economic Statistics 24, 1223. Pal, M., 1980, Consistent moment estimators of regression coefficients in the presence of errors in variables, Journal of Econometrics 14, 349364. Pesaran, M., 2004, General diagnostic tests for cross section dependence in panels, Working Paper, CESifo. Roll, R., 1977, A critique of asset pricing theory's test: Part 1, Journal of Financial Economics 4, 129176. Rothenberg, T., 1983, Asymptotic properties of some estimators in structural models, in Studies in Econometrics, Time Series, and Multivariate Statistics. Shanken, J., 1992, On the estimation of betapricing models, Review of Financial Studies 5, 133. Wansbeek, T., and E. Meijer, 2000, Measurement error and latent variables in econometrics, in Advanced textbooks in economics (NorthHolland, Amsterdam). Wei, J., C. Lee, and A. Chen, 1991, Multivariate regression tests of the arbitrage pricing theory: the instrumentalvariables approach, Review of Quantitative Finance and Accounting 1, 191208. Windmeijer, F., 2005, A finite sample correction for the variance of linear efficient twostep GMM estimators, Journal of Econometrics 126, 2551. Wyhowski, D., 1998, Monte Carlo evidence for dynamic panel data models, Working Paper, Australian National University. 
URI:  http://mpra.ub.unimuenchen.de/id/eprint/33183 