4 edition of **The Stambaugh bias in panel predictive regressions** found in the catalog.

The Stambaugh bias in panel predictive regressions

Erik Hjalmarsson

- 63 Want to read
- 9 Currently reading

Published
**2007**
by Federal Reserve Board in Washington, D.C
.

Written in English

**Edition Notes**

Statement | Erik Hjalmarsson. |

Series | International finance discussion papers -- no. 914, International finance discussion papers (Online) -- no. 914. |

Contributions | Board of Governors of the Federal Reserve System (U.S.) |

Classifications | |
---|---|

LC Classifications | HG3879 |

The Physical Object | |

Format | Electronic resource |

ID Numbers | |

Open Library | OL16442794M |

LC Control Number | 2007702798 |

The point is that there remains a problem in predictive regressions, in the absence of the bias studied by Stambaugh, because of spurious regression. B. Spurious Regression and Data Mining We consider the interaction between spurious regression and data mining, where the instruments to be mined are independent as in Foster et al. (). TESTING INSTABILITY IN A PREDICTIVE REGRESSION MODEL WITH NONSTATIONARY REGRESSORS - Volume 31 Issue 5 - Zongwu Cai, Yunfei Wang, .

() and Stambaugh () pointed out that persistence leads to biased coe!cients in predictive regressions if innovations in the predictor variable are correlated with returns (as is strongly the case for valuation ratios, although not for interest rates). Under the same conditions, the standard t-test for predictability has incorrect size. Abstract. Goyal and Welch argue that the historical average excess stock return forecasts future excess stock returns better than regressions of excess returns on predictor this article, we show that many predictive regressions beat the historical average return, once weak restrictions are imposed on the signs of coefficients and return by:

Bias is the difference between the “truth” (the model that contains all the relevant variables) and what we would get if we ran a naïve regression (one that has omitted at least one key variable). If we have the true regression model, we can actually calculate the . This is analogous to small‐sample bias in standard OLS predictive regressions (e.g., Stambaugh and Nelson and Kim), which enters into forecasts via estimated predictive coefficients Consider, for instance, OLS forecasts of r t + 1 on some predictor z t, where both r .

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Lation results and the empirical results clearly show that the Stambaugh bias is at least as important in panel regressions as it is in time-series regressions. The rest of the paper is organized as follows. Section 2 outlines the panel model and shows the Stambaugh bias in panel predictive by: This paper analyzes predictive regressions in a panel data setting.

The standard fixed effects estimator suffers from a small sample bias, which is the analogue of the Stambaugh bias in time-series predictive regressions. Monte Carlo evidence shows that the bias and resulting size distortions can be severe. This paper analyzes predictive regressions in a panel data setting.

The standard ﬁxed effects estimator suffers from a small sample bias, which is the analogue of the Stambaugh bias in time-series predictive regressions. Monte Carlo evidence shows that the bias and resulting size distortions can be severe.

A new. This paper analyzes predictive regressions in a panel data setting. The standard fixed effects estimator suffers from a small sample bias, which is the analogue of the Stambaugh bias in time-series predictive regressions.

Monte Carlo evidence shows that the bias and resulting size distortions can be by: Downloadable. This paper analyzes predictive regressions in a panel data setting. The standard fixed effects estimator suffers from a small sample bias, which is the analogue of the Stambaugh bias in time-series predictive regressions.

Monte Carlo evidence shows that the bias and resulting size distortions can be severe. A new bias-corrected estimator is proposed, which is shown to. This paper analyzes predictive regressions in a panel data setting. The standard fixed effects estimator suffers from a small sample bias, which is the analogue of Author: Erik Hjalmarsson.

E-mail address: [email protected] (R.F. Stambaugh) Journal of Financial Economics 54 () } Predictive regressionsq Robert F. Stambaugh* The Wharton School, University of Pennsylvania, Philadelphia, PAUSA National Bureau of Economic Research, Cambridge, MAUSA. This is the original paper that explains the Stambaugh bias: Stambaugh (JFE).It examines the bias in the slope coefficient in the predictive regression of returns on past dividend yields, when the dividend yield process is highly persistent.

Predictive Regressions. NBER Working Paper No. t 50 Pages Posted: 17 Mar Last revised: 30 Aug See all articles by Robert F. Stambaugh Robert F. Stambaugh. University of Pennsylvania - The Wharton School; National Bureau of Economic Research (NBER) Date Written: May The bias comes from the paper Stambaugh () and has nothing to do with small sample bias.

It has to do with point (1) below. The argument goes as follows: Typical lagged explanatory variables for stock-return regressions are correlated with contemporaneous stock returns; This contemporaneous correlation biases forecasting regressions.

Introduction. Many empirical studies in economics and finance investigate regressions of the form (1) y t =α+βx t−1 +u t, where y t reflects a change in an asset's price during period t, x t−1 is a lagged variable related to asset prices at the end of period t−1, and u t is the regression's disturbance.

Examples of such a regression occur when y t is the return on a portfolio of. a model where the predictive variable is an AR(1) process and its residuals are correlated with the predictive regressions’ residuals, the ordinary least squares (OLS) estimator of the predictive variable’s coeﬃcient, βˆ, will be biased in ﬁnite sample.

Examples of such predictive regressions abound. Keim and Stambaugh () propose. This paper analyzes predictive regressions in a panel data setting. The standard fixed effects estimator suffers from a small sample bias, which is the analogue of.

very close to one, which gives an upperbound for the bias in ˆ.1 1In predictive regressions, where stock returns are predicted by a lagged variable that is autore-gressive, Ferson et al. () show that data mining for predictor variables interacts with spurious regression by: Predictive Regressions: A Reduced-Bias Estimation Method Yakov Amihud1 Cliﬀord M.

Hurvich2 Novem 1Department of Finance, Stern School of Business, New York University, New York NY 2Statistics, Stern School of Business, New York University, New York NY The authors thank Robert Engle, Gary Simon and Jeﬀrey Simonoﬀ for Cited by: Stambaugh Correlations, Monkey Econometricians and Redundant Predictors August 8, Abstract We consider inference in a widely used predictive model in empirical ﬁnance.

"Stam-baugh Bias" arises when innovations to the predictor variable are correlated with those in the predictive regression.

Stambaugh Correlations and Redundant Predictors ∗ Donald Robertson & Stephen Wright† September 4, Abstract We consider inference in a widely used predictive model in empirical ﬁnance.

"Stam-baugh Bias" arises when innovations to the predictor variable are correlated with those in the predictive regression. incorrect conclusion that the lagged variable has predictive power while in fact it does not.

Stambaugh () derived the bias expression for the OLS estimate, which was subsequently used in empirical studies to obtain a reduced-bias point estimate of the predictive coe–cient.

Subsequent papers developed methods for hypothesis testing of. Downloadable. This paper analyzes econometric inference in predictive regressions in a panel data setting.

In a traditional time-series framework, estimation and testing are often made difficult by the endogeneity and near persistence of many forecasting variables; tests of whether the dividend-price ratio predicts stock returns is a prototypical example.

• The use of panel data allows empirical tests of a wide range of hypotheses. • With panel data we can control for: – Unobserved or unmeasurable sources of individual heterogeneity that vary across individuals but do not vary over time – omitted variable biasFile Size: KB.

“Interpreting long-horizon estimates in predictive regressions ”, Finance Research Letters, Vol. 5, No. 2, Junepp. 9. “The Stambaugh bias in panel predictive regressions ”, Finance Research Junepp. Book Chapters.variables in the literature are highly persistent, and Stambaugh () pointed out that persistence leads to biased coeﬃcients in predictive regressions if innovations in the predictor variable are correlated with returns (as is strongly the case for valuation ratios, although not for interest rates).

Under the same conditions the standard t-Cited by: Introduction. In a class of predictive regressions analyzed by Stambaugh (), a variable is regressed on the lagged value of a predictor variable, which is autoregressive with errors that are correlated with the errors of the regression small samples, the ordinary least-squares (OLS) estimated predictive slope coefficient is biased, potentially leading to an Cited by: