Structural Breaks and Predictive Regression Models of Aggregate U.S. Stock Returns
By David E. Rapach, M. Wohar. 2006
159 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/4c13cab1771a6f3e661b050574ef270830cf2742
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Paresh Kumar Narayan
- S. Sharma
- Muhammad Shahbaz
- K. Lim
- Seema W. Narayan
- R. Brooks
- Russell Smyth *
- Allan G. Timmermann
In Finance
- Paresh Kumar Narayan
- K. Lim
- S. Sharma
- R. Brooks
- Seema W. Narayan
- Allan G. Timmermann
- Muhammad Shahbaz
- Afees A. Salisu
Citations in the corpus
- The Evolution of Stock Market Efficiency Over Time: A Survey of the Empirical Literature (2011, background): “Second, researchers have recently begun to examine the structural stability of the parameters in traditional predictive regression models of aggregate stock returns (see, for example, Paye and Timmermann, 2006; Rapach and Wohar, 2006; Lettau and Nieuwerburgh, 2008; Hjalmarsson, 2009).”
- Advances in Forecasting Under Instability (2012, unknown): “Rapach and Wohar (2006) document the existence of structural breaks in the predictive ability of several variables (such as the dividend price ratio and the default spread) and S&P 500; the results are similar when predicting CRSP equal-weighted real stock returns.”
- Elusive Return Predictability (2008, background): “There is some evidence of negative serial correlation 4Paye and Timmermann (2006) and Rapach and Wohar (2006) report broad evidence of breaks in a wide range of models used to predict stock returns.”
- Improving Forecast Accuracy by Combining Recursive and Rolling Forecasts (2008, unknown): “In the theory, we show a weighted average forecast to be at least as accurate, and often more accurate, than any forecast based on a single estimation sample, even when the single sample is optimized to maximize forecast accuracy.”
- An analysis of commodity markets: What gain for investors? (2013, background): “(3), assuming that yt 1 ¼ 1;R F t 1 0 and b = (b0, b1)0, can be expressed as follows: RSt ¼ y0t 1bþ et ; ð4Þ Following Rapach and Wohar (2006), we allow for a structural break in both the intercept and slope coefficients of the predictive regression model.”