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711,952
Predictive regressions
 Journal of Financial Economics
, 1999
"... When a rate of return is regressed on a lagged stochastic regressor, such as a dividend yield, the regression disturbance is correlated with the regressor's innovation. The OLS estimator's "nitesample properties, derived here, can depart substantially from the standard regression set ..."
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Cited by 452 (19 self)
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When a rate of return is regressed on a lagged stochastic regressor, such as a dividend yield, the regression disturbance is correlated with the regressor's innovation. The OLS estimator's "nitesample properties, derived here, can depart substantially from the standard regression
and Predictive Regression ∗
, 2012
"... A prominent use of local to unity limit theory in applied work is the construction of confidence intervals for autogressive roots through inversion of the ADF t statistic associated with a unit root test, as suggested in Stock (1991). Such confidence intervals are valid when the true model has an au ..."
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of these results for predictive regression tests are explored. It is shown that when the regressor has autoregressive coeffi cient ρ  < 1 and the sample size n → ∞, the Campbell and Yogo (2006) confidence intervals for the regression coeffi cient have zero coverage probability asymptotically
Least angle regression
 Ann. Statist
"... The purpose of model selection algorithms such as All Subsets, Forward Selection and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will be applied. Typically we have available a large collection of possible covariates from which we hope to s ..."
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Cited by 1308 (43 self)
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to select a parsimonious set for the efficient prediction of a response variable. Least Angle Regression (LARS), a new model selection algorithm, is a useful and less greedy version of traditional forward selection methods. Three main properties are derived: (1) A simple modification of the LARS algorithm
Are the Predictive Regression Tests Overrejecting?
, 2009
"... This paper presents a source of statistical distortions induced by present value models that may significantly affect statistical inference in the predictive regression both in finite samples and asymptotically. We show both analytically and by simulation that the regressionbased tests including op ..."
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This paper presents a source of statistical distortions induced by present value models that may significantly affect statistical inference in the predictive regression both in finite samples and asymptotically. We show both analytically and by simulation that the regressionbased tests including
Noisy Predictive Regressions
"... Even if returns are truly forecasted by variables such as the dividend yield, the noise in such a predictive regression may overwhelm the signal of the conditioning variable and render estimation, inference and forecasting unreliable. Unfortunately, traditional asymptotic approximations are not suit ..."
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Even if returns are truly forecasted by variables such as the dividend yield, the noise in such a predictive regression may overwhelm the signal of the conditioning variable and render estimation, inference and forecasting unreliable. Unfortunately, traditional asymptotic approximations
NONPARAMETRIC PREDICTIVE REGRESSION By
, 2012
"... A unifying framework for inference is developed in predictive regressions where the predictor has unknown integration properties and may be stationary or nonstationary. Two easily implemented nonparametric Ftests are proposed. The test statistics are related to those of Kasparis and Phillips (2012) ..."
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A unifying framework for inference is developed in predictive regressions where the predictor has unknown integration properties and may be stationary or nonstationary. Two easily implemented nonparametric Ftests are proposed. The test statistics are related to those of Kasparis and Phillips (2012
Regression Shrinkage and Selection Via the Lasso
 Journal of the Royal Statistical Society, Series B
, 1994
"... We propose a new method for estimation in linear models. The "lasso" minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients that are exactl ..."
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Cited by 4055 (51 self)
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that are exactly zero and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models like subset selection and exhibits the stability of ridge regression. There is also
Predictive regressions with panel data
 International Finance Discussion Papers, Board of Governors of the Federal Reserve System, Washington D.C
, 2006
"... This paper analyzes econometric inference in predictive regressions in a panel data setting. In a traditional timeseries framework, estimation and testing are often made difficult by the endogeneity and near persistence of many forecasting variables; tests of whether the dividendprice ratio predic ..."
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Cited by 2 (0 self)
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This paper analyzes econometric inference in predictive regressions in a panel data setting. In a traditional timeseries framework, estimation and testing are often made difficult by the endogeneity and near persistence of many forecasting variables; tests of whether the dividendprice ratio
Results 1  10
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711,952