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138
A Comprehensive Look at the Empirical Performance of Equity Premium Prediction,”
, 2004
"... Abstract Economists have suggested a whole range of variables that predict the equity premium: dividend price ratios, dividend yields, earningsprice ratios, dividend payout ratios, corporate or net issuing ratios, bookmarket ratios, beta premia, interest rates (in various guises), and consumption ..."
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Cited by 279 (6 self)
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Abstract Economists have suggested a whole range of variables that predict the equity premium: dividend price ratios, dividend yields, earningsprice ratios, dividend payout ratios, corporate or net issuing ratios, bookmarket ratios, beta premia, interest rates (in various guises), and consumptionbased macroeconomic ratios (cay). Our paper comprehensively reexamines the performance of these variables, both insample and outofsample, as of 2005. We find that [a] over the last 30 years, the prediction models have failed both insample and outofsample; [b] the models are unstable, in that their outofsample predictions have performed unexpectedly poorly; [c] the models would not have helped an investor with access only to information available at the time to time the market. JEL Classification: G12, G14. * Thanks to Malcolm Baker, Ray Ball, John Campbell, John Cochrane, Francis Diebold, Ravi Jagannathan, Owen Lamont, Sydney Ludvigson, Rajnish Mehra, Michael Roberts, Jay Shanken, Samuel Thompson, Jeff Wurgler, and Yihong Xia for comments; and Todd Clark for providing us with some critical McCracken values. We especially appreciate John Campbell and Sam Thompson for iterating drafts and exchanging perspectives with (or against) our earlier draftsthis has allowed us to significantly improve.
Why is it so Difficult to Beat the Random Walk Forecast of Exchange Rates
 Journal of International Economics
, 2003
"... Most TI discussion papers can be downloaded at ..."
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Insample or outofsample tests of predictability: which one should we use
 CEPR Discussion Papers 3671, CEPR Discussion Papers
, 2002
"... It is widely known that signiÞcant insample evidence of predictability does not guarantee signiÞcant outofsample predictability. This is often interpreted as an indication that insample evidence is likely to be spurious and should be discounted. In this paper we question this conventional wisdom ..."
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Cited by 162 (15 self)
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It is widely known that signiÞcant insample evidence of predictability does not guarantee signiÞcant outofsample predictability. This is often interpreted as an indication that insample evidence is likely to be spurious and should be discounted. In this paper we question this conventional wisdom. Our analysis shows that neither data mining nor parameter instability is a plausible explanation of the observed tendency of insample tests to reject the no predictability null more often than outofsample tests. We provide an alternative explanation based on the higher power of insample tests of predictability. We conclude that results of insample tests of predictability will typically be more credible than results of outofsample tests.
2006) Approximately normal tests for equal predictive accuracy in nested models. Journal of Econometrics forthcoming
"... Forecast evaluation often compares a parsimonious null model to a larger model that nests the null model. Under the null that the parsimonious model generates the data, the larger model introduces noise into its forecasts by estimating parameters whose population values are zero. We observe that the ..."
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Cited by 149 (14 self)
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Forecast evaluation often compares a parsimonious null model to a larger model that nests the null model. Under the null that the parsimonious model generates the data, the larger model introduces noise into its forecasts by estimating parameters whose population values are zero. We observe that the mean squared prediction error (MSPE) from the parsimonious model is therefore expected to be smaller than that of the larger model. We describe how to adjust MSPEs to account for this noise. We propose applying standard methods (West (1996)) to test whether the adjusted mean squared error difference is zero. We refer to nonstandard limiting distributions derived in Clark and McCracken (2001, 2005a) to argue that use of standard normal critical values will yield actual sizes close to, but a little less than, nominal size. Simulation evidence supports our recommended procedure. West thanks the National Science Foundation for financial support. We thank Pablo M. PincheiraBrown and Taisuke Nakata for helpful comments. The views expressed herein are solely those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of Kansas City or the Federal Reserve System.
Using OutofSample Mean Squared Prediction Errors to Test the Martingale Difference Hypothesis
, 2004
"... We consider using outofsample mean squared prediction errors (MSPEs) to evaluate the null that a given series follows a zero mean martingale difference against the alternative that it is linearly predictable. Under the null of no predictability, the population MSPE of the null “no change” model eq ..."
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Cited by 119 (14 self)
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We consider using outofsample mean squared prediction errors (MSPEs) to evaluate the null that a given series follows a zero mean martingale difference against the alternative that it is linearly predictable. Under the null of no predictability, the population MSPE of the null “no change” model equals that of the linear alternative. We show analytically and via simulations that despite this equality, the alternative model’s sample MSPE is expected to be greater than the null’s. For rolling regression estimators of the alternative model’s parameters, we propose and evaluate an asymptotically normal test that properly accounts for the upward shift of the sample MSPE of the alternative model. Our simulations indicate that our proposed procedure works well.
Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?
, 2004
"... Goyal and Welch (2006) argue that the historical average excess stock return forecasts future excess stock returns better than regressions of excess returns on predictor variables. In this paper we show that many predictive regressions beat the historical average return, once weak restrictions are i ..."
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Cited by 117 (3 self)
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Goyal and Welch (2006) argue that the historical average excess stock return forecasts future excess stock returns better than regressions of excess returns on predictor variables. In this paper we show that many predictive regressions beat the historical average return, once weak restrictions are imposed on the signs of coefficients and return forecasts. The outofsample explanatory power is small, but nonetheless is economically meaningful for meanvariance investors. Even better results can be obtained by imposing the restrictions of steadystate valuation models, thereby removing the need to estimate the average from a short sample of volatile stock returns. Towards the end of the last century, academic finance economists came to take seriously the view that aggregate stock returns are predictable. During the 1980’s a number of papers studied valuation ratios, such as the dividendprice ratio, earningsprice ratio, or smoothed earningsprice ratio. Valueoriented investors in the tradition of Graham and Dodd (1934) had always asserted that high valuation ratios are an indication of an undervalued stock market and should predict high subsequent returns, but these ideas did not carry much weight in the academic literature until authors such as Rozeff (1984), Fama and French (1988), and Campbell and Shiller (1988a,b) found that valuation ratios are positively correlated with subsequent returns and that the implied predictability of returns is substantial at longer horizons. Around the same time, several papers pointed out that yields on short and longterm Treasury and corporate bonds are correlated with subsequent stock returns [Fama and Schwert
Nominal exchange rates and monetary fundamentals: Evidence from a small postBretton woods panel
 Journal of International Economics
, 2001
"... We study the longrun relationship between nominal exchange rates and monetary fundamentals in a quarterly panel of 18 countries extending from 1973.1 to 1997.1. Our analysis is centered on two issues. First, we test whether exchange rates are cointegrated with longrun determinants predicted by eco ..."
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Cited by 114 (9 self)
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We study the longrun relationship between nominal exchange rates and monetary fundamentals in a quarterly panel of 18 countries extending from 1973.1 to 1997.1. Our analysis is centered on two issues. First, we test whether exchange rates are cointegrated with longrun determinants predicted by economic theory. These results generally support the hypothesis of cointegration. The second issue is to reexamine the ability for monetary fundamentals to forecast future exchange rate returns. Panel regression estimates and forecasts con¯rm that this forecasting power is signi¯cant. 1
Towards a solution to the puzzles in exchange rate economics: where do we stand?, Canadian
 Journal of Economics
, 2005
"... This paper provides a selective overview of puzzles in exchange rate economics. We begin with the forward bias puzzle: high interest rate currencies appreciate when one might guess that investors would demand higher interest rates on currencies expected to fall in value. We then analyze the purchasi ..."
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Cited by 82 (2 self)
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This paper provides a selective overview of puzzles in exchange rate economics. We begin with the forward bias puzzle: high interest rate currencies appreciate when one might guess that investors would demand higher interest rates on currencies expected to fall in value. We then analyze the purchasing power parity puzzle: the real exchange rate displays no (strong) reversion to a stable longrun equilibrium level. Finally, we cover the exchange rate disconnect puzzle: the lack of a link between the nominal exchange rate and economic fundamentals. For each puzzle, we critically review the literature and speculate on potential solutions. JEL classification: F31.