Multiple-Predictor Regressions: Hypothesis Testing

Amihud, Yakov; Hurvich, Clifford M.; Wang, Yi

doi:10.1093/rfs/hhn056

Cited by 137 publications

(87 citation statements)

References 36 publications

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“…The basis data generating process (DGP) is a bivariate VAR(1) process previously used in Amihud and Hurvich (2004), Amihud et al (2009), andEngsted andPedersen (2014):…”

Section: Data Generating Processesmentioning

confidence: 99%

Comparing Several Methods to Compute Joint Prediction Regions for Path Forecasts Generated by Vector Autoregressions

Bruder

2014

SSRN Journal

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Path forecasts, defined as sequences of individual forecasts, generated by vector autoregressions are widely used in applied work. It has been recognized that a profound econometric analysis often requires, besides the path forecast, a joint prediction region that contains the whole future path with a prespecified coverage probability. The forecasting literature offers several different methods for computing joint prediction regions, where the existing methods are either bootstrap based or rely on asymptotic results. The aim of this paper is to investigate the finite-sample performance of three methods for constructing joint prediction regions in various scenarios via Monte Carlo simulations.

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“…The basis data generating process (DGP) is a bivariate VAR(1) process previously used in Amihud and Hurvich (2004), Amihud et al (2009), andEngsted andPedersen (2014):…”

Section: Data Generating Processesmentioning

confidence: 99%

Comparing Several Methods to Compute Joint Prediction Regions for Path Forecasts Generated by Vector Autoregressions

Bruder

2014

SSRN Journal

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“…We examine models in which we apply versions of the bias correction procedure developed by Amihud, Hurvich, and Wang (2009) Thus, bias-corrected estimation appears to be useful, and we recommend that future work estimating cointegrated long-run risk models apply bias corrections.…”

Section: The Effects Of Bias Correctionmentioning

confidence: 99%

The "Out of Sample" Performance of Long-run Risk Models

Ferson¹,

Nallareddy²,

Xie³

2012

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This paper studies the ability of long-run risk models to explain out-of-sample asset returns during 1931-2009. The long-run risk models perform relatively well on the momentum effect. A cointegrated version of the model outperforms the classical, stationary version. Both the long-run and the short run consumption shocks in the models are empirically important for the models' performance. The models' average pricing errors are especially small in the decades from the 1950s to the 1990s. When we restrict the risk premiums to identify structural parameters, this results in larger average pricing errors but often smaller error variances. The mean squared errors are not substantially better than those of the classical CAPM, except for Momentum.

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“…We will denote this approach the 'plug-in' approach. Alternatively, we can apply a more elaborate iterative scheme in which bias-adjusted estimates of Φ are recursively inserted in (7) or (10), see, e.g., Amihud and Hurvich [6] and Amihud et al [32]. An iterative scheme is basically just an extension of the 'plug-in' approach and could for the analytical bias formulas go as follows.…”

Section: Unknown Parameter Valuesmentioning

confidence: 99%

“…This VAR model is also used in simulation studies by Amihud and Hurvich [6] and Amihud et al [32] in analyzing return predictability by persistent state variables. The table shows the mean slope coefficients and the average squared bias, variance, and RMSE = √ bias 2 +variance across the four slope coefficients for T = {50, 100, 200, 500}.…”

Section: Bias-correction In Stationary Modelsmentioning

confidence: 99%

Bias-Correction in Vector Autoregressive Models: A Simulation Study

Engsted

Pedersen

2011

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract: We analyze the properties of various methods for bias-correcting parameter estimates in both stationary and non-stationary vector autoregressive models. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study, we show that when the model is stationary this simple bias formula compares very favorably to bootstrap bias-correction, both in terms of bias and mean squared error. In non-stationary models, the analytical bias formula performs noticeably worse than bootstrapping. Both methods yield a notable improvement over ordinary least squares. We pay special attention to the risk of pushing an otherwise stationary model into the non-stationary region of the parameter space when correcting for bias. Finally, we consider a recently proposed reduced-bias weighted least squares estimator, and we find that it compares very favorably in non-stationary models. Terms of use: Documents in

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Multiple-Predictor Regressions: Hypothesis Testing

Abstract: The authors thank James Stock, the Editor and the Referee for helpful comments and suggestions.

Cited by 137 publications

References 36 publications

Comparing Several Methods to Compute Joint Prediction Regions for Path Forecasts Generated by Vector Autoregressions

Comparing Several Methods to Compute Joint Prediction Regions for Path Forecasts Generated by Vector Autoregressions

The "Out of Sample" Performance of Long-run Risk Models

Bias-Correction in Vector Autoregressive Models: A Simulation Study

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