How to implement the bootstrap in static or stable dynamic regression models: test statistic versus confidence region approach

Giersbergen, N.P.A. van; Kiviet, Jan F.

doi:10.1016/s0304-4076(01)00132-4

Cited by 18 publications

(9 citation statements)

References 30 publications

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“…Thus, they recommend the use of restricted residuals to compute the HCCME based test. On the other hand, van Giersbergen and Kiviet (2002) argue that if the null is not true, the use of unrestricted residuals could improve the power and should be preferred. However, this last argument is not supported in simulation experiments, see MacKinnon (2002).…”

Section: Restricted or Unrestricted Residuals?mentioning

confidence: 99%

Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap

Flachaire

2005

Computational Statistics & Data Analysis

165

153

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International audienceIn regression models, appropriate bootstrap methods for inference robust to heteroskedasticity of unknown form are the wild bootstrap and the pairs bootstrap. The finite sample performance of a heteroskedastic-robust test is investigated with Monte Carlo experiments. The simulation results suggest that one specific version of the wild bootstrap outperforms the other versions of the wild bootstrap and of the pairs bootstrap. It is the only one for which the bootstrap test gives always better results than the asymptotic test

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Section: Restricted or Unrestricted Residuals?mentioning

confidence: 99%

Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap

Flachaire

2005

Computational Statistics & Data Analysis

165

153

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“…Instead we take a nonparametric bootstrap approach, so that critical values are tailored to the unknown null distributions of the test statistics. Since these models are dynamic, we follow the suggested restricted sampling technique of van Gierbergen and Kiviet (2002). We conduct 10, 000 bootstrap iterations for each coefficient in each of the models.…”

Section: Results From the Us Gdp Modelsmentioning

confidence: 99%

Long-Term Oil Price Forecasts: A New Perspective on Oil and the Macroeconomy

Miller

2011

Macroecon. Dynam.

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We examine how future real GDP growth relates to changes in the forecasted longterm average of discounted real oil prices and to changes in unanticipated fluctuations of real oil prices around the forecasts. Forecasts are conducted using a state-space oil market model, in which global real economic activity and real oil prices share a common stochastic trend. Changes in unanticipated fluctuations and changes in the forecasted longterm average of discounted real oil prices sum to real oil price changes. We find that these two components have distinctly different relationships with future real GDP growth. Positive and negative changes in the unanticipated fluctuations of real oil prices correlate with asymmetric responses of future real GDP growth. In comparison, changes in the forecasted long-term average are smaller in magnitude but are more influential on real GDP. Persistent upward revisions of forecasts in the 2000s had a substantial negative impact on real GDP growth, according to our estimates.This version:

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“…16 Compute the m-th t-statistic based on the HCCME transformed constrained residuals, labeled ast m . 13 A large body of literature deals with the question whether to use restricted or unrestricted residuals for bootstrap based parameter inference (e.g., Davidson and MacKinnon, 1985;Godfrey and Orme, 2001;MacKinnon, 2002;van Giersbergen and Kiviet, 2002;and Davidson and Flachaire, 2008). 14 Hence, the latter choice directly implies E[ 4 i ] = 1, and thus the fourth-order feature of the errors are retained.…”

Section: Structure Of the MC Experimentsmentioning

confidence: 99%

“…A large body of literature deals with the question whether to use restricted or unrestricted residuals for bootstrap based parameter inference (e.g., Davidson and MacKinnon, ; Godfrey and Orme, ; MacKinnon, ; van Giersbergen and Kiviet, ; and Davidson and Flachaire, ).…”

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confidence: 99%

Is Spatial Bootstrapping a Panacea for Valid Inference?

Klarl

2013

Journal of Regional Science

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How to implement the bootstrap in static or stable dynamic regression models: test statistic versus confidence region approach

Cited by 18 publications

References 30 publications

Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap

Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap

Long-Term Oil Price Forecasts: A New Perspective on Oil and the Macroeconomy

Is Spatial Bootstrapping a Panacea for Valid Inference?

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