A simple consistent bootstrap test for a parametric regression function

Li, Qi; Wang, Suojin

doi:10.1016/s0304-4076(98)00011-6

Cited by 232 publications

(214 citation statements)

References 32 publications

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“…Next, we use the nonparametric specification test proposed by Li and Wang (1998) to check whether the parametric model in (19) can be rejected against the nonparametric model in (17). Details on the test can be found in Appendix E. The null hypothesis is the first-difference version of the parametric model in (19) and the alternative is (18).…”

Section: Figurementioning

confidence: 99%

“…Several arguments can be put forward. The earlier stage of economic development are associated with comparatively 13 The test proposed by Li and Wang (1998) works for models with no serial correlation. If the idiosyncratic error term in the parametric model is a random walk, i.e.…”

Section: Economic and Environmental Implicationsmentioning

confidence: 99%

“…The model is estimated using local kernel regression and marginal integration along the lines of Linton and Nielsen (1995). We also perform the functional monotonicity test of Bowman et al (1998) and the specification test of Li and Wang (1998), in order to compare our nonparametric estimates with parametric analogues. We find that the relationship between CO 2 emissions and GDP per capita is upward sloping, and that the usually adopted polynomial functional form which leads to the environmental Kuznets curve in several studies is rejected against our nonparametric model.…”

Section: Introductionmentioning

confidence: 99%

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Economic Development and Co2 Emissions: A Nonparametric Panel Approach

2005

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Die Discussion Papers dienen einer möglichst schnellen Verbreitung von neueren Forschungsarbeiten des ZEW. Die Beiträge liegen in alleiniger Verantwortung der Autoren und stellen nicht notwendigerweise die Meinung des ZEW dar.Discussion Papers are intended to make results of ZEW research promptly available to other economists in order to encourage discussion and suggestions for revisions. The authors are solely responsible for the contents which do not necessarily represent the opinion of the ZEW.Download this ZEW Discussion Paper from our ftp server:ftp://ftp.zew.de/pub/zew-docs/dp/dp0556.pdf Non technical summaryThe relationship between economic development and environmental quality has been extensively explored in recent years. The shape of this relationship has implications for the definition of an appropriate joint economic and environmental policy. In the literature, this animated debate revolves around the existence of an Environmental Kuznets Curve, which implies that, starting from low levels of income per capita, environmental degradation increases, but after a certain level of income (turning point) it diminishes.This study investigates the question of the existence of an EKC using a nonparametric approach. In this framework, no a priori parametric functional form is assumed for modelling the relationship between carbon dioxide (CO 2 ) emissions and GDP per capita. The main reason for studying CO 2 emissions is that they play a focal role in the current debate on environmental protection and sustainable development. CO 2 has been recognized by most scientists as a major source of global warming through its greenhouse effects. Another reason is that CO 2 emissions are directly related to the use of energy, which is an essential factor in the world economy, both for production and consumption. Therefore, the relationship between CO 2 emissions and economic growth has important implications for environmental and economic policies.To estimate this relationship, we use information drawn from several data sets. CO 2 emissions measured in metric tons are obtained from the data base of the Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory. Real GDP per capita series, measured in thousand constant dollars at 1985 prices, are drawn from the Penn World Table 5.6. The resulting data set, a balanced panel of 100 countries, covers the period 1960-1996.We first consider the issue of structural stability of the relationship between CO 2 emissions and GDP per capita, and we find evidence of structural stability of the relationship over the period . Based on this result, the panel nature of the data allows us to specify a nonparametric model that accounts for heterogeneity across countries. We find that the relationship between CO 2 emissions and GDP per capita is upward sloping, and that the usually adopted polynomial functional form which leads to the environmental Kuznets curve in several studies is rejected against our nonparametric model. Moreover, by comparing different estimation methods ...

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Section: Figurementioning

confidence: 99%

Section: Economic and Environmental Implicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Economic Development and Co2 Emissions: A Nonparametric Panel Approach

2005

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“…Among the related work, only the testing procedures of Fan and Li (1999) and Li (1999) Li and Wang (1998) and Hsiao and Li (2001). A feasible test statistic based on the measure J has a second order degenerate U-statistics as the leading term under the null hypothesis.…”

Section: Introductionmentioning

confidence: 99%

A Consistent Nonparametric Test for Causality in Quantile

2012

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AbstactThis paper proposes a nonparametric test of causality in quantile. Zheng (1998) has proposed an idea to reduce the problem of testing a quantile restriction to a problem of testing a particular type of mean restriction in independent data. We extend Zheng's approach to the case of dependent data, particularly to the test of Granger causality in quantile. The proposed test statistic is shown to have a second-order degenerate U-statistic as a leading term under the null hypothesis. Using the result on the asymptotic normal distribution for a general second order degenerate U-statistics with weakly dependent data of Fan and Li (1996), we establish the asymptotic distribution of the test statistic for causality in quantile under β-mixing (absolutely regular) process.

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“…The test that we propose to check moment instability is a variant of nonparametric conditional moment tests as developed in Fan and Li (1999), Hjellvik, Yao, and Tjøstheim (1998), Lavergne and Vuong (2000), Li andWang (1998), andZheng (1996), among others.…”

Section: The Modelmentioning

confidence: 99%

Nonparametric Tests of Moment Condition Stability

Juhl

Zhang

2012

Econom. Theory

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KU ScholarWorks is a service provided by the KU Libraries' Office of Scholarly Communication & Copyright. This is the published version of the article, made available with the permission of the publisher. The original published version can be found at the link below. ZHIJIE XIAO Boston CollegeThis paper considers testing for moment condition instability for a wide variety of models that arise in econometric applications. We propose a nonparametric test based on smoothing the moment conditions over time. The resulting test takes the form of a U-statistic and has a limiting normal distribution. The proposed test statistic is not affected by changes in the distribution of the data, so long as certain simple regularity conditions hold. We examine the performance of the test through a small Monte Carlo experiment.

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A simple consistent bootstrap test for a parametric regression function

Cited by 232 publications

References 32 publications

Economic Development and Co2 Emissions: A Nonparametric Panel Approach

Economic Development and Co2 Emissions: A Nonparametric Panel Approach

A Consistent Nonparametric Test for Causality in Quantile

Nonparametric Tests of Moment Condition Stability

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