Monitoring Cointegrating Polynomial Regressions: Theory and Application to the Environmental Kuznets Curves for Carbon and Sulfur Dioxide Emissions

Knorre, Fabian; Wagner, Martin; Grupe, Maximilian

doi:10.3390/econometrics9010012

Cited by 4 publications

(2 citation statements)

References 61 publications

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“…Wagner (2008) takes non-linearity into account by replacing GDP by de-factored GDP, and Wagner (2015) extends the Fully modified OLS procedure to deal with this and estimates the EKC. See also Knorre et al (2021) and Wagner et al (2020) for newly developed procedures for monitoring polynomial regressions. Esteve and Tamarit (2012) use threshold cointegration to take the non-linear effects into account.…”

Section: Empirical Assessment Of the Environmental Kuznets Curvementioning

confidence: 99%

Long run non-linearity in CO2 Emissions: The I(2) Cointegration Model and the Environmental Kuznets Curve

Kivedal

2023

Preprint

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I use the I(2) cointegration model in order to assess the empirical relevance of the environmental Kuznets curve for CO2 emissions in the US between 1960 and 2014. This takes the non-linearity of CO2 emissions into account by allowing for using data that are integrated of order two, I(2), directly in the model. This provides an extensive dynamic analysis of the relationship between emissions and economic growth as postulated by the environmental Kuznets curve both in the short, medium and long run. The results indicate that the main driver behind the non-linear shape of US CO2 emissions in the long run is an increase in emissions caused by energy use and a decrease caused by more trade and using less polluting energy sources. GDP only have short run effects. Hence, I do not find evidence in favor of a long run relationship between economic development and the concave shape of emissions as suggested by the environmental Kuznets curve. JEL classification: C32; Q43; Q56

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Section: Empirical Assessment Of the Environmental Kuznets Curvementioning

confidence: 99%

Long run non-linearity in CO2 Emissions: The I(2) Cointegration Model and the Environmental Kuznets Curve

Kivedal

2023

Preprint

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“…Self-normalization has also been applied to testing for a unit root in univariate time series, monitoring cointegrating relationships and the analysis of high-dimensional stationary time series, seeBreitung (2002),Knorre et al (2021) andWang and Shao (2020), respectively.3 The VAR sieve bootstrap is frequently used in related literature:Psaradakis (2001), inspired by the seminal work ofLi and Maddala (1997), shows the usefulness of the sieve bootstrap in cointegrating regressions and provides its asymptotic justification by proving an underlying invariance principle result. Subsequently,Chang and Park (2003) and apply the sieve bootstrap to unit root testing and to conduct D-OLS based inference in cointegrating regressions, respectively.…”

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confidence: 99%

A Bootstrap-Assisted Self-Normalization Approach to Inference in Cointegrating Regressions

Reichold¹,

Jentsch²

2022

Preprint

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Traditional inference in cointegrating regressions requires tuning parameter choices to estimate a long-run variance parameter. Even in case these choices are "optimal", the tests are severely size distorted. We propose a novel self-normalization approach, which leads to a nuisance parameter free limiting distribution without estimating the long-run variance parameter directly. This makes our self-normalized test tuning parameter free and considerably less prone to size distortions at the cost of only small power losses. In combination with an asymptotically justified vector autoregressive sieve bootstrap to construct critical values, the self-normalization approach shows further improvement in small to medium samples when the level of error serial correlation or regressor endogeneity is large. We illustrate the usefulness of the bootstrap-assisted self-normalized test in empirical applications by analyzing the validity of the Fisher effect in Germany and the United States.

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Long run non-linearity in CO2 emissions: the I(2) cointegration model and the environmental Kuznets curve

Kivedal

2023

Empirica

View full text Add to dashboard Cite

I utilize the I(2) cointegration model to assess the empirical relevance of the environmental Kuznets curve for $$\hbox {CO}_2$$ CO 2 emissions in the US between 1960 and 2014. This takes the non-linearity of $$\hbox {CO}_2$$ CO 2 emissions into account by directly incorporating data that are integrated of order two, I(2). As a result, it enables an extensive dynamic analysis of the relationship between emissions and economic growth, as postulated by the environmental Kuznets curve both in the short, medium, and long run. The results indicate that the primary drivers behind the non-linear shape of US $$\hbox {CO}_2$$ CO 2 emissions in the long run are an increase in emissions caused by energy use and a decrease caused by more trade and the utilization of less polluting energy sources. GDP only exhibits short run effects. Hence, I do not find evidence in favor of a long-run relationship between economic development and the concave shape of emissions, as suggested by the environmental Kuznets curve.

show abstract

Monitoring Cointegrating Polynomial Regressions: Theory and Application to the Environmental Kuznets Curves for Carbon and Sulfur Dioxide Emissions

Cited by 4 publications

References 61 publications

Long run non-linearity in CO2 Emissions: The I(2) Cointegration Model and the Environmental Kuznets Curve

Long run non-linearity in CO2 Emissions: The I(2) Cointegration Model and the Environmental Kuznets Curve

A Bootstrap-Assisted Self-Normalization Approach to Inference in Cointegrating Regressions

Long run non-linearity in CO2 emissions: the I(2) cointegration model and the environmental Kuznets curve

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