Dataset Information

Estimating the causal relationship between electricity consumption and industrial output: ARDL bounds and Toda-Yamamoto approaches for ten late industrialized countries.

ABSTRACT: This research investigates the effects of electricity consumption (major independent variable), per capita income, real exchange rate, import and export on manufacturing output by using yearly time series data for the period of 1980-2016 with regard to 10 late industrialized nations. The ARDL bound testing approach, the way to deal with cointegration is applied to estimate the long-run connection between the variables. While, error correction method (ECM) is used to find the short-run dynamics. To test the causality among the variables, Toda-Yamamoto test is performed. The results demonstrate the existence of short-run and long-run relationship among the variables and Toda-Yamamoto causality results support the existence of growth, conservation, feedback and neutrality hypotheses for different nations. The difference in the results can be attributed to structural and macroeconomic parameters. In general, this research brings out a fresh lead of knowledge for late industrialized nations to strengthen their economic development through proficient utilization of energy consumption.

SUBMITTER: Sankaran A

PROVIDER: S-EPMC6600005 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Estimating the causal relationship between electricity consumption and industrial output: ARDL bounds and Toda-Yamamoto approaches for ten late industrialized countries.

Sankaran A A Kumar Sanjay S K Arjun A Das Mousumi M

Heliyon 20190626 6

This research investigates the effects of electricity consumption (major independent variable), per capita income, real exchange rate, import and export on manufacturing output by using yearly time series data for the period of 1980-2016 with regard to 10 late industrialized nations. The ARDL bound testing approach, the way to deal with cointegration is applied to estimate the long-run connection between the variables. While, error correction method (ECM) is used to find the short-run dynamics. ...[more]

PMID: 31304410

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Project description:BackgroundPremature discontinuation and other forms of noncompliance with treatment assignment can complicate causal inference of treatment effects in randomized trials. The intent-to-treat analysis gives unbiased estimates for causal effects of treatment assignment on outcome, but may understate potential benefit or harm of actual treatment. The corresponding upper confidence limit can also be underestimated.PurposeTo compare estimates of the hazard ratio and upper bound of the two-sided 95% confidence interval from causal inference methods that account for noncompliance with those from the intent-to-treat analysis.MethodsWe used simulations with parameters chosen to reflect cardiovascular safety trials of diabetes drugs, with a focus on upper bound estimates relative to 1.3, based on regulatory guidelines. A total of 1000 simulations were run under each parameter combination for a hypothetical trial of 10,000 total subjects randomly assigned to active treatment or control at 1:1 ratio. Noncompliance was considered in the form of treatment discontinuation and cross-over at specified proportions, with an assumed true hazard ratio of 0.9, 1, and 1.3, respectively. Various levels of risk associated with being a non-complier (independent of treatment status) were evaluated. Hazard ratio and upper bound estimates from causal survival analysis and intent-to-treat were obtained from each simulation and summarized under each parameter setting.ResultsCausal analysis estimated the true hazard ratio with little bias in almost all settings examined. Intent-to-treat was unbiased only when the true hazard ratio = 1; otherwise it underestimated both benefit and harm. When upper bound estimates from intent-to-treat were ≥1.3, corresponding estimates from causal analysis were also ≥1.3 in almost 100% of the simulations, regardless of the true hazard ratio. When upper bound estimates from intent-to-treat were <1.3 and the true hazard ratio = 1, corresponding upper bound estimates from causal analysis were ≥1.3 in up to 66% of the simulations under some settings.LimitationsSimulations cannot cover all scenarios for noncompliance in real randomized trials.ConclusionCausal survival analysis was superior to intent-to-treat in estimating the true hazard ratio with respect to bias in the presence of noncompliance. However, its large variance should be considered for safety upper bound exclusion especially when the true hazard ratio = 1. Our simulations provided a broad reference for practical considerations of bias-variance trade-off in dealing with noncompliance in cardiovascular safety trials of diabetes drugs. Further research is warranted for the development and application of causal inference methods in the evaluation of safety upper bounds.

Dataset Information

Estimating the causal relationship between electricity consumption and industrial output: ARDL bounds and Toda-Yamamoto approaches for ten late industrialized countries.

Publications

Estimating the causal relationship between electricity consumption and industrial output: ARDL bounds and Toda-Yamamoto approaches for ten late industrialized countries.

Similar Datasets

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