Lag length selection and p-hacking in Granger causality testing: prevalence and performance of meta-regression models-Reference-Cited by-同舟云学术

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Lag length selection and p-hacking in Granger causality testing: prevalence and performance of meta-regression models

Published:2018-06-04 Issue:3 Volume:56 Page:797-830
ISSN:0377-7332
Container-title:Empirical Economics
language:en
Short-container-title:Empir Econ

Author:

Bruns Stephan B.,Stern David I.

Publisher

Springer Science and Business Media LLC

Subject

Economics and Econometrics,Social Sciences (miscellaneous),Mathematics (miscellaneous),Statistics and Probability

Link

http://link.springer.com/article/10.1007/s00181-018-1446-3/fulltext.html

Reference59 articles.

1. Adom PK (2011) Electricity consumption-economic growth nexus: the Ghanaian case. Int J Energy Econ Policy 1(1):18–31

2. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723

3. Alam M, Begum I, Buysse J, Rahman S, Van Huylenbroeck G (2011) Dynamic modeling of causal relationship between energy consumption, CO2 emissions and economic growth in India. Renew Sustain Energy Rev 15(6):3243–3251

4. Bowden N, Payne J (2009) The causal relationship between US energy consumption and real output: a disaggregated analysis. J Policy Model 31(2):180–188

5. Brodeur A, Le M, Sangnier M, Zylberberg Y (2016) Star wars: the empirics strike back. Am Econ J: Appl Econ 8(1):1–32

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