000			02071nam a22002417a 4500
003			ZW-GwMSU
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008			230627b |||||||| |||| 00| 0 eng d
022			_a13504851
040			_aMSU _cMSU _erda _bEnglish
050	0	0	_aHB1.A666 APP
100	1		_aCummins, M _eauthor
245	1	0	_aMultiple comparisons problem: _brecent advances applied to energy and emissions _ccreated by M. Cummins
264	1		_aNew York: _bTaylor and Francis, _c2013
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
440	1		_aApplied economics letters _vVolume 20, number 9
520	3		_aWithin the field of empirical finance, the econometric analysis of markets commonly suffers from the well-established problem of data-snooping bias, whereby there is a likelihood that statistically significant results may be identified by pure random chance alone. This is the multiple comparisons problem resulting from Multiple Hypothesis Testing (MHT). Recent advances in MHT techniques to control the multiple comparisons problem are uniquely showcased within a VAR and Granger causality testing of energy and emissions market interactions. Four generalized p-value-based MHT techniques show no evidence of interactions between European Union Allowance (EUA) prices and a range of energy prices – spanning key oil, gas, coal and electricity markets – over the first half or so (2008–2010) of Phase II of the EU Emissions Trading Scheme. The generalized familywise error rate procedures show evidence of regional and cross-regional interactions within European electricity markets. However, in contrast, the more conservative false discovery proportion procedures identify much fewer statistically significant relationship and, indeed, show little evidence of such cross-regional electricity market interactions.
650			_aMultiple comparisons problem _vMultiple hypothesis testing _xGeneralised familywise error rate
856			_uhttps://doi.org/10.1080/13504851.2012.761334
942			_2lcc _cJA
999			_c162715 _d162715