Sentence examples for dummy demand from inspiring English sources
Step3: Add a dummy demand point/column with a ( mathrm{demand}=left left({displaystyle sum_{i=1}^m{a}_i+left(m+nright)T}right -P^{prime}right) ) or a dummy supply poinT}right -P^{prime}rightupply}=left(left({displaystyle sum_{j=m+1}^{m+n}{b}_j+left(m+nright)T}right)-P^{prime}right) ).
Step3: Add a dummy demand point/column with a ( mathrm{demand} = P^{prime }-left({displaystyle sum_{i=1}^m{a}_i+left(m+nright)T}right) ) or a dummy supply point/row with a ( mathrm{supply}=left P-left({displaystyle sum_{j=m+1}=left P-leftleft(m+nright)T}right)right) ).
Similarly in the supply-side equation, the P w is the WTI crude oil price,3 Pres is price of residual fuel oil, and P dst is price of distillate fuel oil to analyse the substitution effect in the production process,4 RI is refineries net input of crude oil, d d is dummy demand, and d s is dummy supply, and ω st comprise unexplained explanatory variables.
To identify the dummy for demand (d d ) and supply (d s ), we evaluated relative price from the following equations, where if ΔlpRetail Price − ΔlpConsumer Price Index > 0 indicates that the relative price is increasing and D > S which classifies d s, otherwise (ΔlpRetail Price − ΔlpConsumer Price Index < 0) there is a decrease in the relative price identifying that D < S and that indicates d d.
Roughly speaking, (j=0) denotes a dummy facility absorbing all demand not satisfied by the facilities of the firm or the competitor.
To establish reliability of this conclusion, we test if there is a statistically significant difference in the slopes of the curves in Fig. 8 by performing an ordinary least square (OLS) estimate the relationship between peak and nonpeak demand including the dummy variable and an interaction term between dummy and explanatory variable to control for the situation before and after January 2013.
Those at the low end served as lookouts in a communal sitting area; "pop-off dummies" delivered beatings on demand; and those at the highest ranks controlled housing areas.
By comparing the demand and supply dummies (d d and d s used in Eqs. 3 and 4) with the regimes, we identified that regime 0 is demand and regime 1 is supply regime.
x1 and x2 share the schooling variable and time dummies, since education is likely to have an effect on both the demand and supply components and year dummies take into consideration business cycle fluctuations.
The remaining three groups of variables are controls: DC includes terms of trade and year dummies to account for changes in the demand conditions.
First the categorical variables could be treated as such, but the additional dummy variables will make the already computationally demanding nature of MNAR modelling yet more so.
