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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: Factor based estimation of discrete probability distribution (OPROBIT?) |

Date |
Tue, 22 Feb 2005 22:48:57 -0000 |

The ordered probit seems open to two objections: 1. month (really season) is a circular scale, not an ordered one. Clearly January follows December just as December follows January, but an ordered probit model would take no account of that fact. (That's assuming a conventional numbering 1 ... 12 but any other start to the year provokes a similar comment. 2. regarding season as a response seems the wrong way round to me. As you have you have a temperature variable as well, it is not clear that month as such is needed. In some other problems with circular predictors, using sine and cosine terms works well. On the whole, however, this looks more like a fairly standard regression problem with temperature as presumably the main forcing covariate, except that you may well need to try to capture various production and consumption influences as well; and the whole has a time series flavour. Nick n.j.cox@durham.ac.uk Harald Scheule, PhD > I am currently trying to model the monthly frequency distribution of > electricity consumption (e.g., 20% of the annual consumption > takes place in > February) based on explanatory variables such as temperature, > oil prices > etc.. > > I was thinking that an ordered probit model might work with > the month being > the dependent variable. The problem is that my dataset > includes historic > relative frequencies (e.g., January: 25%, February: 20%, > March: 10%, ..., > December:10%) rather than individual observations. Can I use > a weighting > function such as pweight to take the different frequencies > into account? Do > you think another model type might be more suitable? > * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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