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From |
Timothy Dang <[email protected]> |

To |
[email protected] |

Subject |
st: mlogit constraints and dropped variables |

Date |
Thu, 13 Jan 2005 12:38:37 -0700 (MST) |

Hello- I'm just getting started with Stata and trying to do a multinomial logit regression. It's a product choice regression over 3 products [indexed as 0, 1, 2] (plus the possibility of choosing none which is indexed as -1). I only want to consider product characteristics, not individual characteristics (it's data from a laboratory experiment). Also, I want the structure of the "utility" for each of the three products to be the same. So, I'm running mlogit with a lot of constraints, forcing some coefficients to zero and others to be equal. However, I'm getting some unexpectedly dropped variables. If I include the constants in the regression and try to set them to be equal (using constraint define 1 [0]_cons = [1]_cons constraint define 2 [0]_cons = [2]_cons) then one of the constants is estimated, but the other two are dropped. If I leave the constant out of the regression with noconstant, then other variables are dropped. My data is highly colinear, but not perfectly so. Most particularly, I have three variables (minprice0, minprice1, minprice2) where minpriceX is a flag which is 1 if productX has strictlty the lowest price and 0 otherwise. Since sometimes prices are equal, sometimes all three variables are zero. If I do the regression without the constant, it is the coefficients on these minpriceX variables which are dropped in two of the equations. I hope this is clear. I'll include actual sample code below. I'd be grateful for any advice. Thanks! *********************************************** set matsize 60 constraint define 1 [0]_cons = [1]_cons constraint define 2 [0]_cons = [2]_cons constraint define 3 [0]base0 = [1]base1 constraint define 4 [0]base0 = [2]base2 constraint define 5 [0]price0 = [1]price1 constraint define 6 [0]price0 = [2]price2 constraint define 7 [0]price0lag = [1]price1lag constraint define 8 [0]price0lag = [2]price2lag constraint define 9 [0]count0lag = [1]count1lag constraint define 10 [0]count0lag = [2]count2lag constraint define 11 [0]minprice0 = [1]minprice1 constraint define 12 [0]minprice0 = [2]minprice2 constraint define 13 [0]base1 = 0 constraint define 14 [0]base2 = 0 constraint define 15 [1]base0 = 0 constraint define 16 [1]base2 = 0 constraint define 17 [2]base0 = 0 constraint define 18 [2]base1 = 0 constraint define 19 [0]price1 = 0 constraint define 20 [0]price2 = 0 constraint define 21 [1]price0 = 0 constraint define 22 [1]price2 = 0 constraint define 23 [2]price0 = 0 constraint define 24 [2]price1 = 0 constraint define 25 [0]price1lag = 0 constraint define 26 [0]price2lag = 0 constraint define 27 [1]price0lag = 0 constraint define 28 [1]price2lag = 0 constraint define 29 [2]price0lag = 0 constraint define 30 [2]price1lag = 0 constraint define 31 [0]count1lag = 0 constraint define 32 [0]count2lag = 0 constraint define 33 [1]count0lag = 0 constraint define 34 [1]count2lag = 0 constraint define 35 [2]count0lag = 0 constraint define 36 [2]count1lag = 0 constraint define 37 [0]minprice1 = 0 constraint define 38 [0]minprice2 = 0 constraint define 39 [1]minprice0 = 0 constraint define 40 [1]minprice2 = 0 constraint define 41 [2]minprice0 = 0 constraint define 42 [2]minprice1 = 0 mlogit choice base0 base1 base2 price0 price0lag price1 price1lag price2 price2lag /// count0lag count1lag count2lag minprice0 minprice1 minprice2, base(-1) cons(1-42) *********************************************** ------------------------------ Timothy O'Neill Dang / Cretog8 520-884-7261 One monkey don't stop no show. ------------------------------ The opinions or statements expressed herein are my own and should not be taken as a position, opinion, or endorsement of the University of Arizona. * * 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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