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From |
Arun Adhikari <aadhik2@tigers.lsu.edu> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: Root Mean Square Error and Pseudo R square....Please Help |

Date |
Sun, 26 Apr 2009 23:31:07 -0600 |

Dear All, While performing the simulation for Probit model, how could we calculate the RMSE and Pseudo R square? If it was OLS, we could just use the formula and could do it in Excel but I could not use the formula for Probit, since our Y is (0,1). There might be some code that would help me find these. I would appreciate if anybody could help me. My simulation so far looks like this. Could you please add something in it? .......................................Program............................................................ global numobs 1000 // sample size N global numsims "500" // number of simulations set seed 123456789 capture program drop endoprob program endoprob // , rclass version 10.1 drop _all set obs $numobs generate u = rnormal(0) generate mu = rnormal(0) generate v = rnormal(0) //generate x = rnormal(0) generate z1 = rnormal(0) // 4 Instruements generate z2 = rnormal(0) generate z3 = rnormal(0) generate z4 = rnormal(0) // Also be written *drawnorm u mu x1 z1 z2 z3 z4 generate a = 0.6*u generate x = 0.5 + 0.9*z1 + a regress x z1 // endogenous regressor with four instruments predict px generate y = 0.5 + px + mu + u > 2 //Reduced Form Equation ivprobit y (x= z1 ) predict py end simulate _b _se y py , /// reps($numsims): endoprob mean x* y* ......................................................................END............................................... Thanking you all in anticipation. Regards, Arun Adhikari * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**st: AW: Root Mean Square Error and Pseudo R square....Please Help***From:*"Martin Weiss" <martin.weiss1@gmx.de>

**st: RE: Root Mean Square Error and Pseudo R square....Please Help***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

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