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From |
Suryadipta Roy <[email protected]> |

To |
[email protected] |

Subject |
Re: st: Interpretation of interaction term in log linear (non linear) model |

Date |
Sun, 23 Jun 2013 10:10:22 -0400 |

David, I forgot to mention that the results for my Poisson regressions with the entire sample (positive and zero trade values) were almost similar to that with the truncated sample (i.e. positive values only). However, I would add the results for the Probit regressions as you have suggested. Regards, Suryadipta. On Sun, Jun 23, 2013 at 9:51 AM, Suryadipta Roy <[email protected]> wrote: > Dear David, > Thank you very much for the references! I would read them very > carefully to understand the procedures better. The papers that have > used Poisson (I referred to in the thread before) did not go into > goodness of fit. I recently came across a paper that have used the > procedure that you have mentioned, i.e. of running a separate Probit > regression to explain propensity to import. I would try to do some > residual analysis to explain goodness of fit for my paper as you have > suggested. > > Best regards, > Suryadipta. > > On Fri, Jun 21, 2013 at 11:16 PM, David Hoaglin <[email protected]> wrote: >> Dear Suryadipta, >> >> Thank you for the compliment. As it happens, though, I have not had >> enough information about your data and your analysis to write anything >> approaching a referee's report. >> >> Assessing the fit of a model should involve much more than calculating >> a single number such as R-squared. One usually looks for influential >> data and makes a variety of plots of residuals. If the papers that >> have used Poisson models used data that had a substantial percentage >> of zeros, and did not do anything special about the zeros, I suspect >> that those models did not fit very well. Did the authors not give any >> empirical evidence on how well their models fit? >> >> If propensity to import could be treated as a binary outcome (positive >> imports versus zero imports), one part of the analysis could use >> logistic regression (or a probit model, if you prefer). >> >> If theory favors the use of a fixed effect for each trading pair, what >> does that theory say about how to handle the connections among trading >> pairs that involve the same country? >> >> The project in which we split each set of data into three parts >> produced several papers. The first of those papers is >> Pine M, Jordan HS, Elixhauser A, et al. Enhancement of claims data to >> improve risk adjustment of hospital mortality. Journal of the >> American Medical Association 2007; 297:71-76. >> The chapter on model assessment and selection in the book by Hastie, >> Tibshirani, and Friedman (2009) has some discussion of splitting a >> dataset into a training set (50%), a validation set (25%), and a test >> set (25%). >> >> David Hoaglin >> >> Hastie T, Tibshirani R, Friedman J (2009). The Elements of >> Statistical Learning. Springer. >> >> On Tue, Jun 18, 2013 at 6:05 PM, Suryadipta Roy <[email protected]> wrote: >>> Dear David, >>> >>> Thank you very much for the comments and the wonderful suggestions! >>> These almost read like a referee report! I had to take some time to >>> reply to your comments since the issues that you have raised are >>> substantive. Theoretical work in the gravity model of trade literature >>> mainly suggest the importance of structural factors that prevent >>> countries from trading with each other. Some of the important papers >>> have used -heckman- selection models but that model is more suitable >>> to explain why countries export (or do not export), while my research >>> question focuses on the propensity to import. Moreover, the exclusion >>> restrictions in the selection equation are still not very well >>> founded. The papers that have used Poisson models have not reported >>> the goodness of fit. -poisson- by itself reports a pseudo-rsquare >>> which is not comparable to the linear r-square, while -xtpoisson- or >>> -xtpqml- that I have implemented does not report any r-square. The >>> cluster-robust standard errors address both the problems of >>> overdispersion and serial correlation (Cameron and Trivedi, >>> Microeconometrics using Stata, 2010). It is theory here that guides >>> the use of fixed effects, e.g. I need to incorporate 5638 trading pair >>> fixed effects (since I have 76 countries with complete data, I can >>> have a maximum of 76*75 = 5700 trading pair relationships). >>> >>> Your suggestions on model building by splitting the data have been >>> extremely illuminating. However, I was wondering if you could give me >>> a bit more concrete suggestions as to how to go about it, e.g. could >>> you kindly give me the reference to the paper where you undertook the >>> data splitting approach so that I could read a bit more about it? >>> >>> Best regards, >>> Suryadipta. >> * >> * For searches and help try: >> * http://www.stata.com/help.cgi?search >> * http://www.stata.com/support/faqs/resources/statalist-faq/ >> * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Interpretation of interaction term in log linear (non linear) model***From:*Suryadipta Roy <[email protected]>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*David Hoaglin <[email protected]>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*Suryadipta Roy <[email protected]>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*David Hoaglin <[email protected]>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*Suryadipta Roy <[email protected]>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*David Hoaglin <[email protected]>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*Suryadipta Roy <[email protected]>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*David Hoaglin <[email protected]>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*Suryadipta Roy <[email protected]>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*David Hoaglin <[email protected]>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*Suryadipta Roy <[email protected]>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*David Hoaglin <[email protected]>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*Suryadipta Roy <[email protected]>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*David Hoaglin <[email protected]>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*Suryadipta Roy <[email protected]>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*David Hoaglin <[email protected]>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*Suryadipta Roy <[email protected]>

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