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From |
Suryadipta Roy <sroy2138@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

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

Date |
Sun, 23 Jun 2013 09:51:08 -0400 |

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 <dchoaglin@gmail.com> 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 <sroy2138@gmail.com> 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/

**Follow-Ups**:**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*Suryadipta Roy <sroy2138@gmail.com>

**References**:**st: Interpretation of interaction term in log linear (non linear) model***From:*Suryadipta Roy <sroy2138@gmail.com>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*David Hoaglin <dchoaglin@gmail.com>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*Suryadipta Roy <sroy2138@gmail.com>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*David Hoaglin <dchoaglin@gmail.com>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*Suryadipta Roy <sroy2138@gmail.com>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*David Hoaglin <dchoaglin@gmail.com>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*Suryadipta Roy <sroy2138@gmail.com>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*David Hoaglin <dchoaglin@gmail.com>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*Suryadipta Roy <sroy2138@gmail.com>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*David Hoaglin <dchoaglin@gmail.com>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*Suryadipta Roy <sroy2138@gmail.com>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*David Hoaglin <dchoaglin@gmail.com>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*Suryadipta Roy <sroy2138@gmail.com>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*David Hoaglin <dchoaglin@gmail.com>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*Suryadipta Roy <sroy2138@gmail.com>

**Re: st: Interpretation of interaction term in log linear (non linear) model***From:*David Hoaglin <dchoaglin@gmail.com>

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