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From |
Austin Nichols <austinnichols@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Modeling repeated events with a continuous outcome |

Date |
Thu, 4 Jun 2009 11:54:26 -0400 |

Ian Sue Wing <isw@bu.edu> : How about -poisson- or -glm- with location fixed effects (and cluster by location)? Who is doing the investing? Multiple agents or one? Are people choosing from a finite set of locations to make a required investment, or picking a level of investment in each place more or less independently of their investments in other places? Do you have time-varying characteristics of locations and investors? You might want to hook up with an economist who runs this kind of model (there are a lot of them in your town) and is willing to discuss empirical strategies (fewer of those, I guess). Or google "fdi location" or somesuch to get a sense of what some folks do. The trouble is, there are a hundred different theoretical and statistical models that might fit what you describe below, and the empirical strategy should fit what you believe to be true about the data-generating process, and that last part comes from theory. You can relax the assumptions of -poisson- with fixed effects and robust SE in several directions, but I assert without proof that it is a good starting point. You model expected investment in location i at time t E(y_it) as a function exp(Xb) so you get estimates of point elasticities or semi-elasticities. My guess is that a dollar of investment is not much different than zero in your setting; if that is not true, maybe a different type of model is in order. On Thu, Jun 4, 2009 at 12:10 AM, Ian Sue Wing <isw@bu.edu> wrote: > Dear Statalisters, > > This posting is as much a plea for basic statistical advice as it is for > assistance with Stata, so I apologize in advance. > > I am trying to model the correlates of the timing and severity of repeated > events, in particular, how a particular type of investment at a number of > locations responds to local economic conditions over several years. > Investment occurs in "bursts". At given location there are typically > intervals of several years with zero investment, followed by one year in > which investment takes place. The quantity of investment, which I seek to > model, varies both across locations, and, for the ~10% of locations where I > investment occurs multiple times (< 4), over years. My covariates are > measured as panel data, with no censoring. > > Is anyone familiar with a statistical model which is appropriate for this > kind of process? I have looked for clues the medical literature, and the > closest fit I could find is a survival model of headache incidence in which > the outcome is classified into ordered categories: > > Berridge D M; Whitehead J (1991). Analysis of failure time data with ordinal > categories of response. Statistics in medicine 10:1703-10. > > I am currently awaiting a copy of this article via interlibrary loan, but I > fear that adapting such a framework to deal with continuous data is beyond > my competence. Looking closer to home, I could use -heckman- or -selmlog- to > estimate a linear model with site and year fixed effects that controls for > selection, but I am unclear whether these methods are able to capture the > *cumulative* impact of the covariates on the hazard of event occurrence over > the span of the inter-investment intervals. I can also average my covariates > over these intervals, convert my dataset to spell format, and estimate a > survival model, but this only solves half the problem. The issue then is how > to use the results of the survival analysis to account for fact that the > covariates jointly influence both the selection hazard *and* the magnitude > of investment once it occurs, e.g., through a quantity like the Inverse > Mills Ratio in -heckman, twostep-. > > Is this actually a simple problem that my own lack of statistical acumen is > making too complicated? It is hard for me to imagine that someone hasn't > dealt with a similar question before, and there must be something simple > that can be done short of developing and estimating an entire structural DP > model (which in this case is like Rust's (1987) bus engine replacement model > with engines of different sizes!). Any guidance from more experienced and > able researchers would be greatly appreciated. * * 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/

**References**:**st: Modeling repeated events with a continuous outcome***From:*Ian Sue Wing <isw@bu.edu>

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