Methods Workshop (3/10/07) Topic: Event Count Models

Getting Ready  Open STATA  Type “findit spost”  Download spost software for the version of STATA you are running  Type “findit outreg”  Download outreg software

Introduction  What are event counts?  Examples Number of uses of force per year Number of bills vetoed by the president per year Number of Supreme Court draft opinions circulated Number of people murdered by state (genocide)  Comparison to other data forms Dichotomous variables Event history

Motivation  OLS may produce biased, inconsistent, and inefficient estimates of event count data.  OLS makes predictions for negative Y values, while event counts are truncated at zero.  However, OLS models may be okay as the mean number of events in the event count series increases.

Types of Event Count Models  Poisson  Negative Binomial  Generalized Event Count  Truncated  Hurdle  Zero-inflated  Poisson Autoregressive Model

Poisson Assumptions Pr (y | μ) = (e -μ μ y )/y! Let y be a random variable indicating the number of times that an event has occurred during an interval of time E(y) = μ Var(y) = E(y) = μ (equidispersion) As μ increases, the probability of 0’s decreases As μ increases, the Poisson approximates a normal distribution (Long & Freese, 224). Events in non-overlapping time periods are independent.

Estimating a Poisson Model  Data: Long’s (1990) study of 915 biochemists and the number of papers published during graduate school.  Mean articles = 1.7; Figure 8.2 (Long, 1997: 220)  The Poisson model may not fit best because it under-predicts zeros and over-predicts in the 1-4 publication range.

Estimating Poisson in STATA  poisson art fem mar kid5 phd ment, nolog Poisson regression Number of obs = 915 LR chi2(5) = Prob > chi2 = Log likelihood = Pseudo R2 = art | Coef. Std. Err. z P>|z| [95% Conf. Interval] fem | mar | kid5 | phd | ment | _cons |  We can use a variety of tools to interpret the coefficients.

Interpreting Poisson Estimates  IRR: incidence rate ratios (add irr after comma in poisson command) Poisson regression Number of obs = 915 LR chi2(5) = Prob > chi2 = Log likelihood = Pseudo R2 = art | IRR Std. Err. z P>|z| [95% Conf. Interval] fem | mar | kid5 | phd | ment |  Married graduate students publish 1.17 more articles than single graduate students.  For every article published by a mentor, a graduate student publishes 1.03 more articles.

Interpreting Poisson Estimates  listcoef fem ment, help or listcoef fem ment, percent help poisson (N=915): Percentage Change in Expected Count Observed SD: art | b z P>|z| % %StdX SDofX fem | ment | b = raw coefficient z = z-score for test of b=0 P>|z| = p-value for z-test % = percent change in expected count for unit increase in X %StdX = percent change in expected count for SD increase in X SDofX = standard deviation of X  Being a female scientist decreases the expected number of articles by 20%, holding all other variables constant.

Interpreting Poisson Coefficients  You can use the mfx command in STATA to make point predictions for the counts.  mfx compute, at (mean fem=0)  Female (1.426), Male (1.785)  Married (1.697), Single(1.453)  Kids 0(1.764), 1(1.467), 2(1.219), 3(1.013)  You can also set more than one variable at a theoretically interesting value (e.g. mfx compute, at (mean fem=1 kid5=3).  You can also use the predict command to generate counts. See also Long and Freese’s description of several other substantive effects.

Negative Binomial  What if the rate of productivity, or μ, differs across individuals? This is known as heterogeneity.  Example: suppose men produce at a rate of μ + δ, while women produce at a rate of μ – δ. If there are equal numbers of men and women, then: [(μ + δ) + (μ – δ)]/2 > μ  When the variance exceeds the mean, as it does in this case, then we have a situation of over- dispersion. The Poisson model is not appropriate in this case, thus we estimate a negative binomial model.

Negative Binomial  The Poisson model would also be problematic if there is contagion in the data, where individuals with a given set of x’s initially have the same probability of an event occurring, but this probability changes as events occur (e.g. higher chance of publishing articles once you get the first 1 or 2 pubs.).  We can test for heterogeneity/contagion using the poisgof command or by examining the significant of the alpha parameter in the negative binomial model.

Negative Binomial  nbreg art fem mar kid5 phd ment, nolog Negative binomial regression Number of obs = 915 LR chi2(5) = Prob > chi2 = Log likelihood = Pseudo R2 = art | Coef. Std. Err. z P>|z| [95% Conf. Interval] fem | mar | kid5 | phd | ment | _cons | /lnalpha | alpha | Likelihood-ratio test of alpha=0: chibar2(01) = Prob>=chibar2 =  Interpretation of alpha: Poisson model assumes alpha equals zero; if p-value for chi-square test is less than.05, then the Negative Binomial model is preferred.

Comparing Models with Outreg  poisson art fem mar kid5 phd ment, nolog  outreg using c:\\outregexample  nbreg art fem mar kid5 phd ment, nolog  outreg using c:\\outregexample, append xstats  View output file, with a bit of manipulation it will look like the table in the handout.

Generalized Event Count  Special cases of the GEC (a) Negative Binomial, Var(y) > E(y) (b) Poission, Var(y) = E(y) (c) Continuous Parameter Binomial, Var(y) < E(y)  This model can be estimated using Gary King’s COUNT program.

Other Issues  Exposure: people might have different exposure times (e.g. years in PhD program); you can add an exposure command or add a variable capturing the natural log of exposure time. This could also be applied if there a maximum number of counts (use lny max ).  No zeros in your event count, e.g. observations enter the sample only after the first count occurs. Solution: use a truncated model  Different processes generating zeros: use hurdle count/split population model

Other Issues  Zero-inflated data with different processes generating zeros: use ZIP or ZINB Zero-inflated negative binomial regression Number of obs = 915 Nonzero obs = 640 Zero obs = 275 Inflation model = logit LR chi2(5) = Log likelihood = Prob > chi2 = art | Coef. Std. Err. z P>|z| [95% Conf. Interval] art | fem | mar | kid5 | phd | ment | _cons | inflate | fem | mar | kid5 | phd | ment | _cons | /lnalpha | alpha |

Interpretation of ZINB  listcoef, help  Top half of output represents scientists who have the opportunity to publish (e.g. Among those with the opportunity to publish, being a woman decreases the expected rate of publication by a factor of 0.91, holding all other factors constant).  Bottom half represents chance of being in the always zero group versus the not always zero group (e.g. Being a woman increases the odds of not having an opportunity to publish by a factor of 1.89).

Other Issues  Autocorrelation: if event count series shows persistence, use a PAR model; alpha parameter is misleading in this case  Plot autocorrelation function of event count series (ac command, need to tsset the data). Look for persistence in series.  PAR model can be estimated in R or Gauss; example in handout from Mitchell and Moore (2002)