More Complex Regression Models

  • Michael O. Finkelstein
  • Bruce Levin
Part of the Statistics for Social and Behavioral Sciences book series (SSBS)


When observations of the dependent variable form a series over time, special problems may be encountered. Perhaps the most significant difference from the models discussed thus far is the use as an explanatory variable of the value of the dependent variable itself for the preceding period. A rationale for lagged dependent variables is that they account for excluded explanatory factors. Lagged dependent and independent variables may also be used to correct for “stickiness” in the response of the dependent variable to changes in explanatory factors. For example, in a price equation based on monthly prices, changes in cost or demand factors might affect price only after several months, so that regression estimates reflecting changes immediately would be too high or too low for a few months; the error term would be autocorrelated. A lagged value of the dependent variable might be used to correct for this. Note, however, that inclusion of a lagged dependent variable makes the regression essentially predict change in the dependent variable because the preceding period value is regarded as fixed; this may affect interpretation of the equation’s coefficients. For previous examples, see Sections  13.6.2 and  13.6.3.


Capital Gain Death Sentence Negative Binomial Regression Model District Court Murder Rate 

Copyright information

© Springer Science+Business Media, LLC 2015

Authors and Affiliations

  • Michael O. Finkelstein
    • 1
  • Bruce Levin
    • 2
  1. 1.New YorkUSA
  2. 2.Department of BiostatisticsColumbia University Mailman School of Public HealthNew YorkUSA

Personalised recommendations