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ANALYZING POLICY IMPACT: SELECTION OF A LINEAR TREND MODEL
Author(s) -
Newcomer Kathryn E.,
Hardy Richard J.
Publication year - 1980
Publication title -
policy studies journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.773
H-Index - 69
eISSN - 1541-0072
pISSN - 0190-292X
DOI - 10.1111/j.1541-0072.1980.tb01185.x
Subject(s) - multicollinearity , econometrics , autocorrelation , term (time) , dimension (graph theory) , per capita , series (stratigraphy) , linear model , time series , model selection , economics , trend analysis , regression analysis , statistics , mathematics , paleontology , population , physics , demography , quantum mechanics , sociology , pure mathematics , biology
Policy analysts, as well as politicians, have shown great interest in assessing both short‐term and long‐term consequences of public policies in recent years. Recent time‐trend studies have attempted to depict the time dimension of policy consequences through extensions of regression techniques. This study examines three linear trend models which have been used to depict policy impact through time‐series analyses, and identifies the relative advantages and disadvantages associated with the use of each model. The three models are applied in a quasi‐experimental time‐series design to time‐series of per capita state expenditures for large cities, in eight states, over a twenty‐year period. Differences in degrees of multicollinearity and autocorrelation inherent in the three models are discussed, and the model providing the most conservative coefficient estimates is identified.