Improving estimates accuracy of voter transitions
- JoseM Pavia,
- Rafael Romero
Abstract
The estimation of RxC ecological inference contingency tables from
aggregate data defines one of the most salient and challenging problems
in the field of quantitative social sciences. From the mathematical
programming framework, this paper suggests a new direction for tackling
this problem. For the first time in the literature, a procedure based on
linear programming is proposed to attain estimates of local contingency
tables. Based on this and the homogeneity hypothesis, we suggest two new
ecological inference algorithms. These two new algorithms represent an
important step forward in the ecological inference mathematical
programming literature. In addition to generating estimates for local
ecological inference contingency tables and amending the tendency to
produce extreme transfer probability estimates previously observed in
other mathematical programming procedures, they prove to be quite
competitive and more accurate than the current linear programming
baseline algorithm. The new algorithms place the linear programming
approach once again in a prominent position in the ecological inference
toolkit. We use a unique dataset with almost 500 elections, where the
real transfer matrices are known, to assess their accuracy. Interested
readers can easily use these new algorithms with the aid of the R
package lphom.