In the presence of fallible data, standard estimation and forecasting techniques are biased and inconsistent. Surprisingly, the magnitude of this bias tends to increase, arid not diminish, in time series applications as more observations become available. A solution to this ever-present problem, Ste
Correcting for bias in retrospective data
β Scribed by Ron Shachar; Zvi Eckstein
- Publisher
- John Wiley and Sons
- Year
- 2007
- Tongue
- English
- Weight
- 260 KB
- Volume
- 22
- Category
- Article
- ISSN
- 0883-7252
- DOI
- 10.1002/jae.939
No coin nor oath required. For personal study only.
β¦ Synopsis
Abstract
When panel data are not available, retrospective data are used in the estimation of dynamic choice models. However, retrospective data are not reliable. Previous studies of voting choices, for example, have shown that respondents misreport their past choices in order to appear more consistent with their current choice. Such retrospective bias leads to inconsistent estimates, especially when there is state dependence in choices. Specifically, observed persistence in retrospective data may be due to (a) true state dependence, (b) unobserved heterogeneity, and (c) retrospective bias in reporting previous choices. Whereas Heckman in his 1981 study deals with (a) and (b), we introduce a method to estimate true state dependence while accounting for both unobserved heterogeneity and retrospective reporting bias. Our method is based on modeling the reporting behavior and integrating it into the estimation. The identification strategy is based on the correlation between the reported previous choices and current exogenous variables. Using data on Israeli voters, we find that the probability that a respondent whose vote intention in 1991 differed from his or her past voting choices would lie about their past choices is 0.23. Copyright Β© 2007 John Wiley & Sons, Ltd.
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