Discrete Choice Models
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Discrete Choice Models
2017-11-05
How to assess the fit of multilevel logit models with Stata?
http://d.repec.org/n?u=RePEc:boc:dsug17:05&r=dcm
Abstract: Stata 14 includes the multilevel model for binary (melogit) and ordinal logits (meologit). Unfortunately, except for the global Wald test of the estimated fixed effects, both models do not provide any fit measure to assess its practical significiance. Therefore, I developed an ado-file to calculate McFadden's and McKelvey and Zavoina's pseudo-R²s. It estimates the intraclass correlation (ICC) of the dependent variable for the actual sample to assess the maximum of the contextual effect. Since the early 1990s, a lot of Monte Carlo simulation studies (Hagle and Mitchell 1992; Veall and Zimmermann 1992, 1993, 1994; Windmeijer 1995; DeMaris 2002) proved that McKelvey and Zavoina pseudo-R² is the best one to assess the fit of binary and ordinal logit models. My ado-file calculates this fit in two complementary ways: first, for the fixed effects only, and second, for the fixed and random effects together. The estimation of McFadden's pseudo-R² uses two different zero models: first, the random-intercept-only model (RIOM) knowing the contextual units, and second, the fixed-intercept-only model (FIOM) ignoring the contextual units completely. For each of them, it calculates the global likelihood-ratio-chi2 test statistic whether all fixed effects or all fixed and random effects are zero in the population. An empirical study of drug consumption in European countries demonstrates the usefulness of my fit_meologit_2lev.ado or fit_meologit_3lev.ado files for multilevel binary and ordinal logit models.
Wolfgang Langer
2017-09-20
Identification of Treatment Effects under Conditional Partial Independence
http://d.repec.org/n?u=RePEc:arx:papers:1707.09563&r=dcm
Conditional independence of treatment assignment from potential outcomes is a commonly used but nonrefutable assumption. We derive identified sets for various treatment effect parameters under nonparametric deviations from this conditional independence assumption. These deviations are defined via a conditional treatment assignment probability, which makes it straightforward to interpret. Our results can be used to assess the robustness of empirical conclusions obtained under the baseline conditional independence assumption.
Matthew A. Masten
Alexandre Poirier
2017-07
"Institutionalization Aversion" and the Willingness to Pay for Home Health Care
http://d.repec.org/n?u=RePEc:ces:ceswps:_6703&r=dcm
We examine the presence of a systematic preference for independent living at old age which we refer as “institutionalization aversion” (IA). Given that IA is not observable from revealed preferences, we draw on a survey experiment to elicit individuals’ willingness to pay (WTP) to avoid institutionalization (e.g., in a nursing home), using a double-bounded referendum WTP format. Our results suggest robust evidence of IA and reveal a willingness to pay of up to 16% of respondent’s (individuals over fifty-five years of age) average income. We find that estimates of the willingness to pay to avoid institutionalization (or €292 at the time of the study) exceed the amount respondents are willing to pay for home health care at old age in the event of a mild impairment (€222). WTP estimates vary with income, age and especially, respondents’ housing conditions. Finally, we test the sensitivity of our estimates to anchoring effects and ‘yea-saying’ biases.
Joan Costa-i-Font
institutionalisation aversion, state-dependent preferences, home health care, willingness to pay, caregiving, referendum format
2017