Indirect estimation of linear models with ordinal regressors. A Monte Carlo study and some empirical illustrations

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Dateien:
Aufrufstatistik

URI: http://nbn-resolving.de/urn:nbn:de:bsz:21-opus-21361
http://hdl.handle.net/10900/47456
Dokumentart: ResearchPaper
Date: 1998
Source: Tübinger Diskussionsbeiträge der Wirtschaftswissenschaftlichen Fakultät ; 155
Language: English
Faculty: 6 Wirtschafts- und Sozialwissenschaftliche Fakultät
Department: Wirtschaftswissenschaften
DDC Classifikation: 330 - Economics
Keywords: Latente Variable
Other Keywords:
Microeconometrics , Exogenous Variables with Ordinal Scale , Latent Variables , Indirect Estimation
License: Publishing license excluding print on demand
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Abstract:

This paper investigates the effects of ordinal regressors in linear regression models. Each ordered categorical variable is interpreted as a rough measurement of an underlying continuous variable as it is often done in microeconometrics for the dependent variable. It is shown that using ordinal indicators only leads to correct answers in a few special cases. In most situations, the usual estimators are biased. In order to estimate the parameters of the model consistently, the indirect estimation procedure suggested by Gourieroux et al. (1993) is applied. To demonstrate this method, first a simulation study is performed and then in a second step, two real data sets are used. In the latter case, continuous regressors are transformed into categorical variables to study the behavior of the estimation procedure. In general, the indirect estimators lead to adequate results.

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