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Below results based on the criteria 'limited dependent variables'
Total number of records returned: 2
Recent Developments in Econometric Modelling: A Personal Viewpoint
dynamic panel data models
dynamic models with limited dependent variables
The quotation above (more than three thousand years ago) essentially summarizes my perception of what is going on in econometrics. Dynamic economic modelling is a comprehensive term. It covers everything except pure cross-section analysis. Hence, I have to narrow down the scope of my paper. I shall not cover duration models, event studies, count data and Markovian models. The areas covered are: dynamic panel data models, dynamic models with limited dependent variables, unit roots, cointegration, VAR’s and Bayesian approaches to all these problems. These are areas I am most familiar with. Also, the paper is not a survey of recent developments. Rather, it presents what I feel are important issues in these areas. Also, as far as possible, I shall relate the issues with those considered in the work on Political Methodology. I have a rather different attitude towards econometric methods which my own colleagues in the profession may not share. In my opinion, there is too much technique and not enough discussion of why we are doing what we are doing. I am often reminded of the admonition of the queen to Pollonius in Shakespeare’s Hamlet, “More matter, less art.”
The Split Population Logit (SPopLogit): Modeling Measurement Bias in Binary Data
limited dependent variables
This study describes a split population logit model that can be useful to researchers who are modeling a binary dependent variable that is measured with a biased instrument. To motivate the study we identify two common, yet widely unrecognized, circumstances in which political scientists are likely to study dichotomous variables that have been measured with bias. In one such setting (e.g., surveys) the strategic interests of actors will lead them to misrepresent an attitude or behavior. In another such setting (e.g., content analysis of events) researchers' instruments are unable to distinguish between the absence of a characteristic or event and missing data. We briefly argue that "unobservability," "zero-inflated," and other models form a single class of models that allow researchers to model the bias in operational instruments, and thus not only correct bias in statistical inference but, more importantly, produce theoretical accounts of the bias and then test the hypotheses that those accounts imply. We derive the likelihood function for the split population logit model, describe the properties of its MLEs, present the results from a Monte Carlo study, and briefly describe code that researchers can use to implement the model in the Stata statistical package.