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We develop a new Bayesian estimator that is able to deal with multivariate panel data structure in the presence of spatial correlation. The analysis of panel data introduced here allows us to analyze not only the fixed effect but also the random effect model. This work extends the previous study...
Persistent link: https://www.econbiz.de/10012059270
This paper describes the inference procedures required to perform Bayesian inference to some multivariate econometric models. These models have a spatial component built into commonly used multivariate models. In particular, the seemingly unrelated regression and vector autoregressive models are...
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This paper gives a short overview of Monte Carlo studies on the usefulness of Heckman's (1976, 1979) twostep estimator for estimating a selection model. It shows that exploratory work to check for collinearity problems is strongly recommended before deciding on which estimator to apply. In the...
Persistent link: https://www.econbiz.de/10013428094
this case, the inclusion of the concept of the quasi maximum likelihood theory in the simulated z-test provides …
Persistent link: https://www.econbiz.de/10013428435
well as to the frequency of type II errors. In contrast, the inclusion of the quasi maximum likelihood theory into the … simulated likelihood ratio test leads to substantial computational problems. The combination of this theory with the simulated …
Persistent link: https://www.econbiz.de/10001678183
We design and implement an iterative, program-proposing deferred acceptance mechanism with ties (IDAT) and apply it to childcare assignment in two German cities. The mechanism can accommodate complementarities in providers' preferences, is fast to terminate even in larger cities, is difficult to...
Persistent link: https://www.econbiz.de/10012694261
setting, humans cannot verify the algorithm's quality. We, therefore, argue that algorithms exhibit traits of a credence good … intervention, we explain the way the algorithm functions. We find that while explanation helps participants recognize bias in the … algorithm, it remarkably decreases human decision-making performance. In the second treatment, we reveal the task's correct …
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