Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/284177 
Erscheinungsjahr: 
2023
Schriftenreihe/Nr.: 
Cardiff Economics Working Papers No. E2023/15
Verlag: 
Cardiff University, Cardiff Business School, Cardiff
Zusammenfassung: 
Macroeconomic researchers use a variety of estimators to parameterise their models empirically. One such is FIML; another is a form of indirect inference we term "informal" under which data features are "targeted" by the model -i.e. parameters are chosen so that model-simulated features replicate the data features closely. In this paper we show, based on Monte Carlo experiments, that in the small samples prevalent in macro data, both these methods produce high bias, while formal indirect inference, in which the joint probability of the data- generated auxiliary model is maximised under the model simulated distribution, produces low bias. We also show that FII gets this low bias from its high power in rejecting misspecified models, which comes in turn from the fact that this distribution is restricted by the modelspecified parameters, so sharply distinguishing it from rival misspecified models.
Schlagwörter: 
Moments
Indirect Inference
JEL: 
C12
C32
C52
Dokumentart: 
Working Paper

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