Minimum distance approach to inference with many instruments

Integrated Likelihood Approach to Inference with Many Instruments

I analyze a Gaussian linear instrumental variables model with a single endogenous regressor in which the number of instruments is large. I use an invariance property of the model and a Bernstein-von Mises type argument to construct an integrated likelihood which by design yields inference procedures that are valid under many instrument asymptotics and are asymptotically optimal under rotation i...

Identification and Inference With Many Invalid Instruments

We study estimation and inference in settings where the interest is in the effect of a potentially endogenous regressor on some outcome. To address the endogeneity we exploit the presence of additional variables. Like conventional instrumental variables, these variables are correlated with the endogenous regressor. However, unlike conventional instrumental variables, they also have direct effec...

The Minimum Evolution Distance-based Approach to Phylogenetic Inference

Distance algorithms remain among the most popular for reconstructing phylogenies, especially for researchers faced with data sets with large numbers of taxa. Distance algorithms are much faster in practice than character or likelihood algorithms, and least-squares algorithms produce trees that have several desirable statistical properties. The fast Neighbor Joining heuristic has proven to be qu...

IDENTIFICATION AND INFERENCE WITH MANY INVALID INSTRUMENTS Michal

Measuring Musical Rhythm Similarity: Further Experiments with the Many-to-Many Minimum-Weight Matching Distance

Musical rhythms are represented as sequences of symbols. The sequences may be composed of binary symbols denoting either silent or monophonic sounded pulses, or ternary symbols denoting silent pulses and two types of sounded pulses made up of low-pitched (dum) and high-pitched (tak) sounds. Experiments are described that compare the effectiveness of the many-to-many minimum-weight matching betw...