A Simple Method for Uniform Subvector Inference of Linear Instrumental Variables Models
SSRN Electronic Journal
2015
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- 1Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
We propose a procedure for testing simple hypotheses on a subset of the structural parameters in linear instrumental variables models. Our test is valid uniformly over a large class of distributions allowing for identification failure and heteroskedasticity. The large-sample distribution of our test statistic is shown to depend on a key quantity that cannot be consistently estimated. Under our proposed procedure, we construct a confidence set for this key quantity and then maximize, over this confidence set, the appropriate quantile of the large-sample distribution of the test statistic. This maximum is used as the critical value and Bonferroni correction is used to control the overall size of the test. Monte Carlo simulations demonstrate the advantage of our test over the projection method in finite samples.
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