English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Traditional inference in cointegrating regressions requires tuning parameterchoices to estimate a long-run variance parameter. Even in case these choicesare "optimal", the tests are severely size distorted. We propose a novelself-normalization approach, which leads to a nuisance parameter free limitingdistribution without estimating the long-run variance parameter directly. Thismakes our self-normalized test tuning parameter free and considerably lessprone to size distortions at the cost of only small power losses. Incombination with an asymptotically justified vector autoregressive sievebootstrap to construct critical values, the self-normalization approach showsfurther improvement in small to medium samples when the level of error serialcorrelation or regressor endogeneity is large. We illustrate the usefulness ofthe bootstrap-assisted self-normalized test in empirical applications byanalyzing the validity of the Fisher effect in Germany and the United States.

A Bootstrap-Assisted Self-Normalization Approach to Inference in Cointegrating Regressions