We introduce a method for scaling two data sets from different sources. The proposed method estimates a latent component common to both datasets as well as an idiosyncratic component unique to each. The scaled locations can be modeled as a function of covariates, and efficient implementation allows for inference through resampling methods. A simulation study shows that our proposed method outperforms existing alternatives in recovering latent dimensions. We employ our method to recover latent policy positions of Federal Open Market Committee (FOMC) members around the financial crisis of 2008 using both their speech and policy recommendations obtained from monetary policy meetings.

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