Aggregate output measurements: A common trend approach - Martín Almuzara; Gabriele Fiorentini; Enrique Sentana (Federal Reserve Bank of New York, 2021)
We analyze a model for N different measurements of a persistent latent time series when measurement errors are mean-reverting, which implies a common trend among measurements. We study the consequences of overdifferencing, finding potentially large biases in maximum likelihood estimators of the dynamics parameters and reductions in the precision of smoothed estimates of the latent variable, especially for multiperiod objects such as quinquennial growth rates. We also develop an R2 measure of common trend observability that determines the severity of misspecification. Finally, we apply our framework to U.S. quarterly data on GDP and GDI, obtaining an improved aggregate output measure.
Keywords: C32; E01; 330; cointegration; GDP; GDI; overdifferencing; signal extraction