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Aggregate Output Measurements: A Common Trend Approach

Gabriele Fiorentini (), Martin Almuzara and Enrique Sentana ()

No 962, Staff Reports from Federal Reserve Bank of New York

Abstract: 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: cointegration; GDP; GDI; overdifferencing; signal extraction (search for similar items in EconPapers)
JEL-codes: C32 E01 (search for similar items in EconPapers)
Pages: 38
Date: 2021-03-01
New Economics Papers: this item is included in nep-mac
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Working Paper: Aggregate Output Measurements: A Common Trend Approach (2021) Downloads
Working Paper: Aggregate Output Measurements: A Common Trend Approach (2021) Downloads
Working Paper: Aggregate Output Measurements: a Common Trend Approach (2021) Downloads
Working Paper: Aggregate output measurements: a common trend approach (2021) Downloads
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