2014
DOI: 10.20955/wp.2014.046
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Metro Business Cycles

Abstract: We construct monthly economic activity indices for the 50 largest U.S. metropolitan statistical areas (MSAs) beginning in 1990. Each index is derived from a dynamic factor model based on twelve underlying variables capturing various aspects of metro area economic activity. To accommodate mixed-frequency data and di↵erences in data-publication lags, we estimate the dynamic factor model using a maximumlikelihood approach that allows for arbitrary patterns of missing data. Our indices highlight important similari… Show more

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Cited by 8 publications
(12 citation statements)
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“…A smaller literature uses variants of these mixed‐frequency econometric methods to measure specifically regional economic activity at a higher frequency than estimates provided by the official statistical agencies. Again rather than construct, separately for each region, higher frequency estimates of a latent “economic activity index” (e.g., as Crone & Clayton‐Matthews () do for 50 states, and Arias et al () do for 50 metropolitan statistical areas of the USA), our focus in the MF‐VAR is on producing higher frequency estimates of regional GVA itself. Our multi‐region focus also means another constraint arises.…”
Section: Mixed‐frequency Econometric Methodsmentioning
confidence: 99%
“…A smaller literature uses variants of these mixed‐frequency econometric methods to measure specifically regional economic activity at a higher frequency than estimates provided by the official statistical agencies. Again rather than construct, separately for each region, higher frequency estimates of a latent “economic activity index” (e.g., as Crone & Clayton‐Matthews () do for 50 states, and Arias et al () do for 50 metropolitan statistical areas of the USA), our focus in the MF‐VAR is on producing higher frequency estimates of regional GVA itself. Our multi‐region focus also means another constraint arises.…”
Section: Mixed‐frequency Econometric Methodsmentioning
confidence: 99%
“…Bank of St. Louis. For the MSAs, we use the monthly economic activity indices as developed by Arias et al, (2016) and available for download from the FRED database. These authors derived each of these indices from a DFM based on twelve underlying variables capturing various aspects of metro area economic activity.…”
Section: Datamentioning
confidence: 99%
“…The new metro indices developed by Aria et al, (2016) are based on a much broader set of variables than the few existing metro indices (as well as the state indices reported by the Federal Reserve Bank of Philadelphia, which are basically based on four labor-market variables). Arias et al, (2016) estimate the DFM by using a maximum-likelihood approach that allows for arbitrary patterns of missing data to accommodate mixed-frequency and differences in publication lags. These indices are stationary by design and hence, we apply the DFM-TV-SV directly on them without any further transformations.…”
Section: Datamentioning
confidence: 99%
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“…Areas emerging out of a conflict usually experience a high degree of uncertainty, which can stifle investment decisions beyond the short-run effect of conflict. 99 Thus, households and firms may choose to continue with what are now sub-optimal strategies after the end of conflict, due to uncertainty over whether peace will last. For example, households in Mozambique were still practising many of their wartime coping strategies three years after the 1992 ceasefire.…”
mentioning
confidence: 99%