{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T06:19:46Z","timestamp":1784614786703,"version":"3.55.0"},"reference-count":32,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:00:00Z","timestamp":1773187200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:00:00Z","timestamp":1773187200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Applied Econometrics"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>A collection of time series are \u201crelated\u201d if they follow similar stochastic processes and\/or they are statistically dependent. This paper proposes a related time series (RTS) forecasting model that exploits these relationships. The model's foundation is a set of univariate Gaussian autoregressions, one for each series, which are then augmented to incorporate stochastic volatility, heavy\u2010tailed innovations, additive outliers, time\u2010varying parameters and common factors. The model is estimated and forecasts are computed using Bayesian methods with hierarchical priors that pool information across series. Computationally efficient MCMC methods are proposed. The RTS model is applied to three datasets and yields encouraging pseudo\u2010out\u2010of\u2010sample forecasting results.<\/jats:p>","DOI":"10.1002\/jae.70050","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T14:16:03Z","timestamp":1773238563000},"page":"481-498","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Forecasting Related Time Series"],"prefix":"10.1002","volume":"41","author":[{"given":"Ulrich K.","family":"M\u00fcller","sequence":"first","affiliation":[{"name":"Department of Economics Princeton University  Princeton New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark W.","family":"Watson","sequence":"additional","affiliation":[{"name":"Department of Economics Princeton University  Princeton New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,3,11]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/66.2.229"},{"key":"e_1_2_8_3_1","volume-title":"Federal Reserve Bank of New York Liberty Street Economics","author":"Almuzara M.","year":"2022"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2023.105634"},{"issue":"1","key":"e_1_2_8_5_1","first-page":"2","article-title":"Are Phillips Curves Useful for Forecasting Inflation?","volume":"25","author":"Atkeson A.","year":"2001","journal-title":"Federal Reserve Bank of Minneapolis Quarterly Review"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/jae.2923"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/jae.1137"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.2307\/1271080"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/jae.2315"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1162\/rest_a_01213"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1504\/IJMMNO.2009.030090"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1111\/joes.12095"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.red.2004.10.009"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1468-0084.2011.00642.x"},{"key":"e_1_2_8_15_1","article-title":"The Asymptotic Equivalence of Ridge and Principal Component Regression With Many Predictors","author":"Mol C.","journal-title":"Econometrics and Statistics"},{"key":"e_1_2_8_16_1","doi-asserted-by":"crossref","unstructured":"delNegro M. andC.Otrok.2008.Dynamic Factor Models With Time\u2010Varying Parameters: Changes in International Business CyclesFederal Reserve Bank of New York Staff Report 326.","DOI":"10.2139\/ssrn.1136163"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1080\/07474938408800053"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/89.3.603"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.2307\/1912773"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1972.tb00912.x"},{"key":"e_1_2_8_21_1","volume-title":"Bayesian Data Analysis","author":"Gelman A.","year":"2004"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214504000001132"},{"key":"e_1_2_8_23_1","volume-title":"Forecasting, Structural Time Series Models and the Kalman Filter","author":"Harvey A. 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