{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T04:11:11Z","timestamp":1789099871425,"version":"build-2803163510"},"reference-count":67,"publisher":"Cambridge University Press (CUP)","issue":"3","license":[{"start":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T00:00:00Z","timestamp":1740960000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Econom. Theory"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:p>\n                    We propose a family of\n                    <jats:italic>weighted<\/jats:italic>\n                    statistics based on the CUSUM process of the WLS residuals for the online detection of changepoints in a Random Coefficient Autoregressive model, using both the standard CUSUM and the Page-CUSUM process. We derive the asymptotics under the null of no changepoint for all possible weighing schemes, including the case of the standardized CUSUM, for which we derive a Darling\u2013Erd\u00f6s-type limit theorem; our results guarantee the procedure-wise size control under both an open-ended and a closed-ended monitoring. In addition to considering the standard RCA model with no covariates, we also extend our results to the case of exogenous regressors. Our results can be applied irrespective of (and with no prior knowledge required as to) whether the observations are stationary or not, and irrespective of whether they change into a stationary or nonstationary regime. Hence, our methodology is particularly suited to detect the onset, or the collapse, of a bubble or an epidemic. Our simulations show that our procedures, especially when standardising the CUSUM process, can ensure very good size control and short detection delays. We complement our theory by studying the online detection of breaks in epidemiological and housing prices series.\n                  <\/jats:p>","DOI":"10.1017\/s0266466625000052","type":"journal-article","created":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T04:14:02Z","timestamp":1740975242000},"page":"514-547","source":"Crossref","is-referenced-by-count":2,"title":["REAL-TIME MONITORING WITH RCA MODELS"],"prefix":"10.1017","volume":"42","author":[{"given":"Lajos","family":"Horv\u00e1th","sequence":"first","affiliation":[{"name":"University of Utah"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lorenzo","family":"Trapani","sequence":"additional","affiliation":[{"name":"Universita\u2019 di Pavia and University of Leicester"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2025,3,3]]},"reference":[{"key":"S0266466625000052_r36","doi-asserted-by":"publisher","DOI":"10.1016\/j.spl.2023.109829"},{"key":"S0266466625000052_r60","first-page":"155","article-title":"$\\sqrt{n}$\n\n\n-consistent estimation in a random coefficient autoregressive model","volume":"38","author":"Schick","year":"1996","journal-title":"Australian and New Zealand Journal of Statistics"},{"key":"S0266466625000052_r63","doi-asserted-by":"publisher","DOI":"10.1080\/07474938.2020.1773667"},{"key":"S0266466625000052_r51","volume-title":"Random coefficient autoregressive models: An introduction","volume":"11","author":"Nicholls","year":"2012"},{"key":"S0266466625000052_r45","doi-asserted-by":"publisher","DOI":"10.1080\/07350015.1996.10524673"},{"key":"S0266466625000052_r58","doi-asserted-by":"publisher","DOI":"10.3982\/QE82"},{"key":"S0266466625000052_r37","doi-asserted-by":"publisher","DOI":"10.1093\/ectj\/utae013"},{"key":"S0266466625000052_r44","doi-asserted-by":"publisher","DOI":"10.1002\/jae.2873"},{"key":"S0266466625000052_r11","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9892.2009.00615.x"},{"key":"S0266466625000052_r66","doi-asserted-by":"publisher","DOI":"10.1111\/biom.13462"},{"key":"S0266466625000052_r26","doi-asserted-by":"publisher","DOI":"10.1016\/j.jempfin.2016.11.001"},{"key":"S0266466625000052_r14","doi-asserted-by":"publisher","DOI":"10.1017\/S0266466620000067"},{"key":"S0266466625000052_r22","doi-asserted-by":"publisher","DOI":"10.1080\/02331888.2013.870568"},{"key":"S0266466625000052_r30","doi-asserted-by":"publisher","DOI":"10.1016\/j.jspi.2003.07.014"},{"key":"S0266466625000052_r64","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1987.10478471"},{"key":"S0266466625000052_r48","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2004.00432.x"},{"key":"S0266466625000052_r21","volume-title":"Nonlinear time series: Nonparametric and parametric methods","author":"Fan","year":"2008"},{"key":"S0266466625000052_r43","doi-asserted-by":"publisher","DOI":"10.1111\/jtsa.12236"},{"key":"S0266466625000052_r65","doi-asserted-by":"publisher","DOI":"10.1111\/obes.12540"},{"key":"S0266466625000052_r38","unstructured":"Horvath, L. , Trapani, L. , & Wang, S. 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