{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T12:49:46Z","timestamp":1785934186920,"version":"3.56.0"},"reference-count":56,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T00:00:00Z","timestamp":1783296000000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Summary<\/jats:title>\n                  <jats:p>We adapt double machine learning to macroeconomic time series by combining regularized nuisance estimation with reverse cross-fitting. This deterministic scheme exploits time reversibility to use time-reversed auxiliary blocks and, unlike neighbour-deletion designs, avoids buffer blocks, thereby improving sample usage. We derive conditions for asymptotic validity and show in simulations that the estimator performs well in realistic finite samples across the designs considered, including cases with misspecification, heteroskedasticity, and state dependence. We also show that, in high dimensions, predictive tuning metrics do not minimize bias in the causal score. We therefore propose a calibration rule targeting a Goldilocks zone of tuning parameters delivering stable partialled-out signals and reduced small-sample bias. We extend the method to residualized local projections and apply it to estimate the dynamic effects of a rise in Tier 1 regulatory capital. The results illustrate the usefulness of the approach for macroeconomic time-series inference.<\/jats:p>","DOI":"10.1093\/ectj\/utag019","type":"journal-article","created":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T23:28:43Z","timestamp":1783380523000},"source":"Crossref","is-referenced-by-count":0,"title":["Double machine learning for time series"],"prefix":"10.1093","author":[{"given":"Milos","family":"Ciganovic","sequence":"first","affiliation":[{"name":"Departement of Economics and Law, Sapienza University of Rome , Via del Castro Laurenziano 9, 00161 Rome ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Federico","family":"D\u2019Amario","sequence":"additional","affiliation":[{"name":"Bank of England, Threadneedle Street , London EC2R 8AH ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Massimiliano","family":"Tancioni","sequence":"additional","affiliation":[{"name":"Departement of Economics and Law, Sapienza University of Rome , Via del Castro Laurenziano 9, 00161 Rome ,","place":["Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,7,6]]},"reference":[{"key":"2026080508341957200_bib1","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1093\/ectj\/utae012","article-title":"Local projection inference in high dimensions","volume":"27","author":"Adamek","year":"2024","journal-title":"Econometrics Journal"},{"key":"2026080508341957200_bib2","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1093\/ectj\/utae005","article-title":"Vaccination policy and mortality from COVID-19 in the European Union","volume":"27","author":"Agostini","year":"2024","journal-title":"Econometrics Journal"},{"key":"2026080508341957200_bib3","article-title":"An introduction to double\/debiased machine learning","author":"Ahrens","year":"2026","journal-title":"Journal of Economic Literature"},{"key":"2026080508341957200_bib4","doi-asserted-by":"crossref","first-page":"817","DOI":"10.2307\/2938229","article-title":"Heteroskedasticity and autocorrelation consistent covariance matrix estimation","volume":"59","author":"Andrews","year":"1991","journal-title":"Econometrica"},{"key":"2026080508341957200_bib5","first-page":"7353","article-title":"Recursive partitioning for heterogeneous causal effects","volume":"113","author":"Athey","year":"2016","journal-title":"Proceedings of the National Academy of Sciences of the USA"},{"key":"2026080508341957200_bib6","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1214\/18-AOS1709","article-title":"Generalized random forests","volume":"47","author":"Athey","year":"2019","journal-title":"Annals of Statistics"},{"key":"2026080508341957200_bib7","first-page":"1","article-title":"An assessment of the long-term economic impact of stronger capital and liquidity requirements","author":"BCBS","year":"2010"},{"issue":"2","key":"2026080508341957200_bib8","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1257\/jep.28.2.29","article-title":"High-dimensional methods and inference on structural and treatment effects","volume":"28","author":"Belloni","year":"2014","journal-title":"Journal of Economic Perspectives"},{"key":"2026080508341957200_bib9","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1093\/restud\/rdt044","article-title":"Inference on treatment effects after selection among high-dimensional controls","volume":"81","author":"Belloni","year":"2014","journal-title":"Review of Economic Studies"},{"key":"2026080508341957200_bib10","volume-title":"Convergence of Probability Measures","author":"Billingsley","year":"1968"},{"key":"2026080508341957200_bib11","article-title":"Challenges of the inconsistency regime: Novel debiasing methods for missing data models","author":"Celentano","year":"2023"},{"key":"2026080508341957200_bib12","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1093\/ectj\/utaa001","article-title":"Double\/debiased machine learning for difference-in-differences models","volume":"23","author":"Chang","year":"2020","journal-title":"Econometrics Journal"},{"key":"2026080508341957200_bib13","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1093\/biomet\/86.2.483","article-title":"Miscellanea. 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