{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T16:36:04Z","timestamp":1769186164405,"version":"3.49.0"},"reference-count":47,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T00:00:00Z","timestamp":1641859200000},"content-version":"vor","delay-in-days":10,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":["Complexity"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:p>\n                    In order to reduce the dimensionality of parameter space and enhance out\u2010of\u2010sample forecasting performance, this research compares regularization techniques with Autometrics in time\u2010series modeling. We mainly focus on comparing weighted lag adaptive LASSO (WLAdaLASSO) with Autometrics, but as a benchmark, we estimate other popular regularization methods LASSO, AdaLASSO, SCAD, and MCP. For analytical comparison, we implement Monte Carlo simulation and assess the performance of these techniques in terms of out\u2010of\u2010sample Root Mean Square Error, Gauge, and Potency. The comparison is assessed with varying autocorrelation coefficients and sample sizes. The simulation experiment indicates that, compared to Autometrics and other regularization approaches, the WLAdaLASSO outperforms the others in covariate selection and forecasting, especially when there is a greater linear dependency between predictors. In contrast, the computational efficiency of Autometrics decreases with a strong linear dependency between predictors. However, under the large sample and weak linear dependency between predictors, the Autometrics potency \u27f6 1 and gauge \u27f6\n                    <jats:italic>\u03b1<\/jats:italic>\n                    . In contrast, LASSO, AdaLASSO, SCAD, and MCP select more covariates and possess higher RMSE than Autometrics and WLAdaLASSO. To compare the considered techniques, we made the Generalized Unidentified Model for covariate selection and out\u2010of\u2010sample forecasting for the trade balance of Pakistan. We train the model on 1985\u20132015 observations and 2016\u20132020 observations as test data for the out\u2010of\u2010sample forecast.\n                  <\/jats:p>","DOI":"10.1155\/2022\/2649205","type":"journal-article","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T13:55:33Z","timestamp":1641909333000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Comparison of Weighted Lag Adaptive LASSO with Autometrics for Covariate Selection and Forecasting Using Time\u2010Series Data"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7246-0431","authenticated-orcid":false,"given":"Sara","family":"Muhammadullah","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amena","family":"Urooj","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Faridoon","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed N","family":"Alshahrani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed","family":"Alqawba","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanaa","family":"Al-Marzouki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,1,11]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/b978-0-12-438150-6.50018-2"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1002\/jae.616"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/0304-4076(94)01644-f"},{"key":"e_1_2_8_4_2","doi-asserted-by":"publisher","DOI":"10.1142\/s021797922150171x"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2020.125434"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.07.125"},{"key":"e_1_2_8_7_2","doi-asserted-by":"publisher","DOI":"10.1111\/1468-0262.00273"},{"key":"e_1_2_8_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2008.08.011"},{"key":"e_1_2_8_9_2","volume-title":"Macroeconomic Forecasting Using Large Vector Auto Regressive Model","author":"Hua Y.","year":"2011"},{"key":"e_1_2_8_10_2","doi-asserted-by":"publisher","DOI":"10.2139\/SSRN.2017877"},{"key":"e_1_2_8_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2014.03.016"},{"key":"e_1_2_8_12_2","doi-asserted-by":"publisher","DOI":"10.2139\/SSRN.2531339"},{"key":"e_1_2_8_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2017.01.003"},{"key":"e_1_2_8_14_2","article-title":"Estimating High-Dimensional Time Series Models","author":"Medeiros M. 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K.","year":"2010","journal-title":"Lahore Journal of Economics"},{"key":"e_1_2_8_46_2","first-page":"13","article-title":"Determinant of balance of trade: case study of Pakistan","volume":"41","author":"Muhammad S. D.","year":"2010","journal-title":"European Journal of Scientific Research"},{"key":"e_1_2_8_47_2","volume-title":"Real Exchange Rate Changes and Trade Balance in Pakistan: A Revisit","author":"Shahbaz M.","year":"2010"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2022\/2649205","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1155\/2022\/2649205","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2022\/2649205","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T21:19:45Z","timestamp":1769116785000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2022\/2649205"}},"subtitle":[],"editor":[{"given":"Peican","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2022,1]]},"references-count":47,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["10.1155\/2022\/2649205"],"URL":"https:\/\/doi.org\/10.1155\/2022\/2649205","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"value":"1076-2787","type":"print"},{"value":"1099-0526","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1]]},"assertion":[{"value":"2021-11-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-12-23","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-01-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"2649205"}}