{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T22:38:00Z","timestamp":1764715080645,"version":"3.41.2"},"reference-count":46,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T00:00:00Z","timestamp":1639440000000},"content-version":"vor","delay-in-days":347,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>This research compares factor models based on principal component analysis (PCA) and partial least squares (PLS) with Autometrics, elastic smoothly clipped absolute deviation (E\u2010SCAD), and minimax concave penalty (MCP) under different simulated schemes like multicollinearity, heteroscedasticity, and autocorrelation. The comparison is made with varying sample size and covariates. We found that in the presence of low and moderate multicollinearity, MCP often produces superior forecasts in contrast to small sample case, whereas E\u2010SCAD remains better. In the case of high multicollinearity, the PLS\u2010based factor model remained dominant, but asymptotically the prediction accuracy of E\u2010SCAD significantly enhances compared to other methods. Under heteroscedasticity, MCP performs very well and most of the time beats the rival methods. In some circumstances under large samples, Autometrics provides a similar forecast as MCP. In the presence of low and moderate autocorrelation, MCP shows outstanding forecasting performance except for the small sample case, whereas E\u2010SCAD produces a remarkable forecast. In the case of extreme autocorrelation, E\u2010SCAD outperforms the rival techniques under both the small and medium samples, but further augmentation in sample size enables MCP forecast more accurate comparatively. To compare the predictive ability of all methods, we split the data into two halves (i.e., data over 1973\u20132007 as training data and data over 2008\u20132020 as testing data). Based on the root mean square error and mean absolute error, the PLS\u2010based factor model outperforms the competitor models in terms of forecasting performance.<\/jats:p>","DOI":"10.1155\/2021\/6117513","type":"journal-article","created":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T21:35:05Z","timestamp":1639517705000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Comparing the Forecast Performance of Advanced Statistical and Machine Learning Techniques Using Huge Big Data: Evidence from Monte Carlo Experiments"],"prefix":"10.1155","volume":"2021","author":[{"given":"Faridoon","family":"Khan","sequence":"first","affiliation":[]},{"given":"Amena","family":"Urooj","sequence":"additional","affiliation":[]},{"given":"Saud Ahmed","family":"Khan","sequence":"additional","affiliation":[]},{"given":"Abdelaziz","family":"Alsubie","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2200-7372","authenticated-orcid":false,"given":"Zahra","family":"Almaspoor","sequence":"additional","affiliation":[]},{"given":"Sara","family":"Muhammadullah","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,12,14]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2008.08.010"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1162\/qjec.2005.120.1.387"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1093\/rfs\/hhp063"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2014.03.016"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1257\/jep.28.2.3"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1111\/caje.12336"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1080\/23322039.2015.1045216"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1002\/for.957"},{"key":"e_1_2_9_9_2","first-page":"117","article-title":"Undertanding and comparing factor based forecasts","volume":"1","author":"Boivin J.","year":"2005","journal-title":"International Journal of Central Banking"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2005.01.027"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1198\/016214504000002050"},{"key":"e_1_2_9_12_2","first-page":"478","article-title":"Diffusion index models and index proxies: recent results and new direction","volume":"3","author":"Armah N. 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