Research Article Comparing the Forecast Performance of Advanced Statistical and Machine Learning Techniques Using Huge Big Data: Evidence from Monte Carlo Experiments Faridoon Khan, 1 Amena Urooj, 1 Saud Ahmed Khan, 1 Abdelaziz Alsubie, 2 Zahra Almaspoor , 3 and Sara Muhammadullah 1 1 PIDE School of Economics, Pakistan Institute of Development Economics, Islamabad, Pakistan 2 Department of Basic Sciences, College of Science and eoretical Studies, Saudi Electronic University, Riyadh, Saudi Arabia 3 Department of Statistics, Yazd University, Yazd 89175-741, Iran CorrespondenceshouldbeaddressedtoZahraAlmaspoor;z.almaspoor@stu.yazd.ac.ir Received 12 October 2021; Revised 17 November 2021; Accepted 30 November 2021; Published 14 December 2021 AcademicEditor:PauloJorgeSilveiraFerreira Copyright©2021FaridoonKhanetal.isisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. is research compares factor models based on principal component analysis (PCA) and partial least squares (PLS) with Autometrics, elastic smoothly clipped absolute deviation (E-SCAD), and minimax concave penalty (MCP) under different simulated schemes like multicollinearity, heteroscedasticity, and autocorrelation. e comparison is made with varying sample sizeandcovariates.Wefoundthatinthepresenceoflowandmoderatemulticollinearity,MCPoftenproducessuperiorforecasts incontrasttosmallsamplecase,whereasE-SCADremainsbetter.Inthecaseofhighmulticollinearity,thePLS-basedfactormodel remained dominant, but asymptotically the prediction accuracy of E-SCAD 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 largesamples,AutometricsprovidesasimilarforecastasMCP.Inthepresenceoflowandmoderateautocorrelation,MCPshows outstandingforecastingperformanceexceptforthesmallsamplecase,whereasE-SCADproducesaremarkableforecast.Inthe caseofextremeautocorrelation,E-SCADoutperformstherivaltechniquesunderboththesmallandmediumsamples,butfurther augmentationinsamplesizeenablesMCPforecastmoreaccuratecomparatively.Tocomparethepredictiveabilityofallmethods, wesplitthedataintotwohalves(i.e.,dataover1973–2007astrainingdataanddataover2008–2020astestingdata).Basedonthe root mean square error and mean absolute error, the PLS-based factor model outperforms the competitor models in terms of forecasting performance. 1. Introduction epredictionofmacroeconomicvariablesisveryimportant undermacroeconomicstudies,monetarypolicyanalysis,and environmental economics. Accurate forecasts induce sound insights into mechanisms of dynamic economies [1], more effective monetary policies [2], and better portfolio man- agement and hedging strategies [3]. In the data-rich envi- ronmentexistingthesedays,manymacroeconomicseriesare tracked by economists and decision-makers. Low-dimensional models often include some pre- specified economic covariates for instance vector autoregression and therefore have a complication in cap- turing the dynamic and complex patterns, which contain huge panels of time series [4]. It is a fact that missing im- portant variable(s) leads to an underspecified model, in- ducing biased results. ere is an intense need to propose updatedstatisticalmodelsandanalysisframeworkswiththe purposeofexpandingthelow-dimensionalcounterpartsfor improved forecasts. us, in the recent era, the analysis of “BigData”hasbecomethecoreofeconomicsresearch.is in turn has resulted in special attention being paid to the hugeclassoftechniquesthatareavailableinthedomainof machine learning, dimension reduction, and penalized Hindawi Complexity Volume 2021, Article ID 6117513, 11 pages https://doi.org/10.1155/2021/6117513