Unit Roots in Macroeconomic Time Series: A Comparison of Classical, Bayesian and Machine Learning Approaches

Abstract We compare the effectiveness of Classical, Bayesian, and Machine Learning (ML) methods for predicting the presence of a unit root in univariate time-series models. Framing the issue as a classification problem, we demonstrate how ML may be used to uncover structural features of a macroecono...
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Autor*in:	Ahmad, Yamin [verfasserIn] Check, Adam [verfasserIn] Lo, Ming Chien [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	k-nearest neighbors Random forest Supervised learning Support vector machines

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Computational economics - Springer US, 1988, 63(2023), 6 vom: 31. Mai, Seite 2139-2173
Übergeordnetes Werk:	volume:63 ; year:2023 ; number:6 ; day:31 ; month:05 ; pages:2139-2173

Links:	Volltext

DOI / URN:	10.1007/s10614-023-10397-0

Katalog-ID:	SPR056315058

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allfields	10.1007/s10614-023-10397-0 doi (DE-627)SPR056315058 (SPR)s10614-023-10397-0-e DE-627 ger DE-627 rakwb eng 330 650 004 VZ 85.03 bkl 31.80 bkl 54.10 bkl Ahmad, Yamin verfasserin (orcid)0000-0002-2849-153X aut Unit Roots in Macroeconomic Time Series: A Comparison of Classical, Bayesian and Machine Learning Approaches 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract We compare the effectiveness of Classical, Bayesian, and Machine Learning (ML) methods for predicting the presence of a unit root in univariate time-series models. Framing the issue as a classification problem, we demonstrate how ML may be used to uncover structural features of a macroeconomic time series with small data. We use a Monte Carlo approach to evaluate the predictions from these approaches and find that ML outperforms both the Classical and Bayesian tests using prediction accuracy, and appears to be the most flexible for classifying unit roots when class imbalance is present. In data, we find broad consensus among the approaches for predicted nonstationary series, with some disagreement for predicted stationary series. k-nearest neighbors (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Supervised learning (dpeaa)DE-He213 Support vector machines (dpeaa)DE-He213 Check, Adam verfasserin aut Lo, Ming Chien verfasserin aut Enthalten in Computational economics Springer US, 1988 63(2023), 6 vom: 31. Mai, Seite 2139-2173 (DE-627)270427546 (DE-600)1477445-8 1572-9974 nnns volume:63 year:2023 number:6 day:31 month:05 pages:2139-2173 https://dx.doi.org/10.1007/s10614-023-10397-0 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 85.03 VZ 31.80 VZ 54.10 VZ AR 63 2023 6 31 05 2139-2173
spelling	10.1007/s10614-023-10397-0 doi (DE-627)SPR056315058 (SPR)s10614-023-10397-0-e DE-627 ger DE-627 rakwb eng 330 650 004 VZ 85.03 bkl 31.80 bkl 54.10 bkl Ahmad, Yamin verfasserin (orcid)0000-0002-2849-153X aut Unit Roots in Macroeconomic Time Series: A Comparison of Classical, Bayesian and Machine Learning Approaches 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract We compare the effectiveness of Classical, Bayesian, and Machine Learning (ML) methods for predicting the presence of a unit root in univariate time-series models. Framing the issue as a classification problem, we demonstrate how ML may be used to uncover structural features of a macroeconomic time series with small data. We use a Monte Carlo approach to evaluate the predictions from these approaches and find that ML outperforms both the Classical and Bayesian tests using prediction accuracy, and appears to be the most flexible for classifying unit roots when class imbalance is present. In data, we find broad consensus among the approaches for predicted nonstationary series, with some disagreement for predicted stationary series. k-nearest neighbors (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Supervised learning (dpeaa)DE-He213 Support vector machines (dpeaa)DE-He213 Check, Adam verfasserin aut Lo, Ming Chien verfasserin aut Enthalten in Computational economics Springer US, 1988 63(2023), 6 vom: 31. Mai, Seite 2139-2173 (DE-627)270427546 (DE-600)1477445-8 1572-9974 nnns volume:63 year:2023 number:6 day:31 month:05 pages:2139-2173 https://dx.doi.org/10.1007/s10614-023-10397-0 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 85.03 VZ 31.80 VZ 54.10 VZ AR 63 2023 6 31 05 2139-2173
allfields_unstemmed	10.1007/s10614-023-10397-0 doi (DE-627)SPR056315058 (SPR)s10614-023-10397-0-e DE-627 ger DE-627 rakwb eng 330 650 004 VZ 85.03 bkl 31.80 bkl 54.10 bkl Ahmad, Yamin verfasserin (orcid)0000-0002-2849-153X aut Unit Roots in Macroeconomic Time Series: A Comparison of Classical, Bayesian and Machine Learning Approaches 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract We compare the effectiveness of Classical, Bayesian, and Machine Learning (ML) methods for predicting the presence of a unit root in univariate time-series models. Framing the issue as a classification problem, we demonstrate how ML may be used to uncover structural features of a macroeconomic time series with small data. We use a Monte Carlo approach to evaluate the predictions from these approaches and find that ML outperforms both the Classical and Bayesian tests using prediction accuracy, and appears to be the most flexible for classifying unit roots when class imbalance is present. In data, we find broad consensus among the approaches for predicted nonstationary series, with some disagreement for predicted stationary series. k-nearest neighbors (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Supervised learning (dpeaa)DE-He213 Support vector machines (dpeaa)DE-He213 Check, Adam verfasserin aut Lo, Ming Chien verfasserin aut Enthalten in Computational economics Springer US, 1988 63(2023), 6 vom: 31. Mai, Seite 2139-2173 (DE-627)270427546 (DE-600)1477445-8 1572-9974 nnns volume:63 year:2023 number:6 day:31 month:05 pages:2139-2173 https://dx.doi.org/10.1007/s10614-023-10397-0 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 85.03 VZ 31.80 VZ 54.10 VZ AR 63 2023 6 31 05 2139-2173
allfieldsGer	10.1007/s10614-023-10397-0 doi (DE-627)SPR056315058 (SPR)s10614-023-10397-0-e DE-627 ger DE-627 rakwb eng 330 650 004 VZ 85.03 bkl 31.80 bkl 54.10 bkl Ahmad, Yamin verfasserin (orcid)0000-0002-2849-153X aut Unit Roots in Macroeconomic Time Series: A Comparison of Classical, Bayesian and Machine Learning Approaches 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract We compare the effectiveness of Classical, Bayesian, and Machine Learning (ML) methods for predicting the presence of a unit root in univariate time-series models. Framing the issue as a classification problem, we demonstrate how ML may be used to uncover structural features of a macroeconomic time series with small data. We use a Monte Carlo approach to evaluate the predictions from these approaches and find that ML outperforms both the Classical and Bayesian tests using prediction accuracy, and appears to be the most flexible for classifying unit roots when class imbalance is present. In data, we find broad consensus among the approaches for predicted nonstationary series, with some disagreement for predicted stationary series. k-nearest neighbors (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Supervised learning (dpeaa)DE-He213 Support vector machines (dpeaa)DE-He213 Check, Adam verfasserin aut Lo, Ming Chien verfasserin aut Enthalten in Computational economics Springer US, 1988 63(2023), 6 vom: 31. Mai, Seite 2139-2173 (DE-627)270427546 (DE-600)1477445-8 1572-9974 nnns volume:63 year:2023 number:6 day:31 month:05 pages:2139-2173 https://dx.doi.org/10.1007/s10614-023-10397-0 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 85.03 VZ 31.80 VZ 54.10 VZ AR 63 2023 6 31 05 2139-2173
allfieldsSound	10.1007/s10614-023-10397-0 doi (DE-627)SPR056315058 (SPR)s10614-023-10397-0-e DE-627 ger DE-627 rakwb eng 330 650 004 VZ 85.03 bkl 31.80 bkl 54.10 bkl Ahmad, Yamin verfasserin (orcid)0000-0002-2849-153X aut Unit Roots in Macroeconomic Time Series: A Comparison of Classical, Bayesian and Machine Learning Approaches 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract We compare the effectiveness of Classical, Bayesian, and Machine Learning (ML) methods for predicting the presence of a unit root in univariate time-series models. Framing the issue as a classification problem, we demonstrate how ML may be used to uncover structural features of a macroeconomic time series with small data. We use a Monte Carlo approach to evaluate the predictions from these approaches and find that ML outperforms both the Classical and Bayesian tests using prediction accuracy, and appears to be the most flexible for classifying unit roots when class imbalance is present. In data, we find broad consensus among the approaches for predicted nonstationary series, with some disagreement for predicted stationary series. k-nearest neighbors (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Supervised learning (dpeaa)DE-He213 Support vector machines (dpeaa)DE-He213 Check, Adam verfasserin aut Lo, Ming Chien verfasserin aut Enthalten in Computational economics Springer US, 1988 63(2023), 6 vom: 31. Mai, Seite 2139-2173 (DE-627)270427546 (DE-600)1477445-8 1572-9974 nnns volume:63 year:2023 number:6 day:31 month:05 pages:2139-2173 https://dx.doi.org/10.1007/s10614-023-10397-0 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 85.03 VZ 31.80 VZ 54.10 VZ AR 63 2023 6 31 05 2139-2173
language	English
source	Enthalten in Computational economics 63(2023), 6 vom: 31. Mai, Seite 2139-2173 volume:63 year:2023 number:6 day:31 month:05 pages:2139-2173
sourceStr	Enthalten in Computational economics 63(2023), 6 vom: 31. Mai, Seite 2139-2173 volume:63 year:2023 number:6 day:31 month:05 pages:2139-2173
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	k-nearest neighbors Random forest Supervised learning Support vector machines
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container_title	Computational economics
authorswithroles_txt_mv	Ahmad, Yamin @@aut@@ Check, Adam @@aut@@ Lo, Ming Chien @@aut@@
publishDateDaySort_date	2023-05-31T00:00:00Z
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract We compare the effectiveness of Classical, Bayesian, and Machine Learning (ML) methods for predicting the presence of a unit root in univariate time-series models. Framing the issue as a classification problem, we demonstrate how ML may be used to uncover structural features of a macroeconomic time series with small data. We use a Monte Carlo approach to evaluate the predictions from these approaches and find that ML outperforms both the Classical and Bayesian tests using prediction accuracy, and appears to be the most flexible for classifying unit roots when class imbalance is present. 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author	Ahmad, Yamin
spellingShingle	Ahmad, Yamin ddc 330 bkl 85.03 bkl 31.80 bkl 54.10 misc k-nearest neighbors misc Random forest misc Supervised learning misc Support vector machines Unit Roots in Macroeconomic Time Series: A Comparison of Classical, Bayesian and Machine Learning Approaches
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topic_title	330 650 004 VZ 85.03 bkl 31.80 bkl 54.10 bkl Unit Roots in Macroeconomic Time Series: A Comparison of Classical, Bayesian and Machine Learning Approaches k-nearest neighbors (dpeaa)DE-He213 Random forest (dpeaa)DE-He213 Supervised learning (dpeaa)DE-He213 Support vector machines (dpeaa)DE-He213
topic	ddc 330 bkl 85.03 bkl 31.80 bkl 54.10 misc k-nearest neighbors misc Random forest misc Supervised learning misc Support vector machines
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title	Unit Roots in Macroeconomic Time Series: A Comparison of Classical, Bayesian and Machine Learning Approaches
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title_full	Unit Roots in Macroeconomic Time Series: A Comparison of Classical, Bayesian and Machine Learning Approaches
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title_sort	unit roots in macroeconomic time series: a comparison of classical, bayesian and machine learning approaches
title_auth	Unit Roots in Macroeconomic Time Series: A Comparison of Classical, Bayesian and Machine Learning Approaches
abstract	Abstract We compare the effectiveness of Classical, Bayesian, and Machine Learning (ML) methods for predicting the presence of a unit root in univariate time-series models. Framing the issue as a classification problem, we demonstrate how ML may be used to uncover structural features of a macroeconomic time series with small data. We use a Monte Carlo approach to evaluate the predictions from these approaches and find that ML outperforms both the Classical and Bayesian tests using prediction accuracy, and appears to be the most flexible for classifying unit roots when class imbalance is present. In data, we find broad consensus among the approaches for predicted nonstationary series, with some disagreement for predicted stationary series. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract We compare the effectiveness of Classical, Bayesian, and Machine Learning (ML) methods for predicting the presence of a unit root in univariate time-series models. Framing the issue as a classification problem, we demonstrate how ML may be used to uncover structural features of a macroeconomic time series with small data. We use a Monte Carlo approach to evaluate the predictions from these approaches and find that ML outperforms both the Classical and Bayesian tests using prediction accuracy, and appears to be the most flexible for classifying unit roots when class imbalance is present. In data, we find broad consensus among the approaches for predicted nonstationary series, with some disagreement for predicted stationary series. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract We compare the effectiveness of Classical, Bayesian, and Machine Learning (ML) methods for predicting the presence of a unit root in univariate time-series models. Framing the issue as a classification problem, we demonstrate how ML may be used to uncover structural features of a macroeconomic time series with small data. We use a Monte Carlo approach to evaluate the predictions from these approaches and find that ML outperforms both the Classical and Bayesian tests using prediction accuracy, and appears to be the most flexible for classifying unit roots when class imbalance is present. In data, we find broad consensus among the approaches for predicted nonstationary series, with some disagreement for predicted stationary series. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Unit Roots in Macroeconomic Time Series: A Comparison of Classical, Bayesian and Machine Learning Approaches
url	https://dx.doi.org/10.1007/s10614-023-10397-0
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author2	Check, Adam Lo, Ming Chien
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doi_str	10.1007/s10614-023-10397-0
up_date	2024-07-03T21:36:47.487Z
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Unit Roots in Macroeconomic Time Series: A Comparison of Classical, Bayesian and Machine Learning Approaches

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