Using deep (machine) learning to forecast US inflation in the COVID-19 era

Gespeichert in:

Autor*in:	Stoneman, David [verfasserIn] Duca, John V. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	deep learning forecasting inflation machine learning

Übergeordnetes Werk:	Enthalten in: Journal of forecasting - New York, NY : Wiley Interscience, 1982, 43(2024), 4 vom: Juli, Seite 894-902
Übergeordnetes Werk:	volume:43 ; year:2024 ; number:4 ; month:07 ; pages:894-902

Links:	Link aufrufen Link aufrufen

DOI / URN:	10.1002/for.3079

Katalog-ID:	1892001365

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allfields	10.1002/for.3079 doi (DE-627)1892001365 (DE-599)KXP1892001365 DE-627 ger DE-627 rda eng Stoneman, David verfasserin aut Using deep (machine) learning to forecast US inflation in the COVID-19 era David Stoneman, John V. Duca 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier deep learning (dpeaa)DE-206 forecasting (dpeaa)DE-206 inflation (dpeaa)DE-206 machine learning (dpeaa)DE-206 Duca, John V. verfasserin (DE-588)170812200 (DE-627)060948752 (DE-576)131662511 aut Enthalten in Journal of forecasting New York, NY : Wiley Interscience, 1982 43(2024), 4 vom: Juli, Seite 894-902 Online-Ressource (DE-627)314404422 (DE-600)2001645-1 (DE-576)095299890 1099-131X nnns volume:43 year:2024 number:4 month:07 pages:894-902 https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/for.3079 Verlag lizenzpflichtig https://doi.org/10.1002/for.3079 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 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_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_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 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_2190 GBV_ILN_2232 GBV_ILN_2336 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 43 2024 4 7 894-902 26 01 0206 4541063220 x1z 21-06-24 26 00 DE-206 The 2021–2022 surge in US inflation was unanticipated by the Survey of Professional Forecasters (SPF) and other macroeconomists and institutions. This study assesses whether nascent deep learning frameworks and methods more accurately project recent core personal consumption expenditures inflation. We create a recurrent neural network (RNN) to forecast long-term inflation, and after training on 60 years of quarterly data, the model outperforms the SPF and projects a spike in inflation similar to that of recent years. We compare the model's performance with and without COVID-19–specific data and discuss some implications of our findings for economic forecasting in global crises.
spelling	10.1002/for.3079 doi (DE-627)1892001365 (DE-599)KXP1892001365 DE-627 ger DE-627 rda eng Stoneman, David verfasserin aut Using deep (machine) learning to forecast US inflation in the COVID-19 era David Stoneman, John V. Duca 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier deep learning (dpeaa)DE-206 forecasting (dpeaa)DE-206 inflation (dpeaa)DE-206 machine learning (dpeaa)DE-206 Duca, John V. verfasserin (DE-588)170812200 (DE-627)060948752 (DE-576)131662511 aut Enthalten in Journal of forecasting New York, NY : Wiley Interscience, 1982 43(2024), 4 vom: Juli, Seite 894-902 Online-Ressource (DE-627)314404422 (DE-600)2001645-1 (DE-576)095299890 1099-131X nnns volume:43 year:2024 number:4 month:07 pages:894-902 https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/for.3079 Verlag lizenzpflichtig https://doi.org/10.1002/for.3079 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 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_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_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 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_2190 GBV_ILN_2232 GBV_ILN_2336 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 43 2024 4 7 894-902 26 01 0206 4541063220 x1z 21-06-24 26 00 DE-206 The 2021–2022 surge in US inflation was unanticipated by the Survey of Professional Forecasters (SPF) and other macroeconomists and institutions. This study assesses whether nascent deep learning frameworks and methods more accurately project recent core personal consumption expenditures inflation. We create a recurrent neural network (RNN) to forecast long-term inflation, and after training on 60 years of quarterly data, the model outperforms the SPF and projects a spike in inflation similar to that of recent years. We compare the model's performance with and without COVID-19–specific data and discuss some implications of our findings for economic forecasting in global crises.
allfields_unstemmed	10.1002/for.3079 doi (DE-627)1892001365 (DE-599)KXP1892001365 DE-627 ger DE-627 rda eng Stoneman, David verfasserin aut Using deep (machine) learning to forecast US inflation in the COVID-19 era David Stoneman, John V. Duca 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier deep learning (dpeaa)DE-206 forecasting (dpeaa)DE-206 inflation (dpeaa)DE-206 machine learning (dpeaa)DE-206 Duca, John V. verfasserin (DE-588)170812200 (DE-627)060948752 (DE-576)131662511 aut Enthalten in Journal of forecasting New York, NY : Wiley Interscience, 1982 43(2024), 4 vom: Juli, Seite 894-902 Online-Ressource (DE-627)314404422 (DE-600)2001645-1 (DE-576)095299890 1099-131X nnns volume:43 year:2024 number:4 month:07 pages:894-902 https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/for.3079 Verlag lizenzpflichtig https://doi.org/10.1002/for.3079 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 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_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_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 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_2190 GBV_ILN_2232 GBV_ILN_2336 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 43 2024 4 7 894-902 26 01 0206 4541063220 x1z 21-06-24 26 00 DE-206 The 2021–2022 surge in US inflation was unanticipated by the Survey of Professional Forecasters (SPF) and other macroeconomists and institutions. This study assesses whether nascent deep learning frameworks and methods more accurately project recent core personal consumption expenditures inflation. We create a recurrent neural network (RNN) to forecast long-term inflation, and after training on 60 years of quarterly data, the model outperforms the SPF and projects a spike in inflation similar to that of recent years. We compare the model's performance with and without COVID-19–specific data and discuss some implications of our findings for economic forecasting in global crises.
allfieldsGer	10.1002/for.3079 doi (DE-627)1892001365 (DE-599)KXP1892001365 DE-627 ger DE-627 rda eng Stoneman, David verfasserin aut Using deep (machine) learning to forecast US inflation in the COVID-19 era David Stoneman, John V. Duca 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier deep learning (dpeaa)DE-206 forecasting (dpeaa)DE-206 inflation (dpeaa)DE-206 machine learning (dpeaa)DE-206 Duca, John V. verfasserin (DE-588)170812200 (DE-627)060948752 (DE-576)131662511 aut Enthalten in Journal of forecasting New York, NY : Wiley Interscience, 1982 43(2024), 4 vom: Juli, Seite 894-902 Online-Ressource (DE-627)314404422 (DE-600)2001645-1 (DE-576)095299890 1099-131X nnns volume:43 year:2024 number:4 month:07 pages:894-902 https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/for.3079 Verlag lizenzpflichtig https://doi.org/10.1002/for.3079 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 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_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_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 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_2190 GBV_ILN_2232 GBV_ILN_2336 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 43 2024 4 7 894-902 26 01 0206 4541063220 x1z 21-06-24 26 00 DE-206 The 2021–2022 surge in US inflation was unanticipated by the Survey of Professional Forecasters (SPF) and other macroeconomists and institutions. This study assesses whether nascent deep learning frameworks and methods more accurately project recent core personal consumption expenditures inflation. We create a recurrent neural network (RNN) to forecast long-term inflation, and after training on 60 years of quarterly data, the model outperforms the SPF and projects a spike in inflation similar to that of recent years. We compare the model's performance with and without COVID-19–specific data and discuss some implications of our findings for economic forecasting in global crises.
allfieldsSound	10.1002/for.3079 doi (DE-627)1892001365 (DE-599)KXP1892001365 DE-627 ger DE-627 rda eng Stoneman, David verfasserin aut Using deep (machine) learning to forecast US inflation in the COVID-19 era David Stoneman, John V. Duca 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier deep learning (dpeaa)DE-206 forecasting (dpeaa)DE-206 inflation (dpeaa)DE-206 machine learning (dpeaa)DE-206 Duca, John V. verfasserin (DE-588)170812200 (DE-627)060948752 (DE-576)131662511 aut Enthalten in Journal of forecasting New York, NY : Wiley Interscience, 1982 43(2024), 4 vom: Juli, Seite 894-902 Online-Ressource (DE-627)314404422 (DE-600)2001645-1 (DE-576)095299890 1099-131X nnns volume:43 year:2024 number:4 month:07 pages:894-902 https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/for.3079 Verlag lizenzpflichtig https://doi.org/10.1002/for.3079 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_184 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_266 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_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_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 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_2190 GBV_ILN_2232 GBV_ILN_2336 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 43 2024 4 7 894-902 26 01 0206 4541063220 x1z 21-06-24 26 00 DE-206 The 2021–2022 surge in US inflation was unanticipated by the Survey of Professional Forecasters (SPF) and other macroeconomists and institutions. This study assesses whether nascent deep learning frameworks and methods more accurately project recent core personal consumption expenditures inflation. We create a recurrent neural network (RNN) to forecast long-term inflation, and after training on 60 years of quarterly data, the model outperforms the SPF and projects a spike in inflation similar to that of recent years. We compare the model's performance with and without COVID-19–specific data and discuss some implications of our findings for economic forecasting in global crises.
language	English
source	Enthalten in Journal of forecasting 43(2024), 4 vom: Juli, Seite 894-902 volume:43 year:2024 number:4 month:07 pages:894-902
sourceStr	Enthalten in Journal of forecasting 43(2024), 4 vom: Juli, Seite 894-902 volume:43 year:2024 number:4 month:07 pages:894-902
format_phy_str_mv	Article
building	26:1
institution	findex.gbv.de
selectbib_iln_str_mv	26@1z
topic_facet	deep learning forecasting inflation machine learning
sw_local_iln_str_mv	26: DE-206:
isfreeaccess_bool	false
container_title	Journal of forecasting
authorswithroles_txt_mv	Stoneman, David @@aut@@ Duca, John V. @@aut@@
publishDateDaySort_date	2024-07-01T00:00:00Z
hierarchy_top_id	314404422
id	1892001365
language_de	englisch
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author	Stoneman, David
spellingShingle	Stoneman, David misc deep learning misc forecasting misc inflation misc machine learning Using deep (machine) learning to forecast US inflation in the COVID-19 era
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topic_title	26 00 DE-206 The 2021–2022 surge in US inflation was unanticipated by the Survey of Professional Forecasters (SPF) and other macroeconomists and institutions. This study assesses whether nascent deep learning frameworks and methods more accurately project recent core personal consumption expenditures inflation. We create a recurrent neural network (RNN) to forecast long-term inflation, and after training on 60 years of quarterly data, the model outperforms the SPF and projects a spike in inflation similar to that of recent years. We compare the model's performance with and without COVID-19–specific data and discuss some implications of our findings for economic forecasting in global crises Using deep (machine) learning to forecast US inflation in the COVID-19 era David Stoneman, John V. Duca deep learning (dpeaa)DE-206 forecasting (dpeaa)DE-206 inflation (dpeaa)DE-206 machine learning (dpeaa)DE-206
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title_full	Using deep (machine) learning to forecast US inflation in the COVID-19 era David Stoneman, John V. Duca
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title_short	Using deep (machine) learning to forecast US inflation in the COVID-19 era
url	https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/for.3079 https://doi.org/10.1002/for.3079
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Using deep (machine) learning to forecast US inflation in the COVID-19 era

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