Big data forecasting of South African inflation
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DOI: 10.1007/s00181-022-02329-y
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- Byron Botha & Rulof Burger & Kevin Kotze & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," School of Economics Macroeconomic Discussion Paper Series 2022-03, School of Economics, University of Cape Town.
- Byron Botha & Rulof Burger & Kevin Kotz & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," Working Papers 11022, South African Reserve Bank.
- Byron Botha & Kevin Kotze & Neil Rankin & Rulof P. Burger, 2022. "Big data forecasting of South African inflation," Working Papers 873, Economic Research Southern Africa.
References listed on IDEAS
- Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2021.
"Economic Predictions With Big Data: The Illusion of Sparsity,"
Econometrica, Econometric Society, vol. 89(5), pages 2409-2437, September.
- Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio, 2017. "Economic Predictions with Big Data: The Illusion Of Sparsity," CEPR Discussion Papers 12256, C.E.P.R. Discussion Papers.
- Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2018. "Economic predictions with big data: the illusion of sparsity," Staff Reports 847, Federal Reserve Bank of New York.
- Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio E., 2021. "Economic predictions with big data: the illusion of sparsity," Working Paper Series 2542, European Central Bank.
- Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2018. "Economic Predictions with Big Data: The Illusion of Sparsity," Liberty Street Economics 20180521, Federal Reserve Bank of New York.
- Kirstin Hubrich & David F. Hendry, 2005. "Forecasting Aggregates by Disaggregates," Computing in Economics and Finance 2005 270, Society for Computational Economics.
- Sebastian Doerr & Leonardo Gambacorta & José María Serena Garralda, 2021. "Big data and machine learning in central banking," BIS Working Papers 930, Bank for International Settlements.
- Geoffrey Woglom, 2005. "Forecasting South African Inflation," South African Journal of Economics, Economic Society of South Africa, vol. 73(2), pages 302-320, June.
- Jiahua Chen & Zehua Chen, 2008. "Extended Bayesian information criteria for model selection with large model spaces," Biometrika, Biometrika Trust, vol. 95(3), pages 759-771.
- Jeff Fuhrer & George Moore, 1995.
"Inflation Persistence,"
The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(1), pages 127-159.
- Jeffrey C. Fuhrer & George R. Moore, 1993. "Inflation persistence," Proceedings, Board of Governors of the Federal Reserve System (U.S.).
- Jeffrey C. Fuhrer & George R. Moore, 1993. "Inflation persistence," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
- Jeffrey C. Fuhrer & George R. Moore, 1993. "Inflation persistence," Finance and Economics Discussion Series 93-17, Board of Governors of the Federal Reserve System (U.S.).
- Michael W. McCracken & Serena Ng, 2016.
"FRED-MD: A Monthly Database for Macroeconomic Research,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
- Michael W. McCracken & Serena Ng, 2015. "FRED-MD: A Monthly Database for Macroeconomic Research," Working Papers 2015-12, Federal Reserve Bank of St. Louis.
- Stan Plessis & Gideon Rand & Kevin Kotzé, 2015.
"Measuring Core Inflation in South Africa,"
South African Journal of Economics, Economic Society of South Africa, vol. 83(4), pages 527-548, December.
- Gideon Du Rand & Kevin Kotze & Stan Du Plessis, 2015. "Measuring Core Inflation in South Africa," Working Papers 503, Economic Research Southern Africa.
- Koop, Gary & McIntyre, Stuart & Mitchell, James & Poon, Aubrey, 2021.
"Nowcasting ‘True’ Monthly U.S. Gdp During The Pandemic,"
National Institute Economic Review, National Institute of Economic and Social Research, vol. 256, pages 44-70, April.
- Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2021. "Nowcasting 'true' monthly US GDP during the pandemic," CAMA Working Papers 2021-14, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Sandra Eickmeier & Christina Ziegler, 2008. "How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 237-265.
- P. Richard Hahn & Carlos M. Carvalho, 2015. "Decoupling Shrinkage and Selection in Bayesian Linear Models: A Posterior Summary Perspective," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 435-448, March.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022.
"How is machine learning useful for macroeconomic forecasting?,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Papers 2008.12477, arXiv.org.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Working Papers 20-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Aug 2020.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2011.
"Inference on Treatment Effects After Selection Amongst High-Dimensional Controls,"
Papers
1201.0224, arXiv.org, revised May 2012.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "Inference on treatment effects after selection amongst high-dimensional controls," CeMMAP working papers 26/13, Institute for Fiscal Studies.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2012. "Inference on treatment effects after selection amongst high-dimensional controls," CeMMAP working papers 10/12, Institute for Fiscal Studies.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2012. "Inference on treatment effects after selection amongst high-dimensional controls," CeMMAP working papers CWP10/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "Inference on treatment effects after selection amongst high-dimensional controls," CeMMAP working papers CWP26/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Scott R. Baker & Nicholas Bloom & Steven J. Davis & Stephen J. Terry, 2020. "COVID-Induced Economic Uncertainty," NBER Working Papers 26983, National Bureau of Economic Research, Inc.
- Emi Nakamura & Jón Steinsson, 2013.
"Price Rigidity: Microeconomic Evidence and Macroeconomic Implications,"
Annual Review of Economics, Annual Reviews, vol. 5(1), pages 133-163, May.
- Emi Nakamura & Jón Steinsson, 2013. "Price Rigidity: Microeconomic Evidence and Macroeconomic Implications," NBER Working Papers 18705, National Bureau of Economic Research, Inc.
- Joseph, Andreas & Potjagailo, Galina & Chakraborty, Chiranjit & Kapetanios, George, 2024.
"Forecasting UK inflation bottom up,"
International Journal of Forecasting, Elsevier, vol. 40(4), pages 1521-1538.
- Joseph, Andreas & Kalamara, Eleni & Kapetanios, George & Potjagailo, Galina & Chakraborty, Chiranjit, 2021. "Forecasting UK inflation bottom up," Bank of England working papers 915, Bank of England, revised 27 Sep 2022.
- Clark, Todd E. & West, Kenneth D., 2007.
"Approximately normal tests for equal predictive accuracy in nested models,"
Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
- Todd E. Clark & Kenneth D. West, 2005. "Approximately normal tests for equal predictive accuracy in nested models," Research Working Paper RWP 05-05, Federal Reserve Bank of Kansas City.
- Kenneth D. West & Todd Clark, 2006. "Approximately Normal Tests for Equal Predictive Accuracy in Nested Models," NBER Technical Working Papers 0326, National Bureau of Economic Research, Inc.
- Wolters Maik H. & Tillmann Peter, 2015.
"The changing dynamics of US inflation persistence: a quantile regression approach,"
Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(2), pages 161-182, April.
- Peter Tillmann & Maik H. Wolters, 2012. "The changing dynamics of US inflation persistence: a quantile regression approach," MAGKS Papers on Economics 201206, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
- Tillmann, Peter & Wolters, Maik H., 2014. "The changing dynamics of US inflation persistence: A quantile regression approach," Economics Working Papers 2014-09, Christian-Albrechts-University of Kiel, Department of Economics.
- Tillmann, Peter & Wolters, Maik H., 2014. "The changing dynamics of US inflation persistence: A quantile regression approach," Kiel Working Papers 1951, Kiel Institute for the World Economy (IfW Kiel).
- Tillmann, Peter & Wolters, Maik Hendrik, 2012. "The changing dynamics of US inflation persistence: A quantile regression approach," IMFS Working Paper Series 60, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
- Mehmet Balcilar & Rangan Gupta & Kevin Kotzé, 2017.
"Forecasting South African macroeconomic variables with a Markov-switching small open-economy dynamic stochastic general equilibrium model,"
Empirical Economics, Springer, vol. 53(1), pages 117-135, August.
- Mehmet Balcilar & Rangan Gupta & Kevin Kotze, 2016. "Forecasting South African Macroeconomic Variables with a Markov-Switching Small Open-Economy Dynamic Stochastic General Equilibrium Model," Working Papers 201603, University of Pretoria, Department of Economics.
- Mehmet Balcilar & Rangan Gupta & Kevin Kotze, 2016. "Forecasting South African Macroeconomic Variables with a Markov-Switching Small Open-Economy Dynamic Stochastic General Equilibrium Model," School of Economics Macroeconomic Discussion Paper Series 2016-05, School of Economics, University of Cape Town.
- Elliott, Graham & Gargano, Antonio & Timmermann, Allan, 2013.
"Complete subset regressions,"
Journal of Econometrics, Elsevier, vol. 177(2), pages 357-373.
- Elliott, Graham & Gargano, Antonio & Timmermann, Allan, 2013. "Complete subset regressions," University of California at San Diego, Economics Working Paper Series qt1st3n7z7, Department of Economics, UC San Diego.
- repec:ulb:ulbeco:2013/13388 is not listed on IDEAS
- Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
- Schmitt-Grohe, Stephanie & Uribe, Martin, 2004.
"Solving dynamic general equilibrium models using a second-order approximation to the policy function,"
Journal of Economic Dynamics and Control, Elsevier, vol. 28(4), pages 755-775, January.
- Stephanie Schmitt-Grohe & Martin Uribe, 2001. "Solving Dynamic General Equilibrium Models Using a Second-Order Approximation to the Policy Function," Departmental Working Papers 200106, Rutgers University, Department of Economics.
- Uribe, MartÃn & Schmitt-Grohé, Stephanie, 2001. "Solving Dynamic General Equilibrium Models Using a Second-Order Approximation to the Policy Function," CEPR Discussion Papers 2963, C.E.P.R. Discussion Papers.
- Stephanie Schmitt-Grohe & Martin Uribe, 2002. "Solving Dynamic General Equilibrium Models Using a Second-Order Approximation to the Policy Function," NBER Technical Working Papers 0282, National Bureau of Economic Research, Inc.
- Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010.
"Large Bayesian vector auto regressions,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
- Reichlin, Lucrezia & Giannone, Domenico & Banbura, Marta, 2007. "Bayesian VARs with Large Panels," CEPR Discussion Papers 6326, C.E.P.R. Discussion Papers.
- Martha Banbura & Domenico Giannone & Lucrezia Reichlin, 2008. "Large Bayesian VARs," Working Papers ECARES 2008_033, ULB -- Universite Libre de Bruxelles.
- Marta Bańbura, 2008. "Large Bayesian VARs," 2008 Meeting Papers 334, Society for Economic Dynamics.
- Ba M. Chu & Kim Huynh & David T. Jacho-Chávez & Oleksiy Kryvtsov, 2018. "On the Evolution of the United Kingdom Price Distributions," Staff Working Papers 18-25, Bank of Canada.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014.
"High-Dimensional Methods and Inference on Structural and Treatment Effects,"
Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "High dimensional methods and inference on structural and treatment effects," CeMMAP working papers CWP59/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "High dimensional methods and inference on structural and treatment effects," CeMMAP working papers 59/13, Institute for Fiscal Studies.
- Goulet Coulombe, Philippe & Marcellino, Massimiliano & Stevanović, Dalibor, 2021.
"Can Machine Learning Catch The Covid-19 Recession?,"
National Institute Economic Review, National Institute of Economic and Social Research, vol. 256, pages 71-109, May.
- Marcellino, Massimiliano & Stevanovic, Dalibor & Goulet Coulombe, Philippe, 2021. "Can Machine Learning Catch the COVID-19 Recession?," CEPR Discussion Papers 15867, C.E.P.R. Discussion Papers.
- Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2021. "Can Machine Learning Catch the COVID-19 Recession?," CIRANO Working Papers 2021s-09, CIRANO.
- Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2021. "Can Machine Learning Catch the COVID-19 Recession?," Working Papers 21-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
- Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2021. "Can Machine Learning Catch the COVID-19 Recession?," Papers 2103.01201, arXiv.org.
- A. Belloni & V. Chernozhukov & L. Wang, 2011. "Square-root lasso: pivotal recovery of sparse signals via conic programming," Biometrika, Biometrika Trust, vol. 98(4), pages 791-806.
- Bryan Kelly & Seth Pruitt, 2013. "Market Expectations in the Cross-Section of Present Values," Journal of Finance, American Finance Association, vol. 68(5), pages 1721-1756, October.
- Litterman, Robert B, 1986.
"Forecasting with Bayesian Vector Autoregressions-Five Years of Experience,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
- Robert B. Litterman, 1985. "Forecasting with Bayesian vector autoregressions five years of experience," Working Papers 274, Federal Reserve Bank of Minneapolis.
- Patrick T. Kanda & Mehmet Balcilar & Pejman Bahramian & Rangan Gupta, 2016.
"Forecasting South African inflation using non-linearmodels: a weighted loss-based evaluation,"
Applied Economics, Taylor & Francis Journals, vol. 48(26), pages 2412-2427, June.
- Pejman Bahramian & Mehmet Balcilar & Rangan Gupta & Patrick T. kanda, 2014. "Forecasting South African Inflation Using Non-Linear Models: A Weighted Loss-Based Evaluation," Working Papers 15-19, Eastern Mediterranean University, Department of Economics.
- Patrick T. kanda & Mehmet Balcilar & Pejman Bahramian & Rangan Gupta, 2014. "Forecasting South African Inflation Using Non-Linear Models: A Weighted Loss-Based Evaluation," Working Papers 201416, University of Pretoria, Department of Economics.
- Klenow, Peter J. & Malin, Benjamin A., 2010.
"Microeconomic Evidence on Price-Setting,"
Handbook of Monetary Economics, in: Benjamin M. Friedman & Michael Woodford (ed.), Handbook of Monetary Economics, edition 1, volume 3, chapter 6, pages 231-284,
Elsevier.
- Peter J. Klenow & Benjamin A. Malin, 2010. "Microeconomic Evidence on Price-Setting," NBER Working Papers 15826, National Bureau of Economic Research, Inc.
- Daleen Smal & Coen Pretorius & Nelene Ehlers, 2007. "The core forecasting model of the South African Reserve Bank," Working Papers 3195, South African Reserve Bank.
- Gupta, Rangan & Steinbach, Rudi, 2013. "A DSGE-VAR model for forecasting key South African macroeconomic variables," Economic Modelling, Elsevier, vol. 33(C), pages 19-33.
- James H. Stock & Mark W. Watson, 2010.
"Modeling inflation after the crisis,"
Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 173-220.
- James H. Stock & Mark W. Watson, 2010. "Modeling Inflation After the Crisis," NBER Working Papers 16488, National Bureau of Economic Research, Inc.
- Franz Ruch & Neil Rankin & Stan du Plessis, 2016. "Decomposing inflation using micropricelevel data South Africas pricing dynamics," Working Papers 7353, South African Reserve Bank.
- Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000.
"The Generalized Dynamic-Factor Model: Identification And Estimation,"
The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
- Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 1999. "The Generalized Dynamic Factor Model: Identification and Estimation," CEPR Discussion Papers 2338, C.E.P.R. Discussion Papers.
- Mario Forni & Marc Hallin & Lucrezia Reichlin & Marco Lippi, 2000. "The generalised dynamic factor model: identification and estimation," ULB Institutional Repository 2013/10143, ULB -- Universite Libre de Bruxelles.
- Alberto Cavallo, 2024.
"Inflation with Covid Consumption Baskets,"
IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 72(2), pages 902-917, June.
- Alberto Cavallo, 2020. "Inflation with Covid Consumption Baskets," NBER Working Papers 27352, National Bureau of Economic Research, Inc.
- A. Belloni & V. Chernozhukov & I. Fernández‐Val & C. Hansen, 2017.
"Program Evaluation and Causal Inference With High‐Dimensional Data,"
Econometrica, Econometric Society, vol. 85, pages 233-298, January.
- Alexandre Belloni & Victor Chernozhukov & Ivan Fern'andez-Val & Christian Hansen, 2013. "Program Evaluation and Causal Inference with High-Dimensional Data," Papers 1311.2645, arXiv.org, revised Jan 2018.
- Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2016. "Program evaluation and causal inference with high-dimensional data," CeMMAP working papers 13/16, Institute for Fiscal Studies.
- Alexandre Belloni & Victor Chernozhukov & Ivan Fernandez-Val & Christian Hansen, 2016. "Program evaluation and causal inference with high-dimensional data," CeMMAP working papers CWP13/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Carvalho, V & Garcia, Juan R. & Hansen, S. & Ortiz, A. & Rodrigo, T. & More, J. V. R., 2020.
"Tracking the COVID-19 Crisis with High-Resolution Transaction Data,"
Cambridge Working Papers in Economics
2030, Faculty of Economics, University of Cambridge.
- Hansen, Stephen & Carvalho, Vasco & GarcÃa, Juan Ramón & Ortiz, Alvaro & Rodrigo, Tomasa & RodrÃguez Mora, José V & Ruiz, Pep, 2020. "Tracking the COVID-19 Crisis with High-Resolution Transaction Data," CEPR Discussion Papers 14642, C.E.P.R. Discussion Papers.
- Franz Ruch & Mehmet Balcilar & Rangan Gupta & Mampho P. Modise, 2020.
"Forecasting core inflation: the case of South Africa,"
Applied Economics, Taylor & Francis Journals, vol. 52(28), pages 3004-3022, June.
- Franz Ruch & Mehmet Balcilar Author-Name-First Mehmet & Mampho P. Modise & Rangan Gupta, 2015. "Forecasting Core Inflation: The Case of South Africa," Working Papers 15-08, Eastern Mediterranean University, Department of Economics.
- Franz Ruch & Mehmet Balcilar & Mampho P. Modise & Rangan Gupta, 2015. "Forecasting Core Inflation: The Case of South Africa," Working Papers 201543, University of Pretoria, Department of Economics.
- Raj Chetty & John N Friedman & Michael Stepner & Opportunity Insights Team & Camille Baker & Harvey Barnhard & Matt Bell & Gregory Bruich & Tina Chelidze & Lucas Chu & Westley Cineus & Sebi Devlin-Fol, 2024.
"The Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data,"
The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(2), pages 829-889.
- Raj Chetty & John N. Friedman & Michael Stepner & The Opportunity Insights Team, 2020. "The Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data," NBER Working Papers 27431, National Bureau of Economic Research, Inc.
- Athey, Susan & Imbens, Guido W., 2019.
"Machine Learning Methods Economists Should Know About,"
Research Papers
3776, Stanford University, Graduate School of Business.
- Susan Athey & Guido Imbens, 2019. "Machine Learning Methods Economists Should Know About," Papers 1903.10075, arXiv.org.
- Gupta, Rangan & Kabundi, Alain, 2011.
"A large factor model for forecasting macroeconomic variables in South Africa,"
International Journal of Forecasting, Elsevier, vol. 27(4), pages 1076-1088, October.
- Alain Kabundi & Rangan Gupta, 2009. "A Large Factor Model for Forecasting Macroeconomic Variables in South Africa," Working Papers 137, Economic Research Southern Africa.
- McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
- repec:wrk:wrkemf:28 is not listed on IDEAS
- Liang, Feng & Paulo, Rui & Molina, German & Clyde, Merlise A. & Berger, Jim O., 2008. "Mixtures of g Priors for Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 410-423, March.
- Balcilar, Mehmet & Gupta, Rangan & Kotzé, Kevin, 2015. "Forecasting macroeconomic data for an emerging market with a nonlinear DSGE model," Economic Modelling, Elsevier, vol. 44(C), pages 215-228.
- Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
- Mr Steinbach & Pt Mathuloe & Bw Smit, 2009.
"An Open Economy New Keynesian Dsge Model Of The South African Economy,"
South African Journal of Economics, Economic Society of South Africa, vol. 77(2), pages 207-227, June.
- Rudi Steinbach & Patience Mathuloe & Ben Smit, 2009. "An open economy New Keynesian DSGE model of the South African economy," Working Papers 3431, South African Reserve Bank.
- Duarte, Claudia & Rua, Antonio, 2007. "Forecasting inflation through a bottom-up approach: How bottom is bottom?," Economic Modelling, Elsevier, vol. 24(6), pages 941-953, November.
- David Rossell & Donatello Telesca, 2017. "Nonlocal Priors for High-Dimensional Estimation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 254-265, January.
- Faust, Jon & Wright, Jonathan H., 2013. "Forecasting Inflation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 2-56, Elsevier.
- James H. Stock & Mark W. Watson, 2020. "Slack and Cyclically Sensitive Inflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 52(S2), pages 393-428, December.
- Sami Alpanda & Kevin Kotzé & Geoffrey Woglom, 2010. "The Role Of The Exchange Rate In A New Keynesian Dsge Model For The South African Economy," South African Journal of Economics, Economic Society of South Africa, vol. 78(2), pages 170-191, June.
- Scott R Baker & Robert A Farrokhnia & Steffen Meyer & Michaela Pagel & Constantine Yannelis & Jeffrey Pontiff, 0.
"How Does Household Spending Respond to an Epidemic? Consumption during the 2020 COVID-19 Pandemic,"
The Review of Asset Pricing Studies, Society for Financial Studies, vol. 10(4), pages 834-862.
- Scott R. Baker & R.A. Farrokhnia & Steffen Meyer & Michaela Pagel & Constantine Yannelis, 2020. "How Does Household Spending Respond to an Epidemic? Consumption During the 2020 COVID-19 Pandemic," NBER Working Papers 26949, National Bureau of Economic Research, Inc.
- Scott R. Baker & R.A. Farrokhnia & Steffen Meyer & Michaela Pagel & Constantine Yannelis, 2020. "How Does Household Spending Respond to an Epidemic? Consumption During the 2020 COVID-19 Pandemic," Working Papers 2020-30, Becker Friedman Institute for Research In Economics.
- Rajashri Chakrabarti & Sebastian Heise & Davide Melcangi & Maxim L. Pinkovskiy & Giorgio Topa, 2020. "Did State Reopenings Increase Consumer Spending?," Liberty Street Economics 20200918b, Federal Reserve Bank of New York.
- Kenneth Creamer & Greg Farrell & Neil Rankin, 2012.
"What Price-Level Data Can Tell Us About Pricing Conduct In South Africa,"
South African Journal of Economics, Economic Society of South Africa, vol. 80(4), pages 490-509, December.
- Dr. Kenneth Creamer & Dr. Greg Farrell & Prof. Neil Rankin, 2012. "What pricelevel data can tell us about pricing conduct in South Africa," Working Papers 5117, South African Reserve Bank.
- Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
- Litterman, Robert B, 1986. "A Statistical Approach to Economic Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 1-4, January.
- Christian Schumacher, 2007.
"Forecasting German GDP using alternative factor models based on large datasets,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(4), pages 271-302.
- Schumacher, Christian, 2005. "Forecasting German GDP using alternative factor models based on large datasets," Discussion Paper Series 1: Economic Studies 2005,24, Deutsche Bundesbank.
- Geoffrey Woglom & Kevin Kotze & Sami Alpanda, 2010. "Should Central Banks of Small Open Economies Respond to Exchange Rate Fluctuations: The Case of South Africa," Working Papers 174, Economic Research Southern Africa.
- Byron Botha & Shaun de Jager & Franz Ruch & Rudi Steinbach, 2017. "The Quarterly Projection Model of the SARB," Working Papers 8000, South African Reserve Bank.
- Rangan Gupta & Alain Kabundi, 2010.
"Forecasting macroeconomic variables in a small open economy: a comparison between small- and large-scale models,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 168-185.
- Rangan Gupta & Alain Kabundi, 2008. "Forecasting Macroeconomic Variables in a Small Open Economy: A Comparison between Small- and Large-Scale Models," Working Papers 200830, University of Pretoria, Department of Economics.
- Valen E. Johnson & David Rossell, 2012. "Bayesian Model Selection in High-Dimensional Settings," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 649-660, June.
- Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "The Economics of Artificial Intelligence: An Agenda," NBER Books, National Bureau of Economic Research, Inc, number agra-1, June.
- Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010.
"Large Bayesian vector auto regressions,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
- Marta Bańbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92, January.
- Fuhrer, Jeffrey C., 2010.
"Inflation Persistence,"
Handbook of Monetary Economics, in: Benjamin M. Friedman & Michael Woodford (ed.), Handbook of Monetary Economics, edition 1, volume 3, chapter 9, pages 423-486,
Elsevier.
- Jeff Fuhrer & George Moore, 1995. "Inflation Persistence," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(1), pages 127-159.
- Jeffrey C. Fuhrer, 2009. "Inflation persistence," Working Papers 09-14, Federal Reserve Bank of Boston.
- Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2021. "The Value Of Robust Statistical Forecasts In The Covid-19 Pandemic," National Institute Economic Review, National Institute of Economic and Social Research, vol. 256, pages 19-43, April.
- Guangling 'Dave' Liu & Rangan Gupta & Eric Schaling, 2009.
"A New-Keynesian DSGE model for forecasting the South African economy,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(5), pages 387-404.
- Guangling (Dave) Liu & Rangan Gupta & Eric Schaling, 2008. "A New-Keynesian DSGE Model for Forecasting the South African Economy," Working Papers 200805, University of Pretoria, Department of Economics.
- Cornelia Hammer & Ms. Diane C Kostroch & Mr. Gabriel Quiros-Romero, 2017. "Big Data: Potential, Challenges and Statistical Implications," IMF Staff Discussion Notes 2017/006, International Monetary Fund.
- Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
- World Bank, 2014. "Central America : Big Data in Action for Development," World Bank Publications - Reports 21325, The World Bank Group.
- Sami Alpanda & Kevin Kotzé & Geoffrey Woglom, 2011. "Forecasting Performance Of An Estimated Dsge Model For The South African Economy," South African Journal of Economics, Economic Society of South Africa, vol. 79(1), pages 50-67, March.
- Shelby R. Buckman & Adam Hale Shapiro & Moritz Sudhof & Daniel J. Wilson, 2020. "News Sentiment in the Time of COVID-19," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, vol. 2020(08), pages 1-05, April.
- Jean Boivin & Serena Ng, 2005.
"Understanding and Comparing Factor-Based Forecasts,"
International Journal of Central Banking, International Journal of Central Banking, vol. 1(3), December.
- Jean Boivin & Serena Ng, 2005. "Understanding and Comparing Factor-Based Forecasts," NBER Working Papers 11285, National Bureau of Economic Research, Inc.
- Boivin, Jean & Ng, Serena, 2005. "Understanding and Comparing Factor-Based Forecasts," MPRA Paper 836, University Library of Munich, Germany.
- Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
- Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
- Kelly, Bryan & Pruitt, Seth, 2015. "The three-pass regression filter: A new approach to forecasting using many predictors," Journal of Econometrics, Elsevier, vol. 186(2), pages 294-316.
- Jens Mehrhoff, 2017. "Central banks' use of and interest in "big data"," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Big Data, volume 44, Bank for International Settlements.
- Rajashri Chakrabarti & Sebastian Heise & Davide Melcangi & Maxim L. Pinkovskiy & Giorgio Topa, 2020. "How Did State Reopenings Affect Small Businesses?," Liberty Street Economics 20200921, Federal Reserve Bank of New York.
- Franz Ruch & Neil Rankin & Stan du Plessis, 2016. "Decomposing inflation using microprice data Stickyprice inflation," Working Papers 7354, South African Reserve Bank.
- Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
- Okiriza Wibisono & Hidayah Dhini Ari & Anggraini Widjanarti & Alvin Andhika Zulen & Bruno Tissot, 2019. "The use of big data analytics and artificial intelligence in central banking – An overview," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
- Marcelo C. Medeiros & Gabriel F. R. Vasconcelos & Álvaro Veiga & Eduardo Zilberman, 2021.
"Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 98-119, January.
- Marcelo Madeiros & Gabriel Vasconcelos & Álvaro Veiga & Eduardo Zilberman, 2019. "Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods," Working Papers Central Bank of Chile 834, Central Bank of Chile.
- Rajashri Chakrabarti & Maxim L. Pinkovskiy, 2020. "Did State Reopenings Increase Social Interactions?," Liberty Street Economics 20200617, Federal Reserve Bank of New York.
- Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
- Elliott, Graham & Gargano, Antonio & Timmermann, Allan, 2015. "Complete subset regressions with large-dimensional sets of predictors," Journal of Economic Dynamics and Control, Elsevier, vol. 54(C), pages 86-110.
- Agrawal, Ajay & Gans, Joshua & Goldfarb, Avi (ed.), 2019. "The Economics of Artificial Intelligence," National Bureau of Economic Research Books, University of Chicago Press, number 9780226613338, July.
- G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
- Valen E. Johnson & David Rossell, 2010. "On the use of non‐local prior densities in Bayesian hypothesis tests," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(2), pages 143-170, March.
- Cornelia Hammer & Diane C Kostroch & Gabriel Quiros-Romero, 2017. "Big Data; Potential, Challenges and Statistical Implications," IMF Staff Discussion Notes 17/06, International Monetary Fund.
- Bai, Jushan & Ng, Serena, 2008. "Large Dimensional Factor Analysis," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(2), pages 89-163, June.
- Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
- Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2013. "Supplementary Appendix for "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls"," Papers 1305.6099, arXiv.org, revised Jun 2013.
- Bruno Tissot, 2019. "Big data for central banks," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.
- Ibarra, Raul, 2012. "Do disaggregated CPI data improve the accuracy of inflation forecasts?," Economic Modelling, Elsevier, vol. 29(4), pages 1305-1313.
- Peter McCullagh & Nicholas G Polson, 2018. "Statistical sparsity," Biometrika, Biometrika Trust, vol. 105(4), pages 797-814.
Citations
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Cited by:
- Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2023.
"Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany,"
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34/2023, Deutsche Bundesbank.
- Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2024. "Nowcasting consumer price inflation using high-frequency scanner data: evidence from Germany," Working Paper Series 2930, European Central Bank.
- Shovon Sengupta & Tanujit Chakraborty & Sunny Kumar Singh, 2023. "Forecasting CPI inflation under economic policy and geopolitical uncertainties," Papers 2401.00249, arXiv.org, revised Jul 2024.
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More about this item
Keywords
Micro-data; Inflation; High-dimensional regression; Penalised likelihood; Bayesian methods; Statistical learning;All these keywords.
JEL classification:
- C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
- E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
Statistics
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