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Big data forecasting of South African inflation

Author

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  • Byron Botha
  • Kevin Kotze
  • Neil Rankin
  • Rulof P. Burger

Abstract

We investigate whether the use of machine learning techniques and big data can enhance the accuracy of inflation forecasts and our understanding of the drivers of South African inflation. We make use of a large dataset for the disaggregated prices of consumption goods and services to compare the forecasting performance of a suite of different statistical learning models […]

Suggested Citation

  • Byron Botha & Kevin Kotze & Neil Rankin & Rulof P. Burger, 2022. "Big data forecasting of South African inflation," Working Papers 873, Economic Research Southern Africa.
    • Handle: RePEc:rza:wpaper:873
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    1. 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.
    2. 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.
    3. 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.
    4. Kirstin Hubrich & David F. Hendry, 2005. "Forecasting Aggregates by Disaggregates," Computing in Economics and Finance 2005 270, Society for Computational Economics.
    5. 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.
    6. 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.
    7. Geoffrey Woglom, 2005. "Forecasting South African Inflation," South African Journal of Economics, Economic Society of South Africa, vol. 73(2), pages 302-320, June.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Jeff Fuhrer & George Moore, 1995. "Inflation Persistence," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(1), pages 127-159.
    13. 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.
    14. 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.
    15. Byron Botha & Shaun de Jager & Franz Ruch & Rudi Steinbach, 2017. "The Quarterly Projection Model of the SARB," Working Papers 8000, South African Reserve Bank.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. Hansen, Stephen & Carvalho, Vasco & García, Juan Ramón & Ortiz, Alvaro & Rodrigo, Tomasa & Rodríguez 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.
    24. 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.
    25. 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.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    31. 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.
    32. 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.
    33. 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.
    34. 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.
    35. Elliott, Graham & Gargano, Antonio & Timmermann, Allan, 2013. "Complete subset regressions," Journal of Econometrics, Elsevier, vol. 177(2), pages 357-373.
    36. World Bank, 2014. "Central America : Big Data in Action for Development," World Bank Publications - Reports 21325, The World Bank Group.
    37. 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.
    38. 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.
    39. 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.
    40. 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.
    41. 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.
    42. 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.
    43. 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.
    44. repec:ulb:ulbeco:2013/13388 is not listed on IDEAS
    45. 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.
    46. 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.
    47. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
    48. 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.
    49. 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.
    50. 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.
    51. 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.
    52. 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.
    53. 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.
    54. 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.
    55. 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.
    56. 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.
    57. 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.
    58. Franz Ruch & Neil Rankin & Stan du Plessis, 2016. "Decomposing inflation using microprice data Stickyprice inflation," Working Papers 7354, South African Reserve Bank.
    59. 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.
    60. 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.
    61. 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.
    62. Daleen Smal & Coen Pretorius & Nelene Ehlers, 2007. "The core forecasting model of the South African Reserve Bank," Working Papers 3195, South African Reserve Bank.
    63. 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.
    64. 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.
    65. 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.
    66. Franz Ruch & Neil Rankin & Stan du Plessis, 2016. "Decomposing inflation using micropricelevel data South Africas pricing dynamics," Working Papers 7353, South African Reserve Bank.
    67. 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.
    68. 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.
    69. 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.
    70. Alberto Cavallo, 2024. "Inflation with Covid Consumption Baskets," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 72(2), pages 902-917, June.
    71. Rajashri Chakrabarti & Maxim L. Pinkovskiy, 2020. "Did State Reopenings Increase Social Interactions?," Liberty Street Economics 20200617, Federal Reserve Bank of New York.
    72. 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.
    73. 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.
    74. 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.
    75. 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, September.
    76. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    77. 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.
    78. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
    79. repec:wrk:wrkemf:28 is not listed on IDEAS
    80. 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.
    81. 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.
    82. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    83. 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.
    84. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    85. 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.
    86. 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.
    87. 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.
    88. 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.
    89. 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.
    90. Bai, Jushan & Ng, Serena, 2008. "Large Dimensional Factor Analysis," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(2), pages 89-163, June.
    91. 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.
    92. 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.
    93. 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.
    94. 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.
    95. 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.
    96. 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.
    97. Ibarra, Raul, 2012. "Do disaggregated CPI data improve the accuracy of inflation forecasts?," Economic Modelling, Elsevier, vol. 29(4), pages 1305-1313.
    98. Peter McCullagh & Nicholas G Polson, 2018. "Statistical sparsity," Biometrika, Biometrika Trust, vol. 105(4), pages 797-814.
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    1. 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," Discussion Papers 34/2023, Deutsche Bundesbank.
    2. 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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    Keywords

    Conflict; Crime and Violence; Discrimination; econometric modelling; Group Behavior; Inflation; Quantitative Methods; Religious Institutions;
    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
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • 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
    • H56 - Public Economics - - National Government Expenditures and Related Policies - - - National Security and War
    • O10 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - General
    • O55 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Africa

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