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Model Evaluation, Validation, and Selection


Big data forecasting of South African inflation

Byron Botha, Rulof Burger, Kevin Kotze, Neil Rankin, and Daan Steenkamp
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...
Feb 2022
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Modelling Stock Return Volatility Dynamics in Selected African Markets

Daniel King and Ferdi Botha
This paper examines whether accounting for structural changes in the conditional variance process, through the use of Markov-switching models, improves estimates and forecasts of stock return volatility over those of the more conventional single-state (G)ARCH models, within and across selected...
Jan 2014
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Time-Varying Parameter in the Almost Ideal Demand System and the Rotterdam Model: Will the Best Specification Please Stand Up?

William A. Barnett and Isaac Kalonda-Kanyama
This paper assesses the ability of the Rotterdam model and of three versions of the almost ideal demand system (AIDS) to recover the time-varying elasticities of a true demand system and to satisfy theoretical regularity. Using Monte Carlo simulations, we nd that the Rotterdam model performs better...
Mar 2013
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Risk-return tradeoff and the behaviour of volatility on the South African stock market: Evidence from both aggregate and disaggregate data

Mandimika, N.Z. and Chinzara, Z.
The study analyses the nature and behaviour of volatility, the risk-return relationship and the long-term trend of volatility on the South African equity markets, using aggregate-level, industrial-level and sectoral-level daily data for the period 1995-2009. By employing dummy variables for the...
Nov 2010
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