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Given lags in the release of data, a central bank must ‘nowcast’ current gross domestic product (GDP) using available quarterly or higher frequency data to understand the current state of economic activity. This paper uses various statistical modelling techniques to draw on a large number of series to nowcast South African GDP. We also show that GDP volatility has increased markedly over the last 5 years, making GDP forecasting more difficult. We show that all the models developed, as well as the Reserve Bank's official forecasts, have tended to overestimate GDP growth over this period. However, several of the statistical nowcasting models we present in this paper provide competitive nowcasts relative to the official Reserve Bank and market analysts' nowcasts.

This paper produces aggregate and industry estimates of total factor productivity (TFP) for New Zealand. TFP is estimated based on constant elasticity of substitution (CES) production functions that permit varying assumptions about factor augmentation and that allow for industry‐specific values of the elasticity of substitution between inputs. The CES approach simultaneously explains changes in labour share and output over time, and provides estimates of the contribution of capital and labour to productivity in New Zealand. The results suggest that negative capital‐augmenting technical change in several industries has weighed on productivity in New Zealand.

We investigate whether the use of statistical learning techniques and big data can enhance the accuracy of inflation forecasts. We make use of a large dataset for the disaggregated prices of consumption goods and services, which we partially reconstruct, and a large suite of different statistical learning and traditional time-series models. The results suggest that the statistical learning models are able to compete with most benchmarks over medium to longer horizons, despite the fact that we only have a relatively small sample of available data. This may imply that the ability of statistical learning models to explain nonlinear relationships, or as an alternative, restrict the set of predictors to relevant information, is of importance. These characteristics of the statistical learning models may be particularly useful during periods of crisis, when deviations from the steady state are more persistent. We find that the accuracy of the central bank's near-term inflation forecasts compares favourably with those of The authors would like to thank Patrick Kelly and Marietjie Bennett from Statistics South Africa for their assistance with the data. The insightful comments of the anonymous referees have also lead to a number of improvements, for which the authors are grateful. The remaining errors are those of the authors.