In this paper, we estimate the long-run equilibrium relationship between money balance as a ratio of income and the Treasury bill rate for the period of 1965:02 to 2007:01, and in turn use the relationship to obtain welfare cost estimates of inflation. Using the Johansen technique, we estimate a log-log specification and a semi-log model of the above relationship. Based on the fits of the specifications, we decided to rely more on the welfare cost measure obtained under the log-log money demand model. Our estimates suggest that the welfare cost of inflation for South Africa ranges between 0.34% and 0.67% of GDP, for a band of 3-6% of inflation. Thus, it seems that the South African Reserve Bank's current inflation target band of 3-6% is not too poorly designed in terms of welfare.

One characteristic of many macroeconomic and financial time series is their asymmetric behaviour during different phases of a business cycle. Oil price shocks have been amongst those economic variables that have been identified in theoretical and empirical literature to predict the phases of business cycles. However, the role of oil price shocks to determine business cycle fluctuations has received less attention in emerging and developing economies. The aim of this study is to investigate the role of oil price shocks in predicting the phases of the South African business cycle associated with higher and lower growth regimes. By adopting a regime dependent analysis, we investigate the impact of oil price shocks under two phases of the business cycle, namely high and low growth regimes. As a net importer of oil, South Africa is expected to be vulnerable to oil price shocks irrespective of the phase of the business cycle. Using a Bayesian Markov switching vector autoregressive (MS-VAR) model and data for the period 1960Q2 to 2013Q3, we found the oil price to have predictive content for real output growth under the low growth regime. The results also show the low growth state to be shorter-lived compared to the higher growth state.

Recent empirical evidence on the direct link of inflation targeting and inflation volatility is at best mixed. However, comparing inflation volatility across alternative monetary policy regimes within a country based on conventional ways, used in previous studies, begs the question. The question is not whether the volatility of inflation has changed, but rather whether the volatility is different than it otherwise would have been. In such a backdrop, this paper uses the cosine-squared cepstrum to provide evidence that CPI inflation in South Africa has become more volatile since the first quarter of 2000, when the country moved into an inflation targeting regime, than it would have been had the South African Reserve Bank (SARB) continued with the more eclectic monetary policy approach pursued in the pre-targeting era.JEL Codes: C65; E42; E52; E64.

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Purpose The purpose of this paper is to examine intentional herding among institutional investors with a particular focus on the technology sector that was the driver of the “New Economy” in the USA during the dot-com bubble of the 1990s. Design/methodology/approach Using data on technology stockholdings of 115 large institutional investors, the authors test the presence of herding by examining linear dependence and feedback between individual investors’ technology stockholdings and that of the aggregate market. Unlike other models to detect herding, the authors use Geweke (1982) type causality tests that allow authors to disentangle spurious herding from intentional herding via tests of bidirectional and instantaneous causality across portfolio positions in technology stocks. Findings After controlling information-based (spurious) herding, the tests show that 38 percent of large institutional investors tend to intentionally herd in technology stocks. Originality/value The findings support the existing literature that investment decisions by large institutional investors are not only driven by fundamental information, but also by cognitive bias that is characterized by intentional herding.