The long-run impact of inflation in South Africa

This paper evaluates the hypothesis of long-run super-neutrality of money (LRSN) within the context of the South African economy. The long-run impact of inflation on the interest rate and subsequently, output is estimated by employing a trivariate structural vector autoregression model, using quarterly data for the period of 1960:1 to 2010:1. The estimation results suggest that the hypothesis of LRSN cannot be rejected, thereby suggesting that monetary policy in South Africa cannot be used to solve the large and persistent unemployment problem in South Africa, which is understandable, since unemployment is inherently structural and is due to skills-shortage. This is further supported by our one of our other results which shows that significant long-run impact on output is obtained from technological improvements.     

Is Economic Policy Uncertainty Related to Suicide Rates? Evidence from the United States

The main objective of this paper was to see whether different countries around the world show differences in their sustainability levels as captured in the indicators from the Sustainable Society Index (SSI, Van de Kerk and Manuel in Ecol Econ 66:228–242, 2012) according to their level of income. To do so, the X-STATIS and CO-STATIS multivariate techniques were employed. With these methods, our sample of 151 countries and 21 indicators can be jointly represented along four time periods. The results obtained permit us to visualize that the groups of countries by income levels show differences in some of the variables from the SSI, because of the lack of proximities between those variables and the countries. Moreover, with the X-STATIS technique, the possible evolution of the countries or indicators over time can be represented, and with CO-STATIS, the relations between the social, economic and environmental aspects can be shown as well. From our results we were able to deduce that, on the one hand, social and economic indicators, such as Public Debt or Employment, are associated with countries having high and upper-middle incomes, for example, Chile, Israel, Malta, Kuwait, Saudi Arabia, Oman, Spain, Portugal, France, Poland and Czech Republic. On the other hand, countries with low and lower-middle incomes are more associated with environmental issues. Also, after finding that the differences between the countries by income levels are mainly caused by the economic indicators, we carried out two CO-STATIS analyses, one for social and economic variables, and the other for economic and environmental variables. These findings led us to deduce that, generally, the social and economic indicators are not related to each other, nor are the economic indicators related to the environmental ones. However, for some of the countries individually both relations may be possible.     

The unending embrace: Raymond Vernon, multinational enterprises, and national governments

In this article commemorating Raymond Vernon, I make no attempt to summarize his expansive work on the nature and prospects of multinational enterprises (MNEs). Rather, I discuss three forces — rivalry, uncertainty, and time — that Vernon accentuated in his explanations of the behavior of MNEs. I then make brief observations about Vernon and methodology. Subsequently, I speculate on the following question: A hundred years from now, what single idea will Vernon be remembered for? Acknowledging the product cycle as a leading contender, I nominate and discuss (under the moniker “the unending embrace”) another candidate, one that dwells on the undulating tension between MNEs and governments. I conclude by flagging for future research some questions that that central idea raises.     

On the directional accuracy of inflation forecasts: evidence from South African survey data

We study the information content of South African inflation survey data by determining the directional accuracy of both short-term and long-term forecasts. We use relative operating characteristic (ROC) curves, which have been applied in a variety of fields including weather forecasting and radiology, to ascertain the directional accuracy of the forecasts. A ROC curve summarizes the directional accuracy of forecasts by comparing the rate of true signals (sensitivity) with the rate of false signals (one minus specifity). A ROC curve goes beyond market-timing tests widely studied in earlier research as this comparison is carried out for many alternative values of a decision criterion that discriminates between signals (of a rising inflation rate) and nonsignals (of an unchanged or a falling inflation rate). We find consistent evidence that forecasts contain information with respect to the subsequent direction of change of the inflation rate.     

UK macroeconomic volatility: Historical evidence over seven centuries

Breaking ground from all previous studies, we estimate a time-varying Vector Autoregression model that examines the time-period 1270–2016 — the entire economic history of the U.K. Focusing on permanent and transitory shocks in the economy, we study the fluctuation in conditional volatilities and time-varying long-run responses of output growth and inflation. Unlike all previous studies that use time invariant linear models, our approach reveals that the pre 1600 period is a turbulent economic period of high volatility that is only repeated in the 20th century. The repeating patterns in the conditional volatilities follow from aggregate supply shocks, while most of the inflation responses follow from aggregate demand shocks. Thus, we uncover that despite the technological growth and the various changes in the structure of the U.K. economy in the last century, the recurring patterns call for an examination of the true impact of the various policies on the economy.     

Using large data sets to forecast sectoral employment

We use several models using classical and Bayesian methods to forecast employment for eight sectors of the US economy. In addition to using standard vector-autoregressive and Bayesian vector autoregressive models, we also augment these models to include the information content of 143 additional monthly series in some models. Several approaches exist for incorporating information from a large number of series. We consider two multivariate approaches—extracting common factors (principal components) and Bayesian shrinkage. After extracting the common factors, we use Bayesian factor-augmented vector autoregressive and vector error-correction models, as well as Bayesian shrinkage in a large-scale Bayesian vector autoregressive models. For an in-sample period of January 1972 to December 1989 and an out-of-sample period of January 1990 to March 2010, we compare the forecast performance of the alternative models. More specifically, we perform ex-post and ex-ante out-of-sample forecasts from January 1990 through March 2009 and from April 2009 through March 2010, respectively. We find that factor augmented models, especially error-correction versions, generally prove the best in out-of-sample forecast performance, implying that in addition to macroeconomic variables, incorporating long-run relationships along with short-run dynamics play an important role in forecasting employment. Forecast combination models, however, based on the simple average forecasts of the various models used, outperform the best performing individual models for six of the eight sectoral employment series.