Synchronization between South Africa and the U.S.: A Structural Dynamic Factor Analysis

This paper studies the synchronization of economic variables between South Africa and the US. In addition it examines transmission channels through which supply and demand shocks from the US effect economic activity in South Africa. We use a structural dynamic factor model approach, instead of the well known structural vector autoregressive method, as it accommodates a large panel of time series variables. The paper contains four findings. First, using the full-sample period, US supply shocks are transmitted to South Africa through business confidence and imports of goods and services; while US demand shocks are transmitted via interest rates, stock prices, exports of goods and services, and real effective exchange rates. Second, there is a decrease in integration over time as the common component of GDP drops in the reduced sample. The impact of an increase in comovement of GDP is outweighed by several factors resulting from the structural reforms initiated by the government after the end of apartheid. Thirdly, in the latter period the South African economy is mainly affected by the US supply shocks through a variety of channels. For this latter period, US supply shocks are forcefully transmitted to South Africa via consumer and business confidence, stock prices and real effective exchange rates. Finally, the idiosyncratic component still plays an important role in the South African economy. Structural reforms are crucial to make the domestic economy competitive internationally.Dynamic factor models, international business cycles, sign restrictions

Global Financial Crises and Time-varying Volatility Comovement in World Equity Markets

This paper studies volatility comovement in world equity markets between 1994 and 2008. Global volatility factors are extracted from a panel of monthly volatility proxies relating to 25 developed and 20 emerging stock markets. A dynamic factor model (FM) is estimated using two-year rolling window regressions. The FMÂ’s time-varying variance shares of global factors map variations in volatility comovement over time and across countries. The results indicate that global volatility linkages are particularly strong during Â…nancial crises in Asia (1997-8), Russia (1998), and the United States (2007-8). Emerging markets are less syncrhonised with world volatility than are developed markets. In particular, we observe decoupling between emerging and world volatilities between 2001 and 2007. Recoupling occurs during 2008, thus identifying emerging market investments as a temporary hedge against volatility spillovers from the US subprime crisis.Asset Market Linkages, Dynamic Factor Model, Financial Crisis, International DiversiÂ…cation, Volatility Comovement

A Large Factor Model for Forecasting Macroeconomic Variables in South Africa

This paper uses large Factor Models (FMs) which accommodates a large cross-section of macroeconomic time series for forecasting per capita growth rate, inflation, and the nominal short-term interest rate for the South African economy. The FMs used in this study contains 267 quarterly series observed over the period of 1980Q1-2006Q4. The results, based on the RMSEs of one- to four-quarters-ahead out of sample forecasts over 2001Q1 to 2006Q4, indicate that the FMs tend to outperform alternative models such as an unrestricted VAR, Bayesian VARs (BVARs) and a typical New Keynesian Dynamic Stochastic General Equilibrium (NKDSGE) model in forecasting the three variables under consideration, hence, indicating the blessings of dimensionality.Large Factor Model, VAR, BVAR, NKDSGE Model, Forecast Accuracy

Volatility Spillovers across South African Asset Classes during Domestic and Foreign

This paper studies domestic volatility transmission in an emerging economy. Daily volatility spillover indices, relating to South African (SA) currencies, bonds and equities, are estimated using variance decompositions from a generalised vector autoregressive (GVAR) model (Pesaran and Shin 1998). The results suggest substantial time-variation in volatility linkages between October 1996 and June 2010. Typically, large increases in volatility spillovers coincide with domestic and foreign financial crises. Equities are the most important source of volatility spillovers to other asset classes. However, following the 2001 currency crisis, and up until mid-2006, currencies temporarily dominate volatility transmission. Bonds are a consistent net receiver of volatility spillovers. In comparison to similar research focussing on the United States (Diebold and Yilmaz 2010), volatility linkages between SA asset classes are relatively strong.Asset Market Linkages, Dynamic Correlation, Financial Crisis, Generlised Vector Autoregression, Variance Decomposition, Volatility Spillover.

Trade Shocks from BRIC to South Africa: A Global VAR Analysis

This paper studies the trade linkages between South Africa and the BRIC (Brazil, Russia, India, and China) countries. We apply a global vector autoregressive model (global VAR) to investigate the degree of trade linkages and shock transmission between South Africa and the BRIC countries over the period 1995Q1-2009Q4. The model contains 32 countries and has two different estimations: the first one consists of 24 countries and one region, with the 8 countries in the euroarea treated as a single economy; and the second estimation contains 20 countries and two regions, with the BRIC and the euro area countries respectively treated as a single economy. The results suggest that trade linkages exist between our focus economies; however the magnitude differs between countries. Shocks from each BRIC country are shown to have considerable impact on South African real imports and output.BRICS, Trade Linkages, Global VAR

Is a DFM Well Suited for Forecasting Regional House Price Inflation?

This paper uses the Dynamic Factor Model (DFM) framework, which accommodates a large cross-section of macroeconomic time series for forecasting regional house price inflation. As a case study, we use data on house price inflation for five metropolitan areas of South Africa. The DFM used in this study contains 282 quarterly series observed over the period 1980Q1-2006Q4. The results, based on the Mean Absolute Errors of one- to four-quarters-ahead out of sample forecasts over the period of 2001Q1 to 2006Q4, indicate that, in majority of the cases, the DFM outperforms the VARs, both classical and Bayesian, with the latter incorporating both spatial and non-spatial models. Our results, thus, indicate the blessing of dimensionality.Dynamic Factor Model, VAR, BVAR, Forecast Accuracy

Could we have predicted the recent downturn in the South African Housing Market?

This paper develops large-scale Bayesian Vector Autoregressive (BVAR) models, based on 268 quarterly series, for forecasting annualized real house price growth rates for large-, medium and small-middle-segment housing for the South African economy. Given the in-sample period of 1980:01 to 2000:04, the large-scale BVARs, estimated under alternative hyperparameter values specifying the priors, are used to forecast real house price growth rates over a 24-quarter out-of-sample horizon of 2001:01 to 2006:04. The forecast performance of the large-scale BVARs are then compared with classical and Bayesian versions of univariate and multivariate Vector Autoregressive (VAR) models, merely comprising of the real growth rates of the large-, medium and small-middle-segment houses, and a large-scale Dynamic Factor Model (DFM), which comprises of the same 268 variables included in the large-scale BVARs. Based on the one- to four-quarters ahead Root Mean Square Errors (RMSEs) over the out-of-sample horizon, we find the large-scale BVARs to not only outperform all the other alternative models, but to also predict the recent downturn in the real house price growth rates for the three categories of the middle-segment-housing over the period of 2003:01 to 2008:02.Dynamic Factor Model, BVAR, Forecast Accuracy

Forecasting Real U.S. House Prices: Principal Components Versus Bayesian Regressions

This paper analyzes the ability of principal component regressions and Bayesian regression methods under Gaussian and double-exponential prior in forecasting the real house prices of the United States (U.S.), based on a monthly dataset of 112 macroeconomic variables. Using an in-sample period of 1992:01 to 2000:12, Bayesian regressions are used to forecast real U.S. house prices at the twelve-months-ahead forecast horizon over the out-of-sample period of 2001:01 to 2004:10. In terms of the Mean Square Forecast Errors (MSFEs), our results indicate that a principal component regression with only one factor is best-suited for forecasting the real U.S. house prices. Among the Bayesian models, the regression based on the double exponential prior outperforms the model with Gaussian assumptions

L'enfance, l'éducation et la déviance en Afrique

Actes du Colloque international de Kinshasa (BASE-CICC - décembre 1986)Etude qui analyse les multiples métamorphoses que subissent les sociétés africaines afin d'en évaluer les incidences criminogènes. Il s'agit de voir comment une amélioration générale des conditions de vie peut engendrer une augmentation des comportements déviants. Cette étude traite de la situation des enfants en Afrique, de la jeunesse délinquante et de la réaction sociale et enfin de la prévention de la délinquance juvénile

Three Cycles: Housing, Credit and Real Activity

We examine the characteristics and comovement of cycles in house prices, credit, real activity and interest rates in advanced economies during the past 25 years, using a dynamic generalised factor model. House price cycles generally lead credit and business cycles over the long term, while in the short to medium run the relationship varies across countries. Interest rates tend to lag other cycles at all time horizons. While global factors are important, the U.S. business cycle, house price cycle and interest rate cycle tend to lead the respective cycles in other countries over all time horizons. However, the U.S. credit cycle leads mostly over the long term.Macro-financial linkages, house prices, credit, business cycle