Recall that this study was largely motivated by the 84% statistically incorrect probability of S.A. descending into a recession in 2003, according to Khomo and Aziakpono (2007), which did not take place in reality. This incorrect probability raised concerns of the consistency of the predictive power of the S.A. term spread in forecasting S.A. recessions. As such, the incorrect probability provided suggestions that there might be a weak relationship between S.A. term spread and domestic economic activity, and hence the predictions of recession.
The in-sample results in section 5.3.1 illustrate that the best-fit model estimated from the S.A. term spread and the recession indicator a quarter earlier were able to provide signals of S.A. recessions in the in-sample period. The findings also reveal that in some instances, such as the recessions of 1984 and 1996, the signals were provided about 1 quarter earlier. However, in other instances such as the recessions of 1980 and 1989, at the onset of the recessions the probabilities provided were low, but they increased as S.A. entered the recessions. Results confirm the study by Khomo and Aziakpono (2007) that the S.A. term spread was able to predict the 1984, 1996, 1980 and 1989 recessions correctly in-sample. The findings by Moolman (2002) also illustrate that the S.A. term spread was able to predict those four recessions accurately, despite the fact that a static probit model was used, rather than the dynamic probit model used in this study.
Page 93 of 119
However, since the incorrect probability of recession by Khomo and Aziakpono (2007) in 2003 was produced in-sample, this study evaluated the real world predictive power of the S.A. term spread in the out-of-sample period from 2000 to 2012. The findings of this study show two important features. First, in 2003 the probability of recession was lower than 40%, correctly indicating that S.A. was not about to descend into a recession. Secondly, the global recession of 2007 was accurately predicted by the S.A. best-fit model. This was observed as a probability of recession, given the level of the S.A. term spread and the recession indicator 1 quarter earlier, increasing as the S.A. recession was entering the recession period.
The S.A. best-model confirmed an important observation in the literature: that the relationship between the term spread and the probability of recession is negative. The implication is that the more negative the slope of the yield curve, the higher the probability of recession. Similarly, the more positive the slope of the yield curve, the lower the probability of recession (Moolman, 2002:28). In this study this was observed by the negative coefficient of the term spread from the S.A. best-fit model (5.1).
Bernard and Gerlach (1996:6) have stated that term spreads and economic activity across countries is correlated. As such it would generally be expected of foreign term spreads to provide signals of domestic recessions. This suggests that there might be other term spreads that could predict S.A. recessions better than the S.A. term spread; hence the investigation of the Chinese, U.S. and German term spreads.
The in-sample predictions of the best-fit model of China suggested that given the Chinese term spread of 1 quarter earlier, the recession of 1996 was accurately predicted about 2 quarters earlier, with probabilities of recession at above 60% prior to the recession. However, despite the fact that the U.S. term spread used with the dynamic probit model was able to forecast the U.S. recessions in-sample (as reported by Hao and Ng, 2011), the U.S. term spread was able to forecast some S.A. recessions such as those of 1982, 1989 and 1996. However, it was not able to provide signals for the recession of 1984, and also produced wrong signals for recessions in 1987, which
Page 94 of 119
did not take place (see section 5.3.2.1). The in-sample results also revealed that Germany, on the other hand, provided countercyclical signs of recessions. In periods in which S.A. was not in a recession, the German best-fit model provided high probabilities of recessions, and in recession periods it provided low probabilities of recessions. This observation suggested that given the German term spread and the S.A. recession indicator 8 quarters earlier, the best-fit model was not able to accurately predict S.A. recessions in-sample.
From the out-of-sample, it was observed that given the Chinese term spread and the S.A. recession indicator 1 quarter earlier, the S.A. recession of 2007 was accurately predicted. This was shown by the expected probabilities of recessions produced by the Chinese best-fit model almost matching the actual probabilities of recessions. The findings also suggest that the Chinese best-fit model performed better than the S.A. best-fit model. This was indicated by the RMSE of 0.101 by China as compared to 0.163 by S.A. The U.S. on the other hand, provided probabilities as high as 60% at about 5 quarters prior to the occurrence of the 2007 recession, but also provided some incorrect signals of recession around 2002. The same trend observed in-sample continued in the out-of-sample with regard to Germany in that given the term spread and the S.A. recession indictor 8 quarters earlier, the probabilities generated were countercyclical. This suggested that in reality the German term spread does not predict the S.A. recessions.
5.6 CONCLUSION
The objective of this chapter was to evaluate the estimation of the best-fit models and the forecasting performance of the S.A., Chinese, U.S. and German term spreads in forecasting the S.A. recessions. The evaluation was done both in-sample and out-of sample for each term spread. The out-of-sample was to test the real-world forecasting ability of the term spreads in predicting S.A. recessions.
With regard to unit root testing, unit root is rejected at 5% significance level for both S.A. and Chinese term spreads with an intercept restriction. With the U.S. term spread, unit
Page 95 of 119
root is rejected at 1% significance level at restrictions of an intercept and a trend. Lastly, with regard to the German term spread, unit root is rejected at 5% significance level without any restrictions. All the term spreads used in this study are I (0) stationary on the level.
The criteria for choosing the optimal lag of forecasting were set as: the lag with the highest and lowest Schwartz-Bayesian Information Criteria (SIC), conditional that the Root Mean Square Error (RMSE) was also the lowest at that particular forecasting lag. Otherwise, the lag with the lowest RMSE was selected as the optimal lag of forecasting for that particular term spread. For S.A. and China the optimal lag selected for the best-fit models of these two term spreads was 1. The results illustrated that S.A. and China accurately provided S.A. recession signals, provided by the level of the term spreads and S.A. recession indicator 1 quarter earlier in-sample. For the U.S., the optimal lag was selected at 5 quarters, while the optimal lag for Germany was 8 quarters. The U.S. term spread was able to forecast some S.A. recessions such as 1982, 1989 and 1996, but was not able to provide signals for the recession of 1984, and also produced wrong signals of recessions in 1987 which did not materialize. This could have been because the S.A. economy was not yet the open economy it became after the democratic elections of 1994. According to the IMF (2005:76) Africa Report, formal negotiation of a free trade agreement between S.A. and the U.S. began later, in the years after 1994. This might have meant that the U.S. influence on the South African economy was not as considerable as it is today. Germany, however, provided countercyclical signs of recessions. In periods in which S.A. was not in a recession, the German best-fit model provided high probabilities of recessions and in recession periods it provided low probabilities of recessions. The observation was that given the German term spread and the S.A. recession indictor 8 quarters earlier, the German best-fit model does not provide signs of S.A. recessions.
In the out-of-sample forecasting China produced the lowest RMSE, followed by S.A. (though the RMSE was close to that of China), the U.S. and Germany respectively. This means that in the out-of-sample forecasting China provided more accurate signals of
Page 96 of 119
S.A. recessions compared to S.A., the U.S. and Germany. The U.S. best-model provided signals for the 2008 recession about 5 quarters earlier; however, it also generated false probabilities of recessions around 2003 and 2004. For Germany, the same trend of countercyclical recession signals continued in the out-of-sample. With the results for each of the term spreads discussed, the next chapter provides the conclusion to this study.
Page 97 of 119
CHAPTER 6
THE SUMMARY OF THE FINDINGS AND RECOMMENDATIONS
6.1 INTRODUCTION
The objective of this study was largely driven by the research undertaken by Khomo and Aziakpono (2007). In their study the S.A. term spread produced a statistically incorrect probability of about 84% of S.A. being in a recession in 2003, which did not happen (SARB, 2012:S-153). As such, the incorrect prediction by the term spread lifted concerns about the consistency of the term spread in forecasting S.A. recessions. Moreover, this provided indications that there might be a weak relationship between the S.A. term spread and S.A. economic activity.
According to Bernard and Gerlach (1996:6), term spreads and economic activities across countries are correlated. As such, it is generally expected of foreign term spreads to provide predictions about domestic economic conditions. Thus the Chinese, U.S. and German term spreads were investigated in comparison to the S.A. term spread to determine the term spread that better forecast S.A. recessions. The choice of countries was based on a SARS report of January 2012, in which these countries were the top three destinations of S.A. value-add exports (SARS: 2012). As such, severe economic shocks in these countries would affect the revenue streams into S.A. and hence the economic conditions. The dynamic probit model was used as an econometric model in evaluating the performance of the term spreads investigated.
To provide a response to the main research question, it was important to understand the theoretical background behind the term structure of interest rates, and its formation. Moreover, it was equally important to understand the relationship between the terms structure of interest rates and economic activities and consequent recessions. The following sections summarize discussions in the following order: first, the term structure of interest rates and economic activity; second, the relevance of the yield curve in practice; third, the predictive ability of the term spreads investigated in this study in
Page 98 of 119
forecasting S.A. recessions. Lastly, this chapter provides the conclusion to the study and provides recommendations for further research.