Abstract
Evidence in favour of the ability of the term spread to forecast economic growth of the South African economy is non-existent. This could be due to the term spread aggregating information contained in the expected spread and the term premium. To decompose the term spread into its subcomponents, we develop an estimable small open economy new Keynesian dynamic stochastic general equilibrium (SOENKDSGE) model of the inflation targeting South African economy. The SOENKDSGE model is estimated with Bayesian methods over the quarterly period of 2000:01–2014:04. We then use a linear predictive regression framework to analyse the out-of-sample forecasting ability of the aggregate term spread, as well as the expected spread and term premium. Our forecasting results fail to detect forecasting gains from the aggregate term spread and also the term premium, but the expected spread is found to contain important information in forecasting output growth over short- to medium-run horizons, over the period of 2004:01–2014:04, using an in-sample period of 2000:01–2003:04. The results therefore highlight the importance of the forward-looking component of the term spread—the expected spread—in forecasting the output growth of South Africa.
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Notes
See Mitchell (1913).
Both the Akaike information criterion and the Schwarz information criterion chose a lag length of one for the growth rate of GDP, allowing for a maximum lag of four.
Complete details of the Bai and Perron (2003) test of multiple structural breaks are available upon request from the authors.
For the purposes of this paper, \(L=40\) such that the L-period bonds represent South African 10-year government bonds.
Nominal bond holding \(B_{L,t}\) is deflated by the domestic price level \(P_t^d\), and rendered stationary by removing the common trend as reflected by the permanent technology shock \(z_t\), as follows: \(b_{L,t} = B_{L,t}/(z_t P_t^d)\).
The small open economy model structure largely follows the lines of Adolfson et al. (2007), as it forms the backbone of an operational DSGE model that is used for policy analysis in an inflation targeting central bank. Nevertheless, the model laid out below departs from Adolfson et al. (2007) in three key aspects. Firstly, allowance is made for the fact that on average, inflation in South Africa exceeds that of its trading partners. In the context of the model, this is achieved by assuming that South Africa has a higher steady-state inflation rate. By implication, these differential inflation rates yield a nominal exchange rate depreciation in steady state, as predicted by purchasing parity theory. Secondly, it is assumed that there is no cost channel of monetary policy; hence, firms do not borrow their wage bill. Finally, apart from lump-sum transfers, the role of taxes in the model is disregarded.
The Centre for Economic Research and its Application (CEPREMAP), together with Douglas Laxton’s team at the IMF in Washington, DC, develops and supports the GPM: a large-scale quarterly macroeconomic model of the world economy which consists of approximately 35 countries, aggregated into six regions.
That is, the rate of return on capital has a slightly less than one-to-one relationship with variable capital utilization.
Note that for the sake of consistency with the estimation of the SOENKDSGE model based on the observable variables, we use the repo rate as the measure of the short-term rate of interest instead of the three-month Treasury Bill rate, while decomposing the term spread. We do not expect our results to be affected by such a choice, given that the repo rate and the three-month Treasury bill rate virtually comove and share a (positive) correlation of 0.94, which is significant at 1% level of significance.
Bernanke (2006), in his 2006 address to The Economic Club of New York, cited the narrowing of the term premium as having a stimulative impact on economic activity rather than indicative of an expected decline in economic activity. While both practitioners and academics hold true to the negative correlation between the term premium and economic activity, there is more ambiguity on its predictive power for forecasting economic activity.
A comparison of out-of-sample forecasts for output growth from this current DSGE model which incorporates the term structure relative to the one that does not, showed that our DSGE framework consistently predicted output growth better over two to eight quarters ahead, with the exception of the first quarter. Complete details of these results are available upon request from the authors.
Similar results were also obtained from the in-sample analysis, with only ES showing predictive power. Complete details of these results are available upon request from the authors.
Complete details of the NP unit root tests are available upon request from the authors.
As far as the in-sample is concerned, we observed predictability for all the three first-differenced predictors, with ES producing the strongest prediction, followed by TS and TP.
This line of thinking was further vindicated when we could not detect out-of-sample forecasting gains from the predictive regression model which contained both the expected spread and term premium simultaneously. Complete details of these results are available upon request from the authors.
Based on the suggestions of an anonymous referee, we also used a nonlinear framework, namely the quantiles-based version of the linear predictive regression used in our paper. In general, our results were in line with those obtained under the linear model, in the sense that the expected spread is found to be a stronger predictor than the overall term spread and the term premium, and that too around the median of the conditional distribution of the growth rates, rather than the quantiles of 0.25 and 0.75, capturing recessions and expansions, respectively. We have refrained from presenting these results formally in the paper, because tests of nonlinearity and slope equality across quantiles did not find any evidence in favour of the quantile (i.e. nonlinear) model over the linear framework we have used in the paper. However, complete details of these results are available upon request from the authors.
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We would like to thank two anonymous referees for many helpful comments. However, any remaining errors are solely ours.
A The linearized model
A The linearized model
1.1 Households
Optimal L-period bond holdings, \(\hat{b}_{L,t}\)
Money holdings, \(\hat{m}_t\)
Wage setting (real) \(\hat{w}_{t}\)
Consumption Euler equation, \(\hat{c}_{t}\)
Investment Euler equation, \(\hat{i}_{t}\)
Price of installed capital, \(\hat{P}^k_{t}\)
Capital’s law of motion, \(\hat{k}_{t}\)
Capital utilization, \(\hat{u}_{t}\)
Capital services, \(\hat{k}^s_{t}\)
Optimal 1-period asset holdings, \(\hat{\psi }^z_{t}\)
Exchange rate’s modified UIP condition, \(\hat{S}_{t}\)
1.2 Firms
1.2.1 Domestic goods
Production, \(\hat{y}_{t}\)
Rental rate of capital, \(\hat{r}^k_{t}\)
Real marginal cost, \(\hat{mc}^{d}_{t}\)
Price setting, \(\hat{\pi }^{d}_{t}\)
1.2.2 Imported goods
Price setting: imported consumption goods, \(\hat{\pi }^{m,c}_{t}\)
Marginal cost: imported consumption goods, \(\hat{mc}^{m,c}_{t}\)
Price setting: imported investment goods, \(\hat{\pi }^{m,i}_{t}\)
Marginal cost: imported investment goods, \(\hat{mc}^{m,i}_{t}\)
1.2.3 Exported goods
Price setting: exported goods, \(\hat{\pi }^{x}_{t}\)
Marginal cost: exported goods, \(\hat{mc}^{x}_{t}\)
1.3 Government
Liabilities, \(\hat{\ell }_{t}\)
Expenditure’s budget constraint, \(\hat{g}_t\)
Taxation, \(\hat{\tau }_{t}\)
1.4 The Central Bank
Interest rate policy rule, \(\hat{R}_{t}^{}\)
where CPI inflation is given by
1.5 Relative prices, \(\hat{\gamma }_{t}^{i,j}\)
Consumption and investment goods
Imported consumption and investment goods
Export goods
Domestic–foreign goods relative price
Real exchange rate
1.6 Market clearing
Domestic goods market, \(\hat{y}_{t}\)
Net foreign assets, \(\hat{a}_{t}\)
1.7 AR(1) shock processes
where
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Gupta, R., Hollander, H. & Steinbach, R. Forecasting output growth using a DSGE-based decomposition of the South African yield curve. Empir Econ 58, 351–378 (2020). https://doi.org/10.1007/s00181-018-1607-4
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DOI: https://doi.org/10.1007/s00181-018-1607-4
Keywords
- Structural decomposition
- Term spread
- DSGE
- Predictive regression framework
- Forecasting output growth
- South Africa