Abstract
Alternative model specifications can affect the estimated structure and dynamics of the business cycle, which has important implications for policy analysis. This paper evaluates the consistency of model estimations in the extant literature and tests the sensitivity of alternative models tailored to the South African economy. We find that both parameter estimates and model dynamics are sensitive to model specification. Our findings suggest that a three-equation New-Keynesian model and a traditional open economy model provide qualitatively and quantitatively similar results to the benchmark medium-scale New-Keynesian model with sticky prices and wages, habit formation, and investment adjustment costs. However, significant differences from the benchmark New-Keynesian specification are revealed once financial frictions are included. In addition, the types of exogenous shocks included in the model are key determinants for the variation of results.
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Notes
- 1.
In particular, microfoundations relay the ability of these models to incorporate forward-looking, policy invariant, and heterogeneous agent behaviour (see, e.g., Ohanian et al. 2009). However, these models are not without their drawbacks. In the next section a discussion is undertaken to describe the current role for these type of models and the extensions that need to be incorporated to keep them relevant to the underlying economic environment.
- 2.
The role for DSGE models in providing accurate forecasts of macroeconomic indicators is not so clear cut. Although DSGE models can be used in conjunction with other macroeconometric tools, this is not its strong suit, and traditional econometric models might perform better in this regard. See, for example, the discussion in Blanchard (2018) about the relevance of DSGE models when it comes to predicting events or the path of macroeconomic indicators.
- 3.
Kotzé’s (2014) PhD thesis provides a detailed discussion and application of DSGE approaches similar to the objective of this chapter.
- 4.
Works-in-progress incl. Kotzé (2017) and Kemp (PhD).
- 5.
According to Krugman (2018) the big new idea that will reshape macroeconomics has not yet arrived. Many ideas have been forwarded, but none is taking the profession by storm.
- 6.
Blanchard (2018) compares DSGE models to a Meccano set, a platform that can easily integrate new elements developed outside the general equilibrium setting.
- 7.
That is, the model extends directly from the real business cycle (RBC) framework.
- 8.
The distinction between Calvo-type (fixed probability of adjustment) and Rotemberg-type (quadratic adjustment costs) price setting is unimportant at first-order approximation (Ireland 2004, 2007), and therefore our discussions in this paper will limit the details of alternative modelling approaches that deliver similar first-order outcomes. There are, however, important non-linear effects on adjustment dynamics and welfare (see, e.g., Leith and Liu 2016).
- 9.
Again, at a first-order approximation, the distinction of a deterministic (constant) growth or level steady-state is unimportant for our business cycle analysis here. Although, estimations will be sensitive to the specification and treatment of observational variables in the estimation of models (see Pfeifer 2018).
- 10.
For tractability, we simplify the model as much as possible and therefore ignore the more complex intertemporal modelling of internal habit formation (see Fuhrer 2000). Again, the distinction between internal (time non-separable) and external (exogenous) habit formation becomes important for higher-order approximations (e.g., uncertainty) or regime changes.
- 11.
Φ′ > 0, Φ′′ < 0, Φ′(δ) = 0, Φ(δ) = 0. Specifically, Φ(V t∕K t)K t = (κ v∕2δ)(V t∕K t − δ)2K t.
- 12.
Given that Y t, C t, and V t are observed data inputted into the model for estimation, the AR(1) stochastic process \(\xi ^g_t\) is included, as in Smets and Wouters (2007), to avoid stochastic singularity and to reduce estimation bias toward the other structural shocks.
- 13.
ρ i in the monetary policy rule (18) captures the persistence of innovations to \(\xi ^{i}_t\). In this context, it captures the degree of interest rate smoothing by the monetary authorities.
- 14.
Ireland (2011) assumes log preferences (σ c = 1) and linear disutility of hours worked (σ n = 0). In addition, to enrich the dynamics of the model, habit formation is endogenous to the representative household (internal). This specification is shown in Appendix 1. Note that the three-equation New-Keynesian model presented here follows very similarly to the three-equation model specification for the foreign economy in Hollander et al. (2019).
- 15.
Log-linearizing the optimality condition for wage-setting and solving for \(\tilde {w}_{t}\) gives the optimal reset wage equation:
$$\displaystyle \begin{aligned} \tilde{w}_{t} = \frac{(1-\theta_{w}\beta)}{(1 + \xi^{w}\sigma_{n})} E_{t}\sum_{i=0}^{\infty}(\theta_{w}\beta)^{i}\biggl({\chi}mrs_{t+i} + \xi^{w}{\sigma_{n}}w_{t+i} + p_{t+i} - \gamma_{w}\pi_{t+i-1} \biggr), \end{aligned} $$(25)where \(\chi {\equiv } \frac {{W}}{MRS^s{\mu ^w}}\). Combining (25) with the log-linearized wage index equation gives the aggregate sticky real wage (\(\hat {w}_t = w_t - p_t\)) equation:
$$\displaystyle \begin{aligned} \hat{w}_t &= \Omega{\beta}E_{t}\hat{w}_{t+1} + {\Omega}\hat{w}_{t-1} + {\Omega}\Omega^{*}(\hat{mrs}_{t} - \hat{w}_{t}) \\ &\quad + \Omega{\beta}E_{t}\hat{\pi}_{t+1} - {\Omega}\hat{\pi}_{t} - {\Omega}\theta_{w}\beta\gamma_{w}\hat{\pi}_{t} + {\Omega}\gamma_{w}\hat{\pi}_{t-1} ~, \end{aligned} $$(26)where \(\Omega = \frac {1}{(1 + \beta )}\). Equation 26 can be re-written in nominal wage inflation form as Eq. 24.
- 16.
For a given exchange rate, domestic importing retailers are import “price-takers”, but face a downward sloping domestic demand curve.
- 17.
I.e., RER equates the marginal utilities of consumption between the domestic and foreign households.
- 18.
I.e., the change in foreign demand for domestic goods given the foreign price of domestic goods relative to the foreign price of foreign goods can be expressed as: \(\hat {pr}^{h*}_{t} - \hat {pr}^{f*}_{t} = \hat {pr}^h_t - \hat {rer}_t\).
- 19.
Recent empirical evidence suggests that bank capital adequacy ratios in South Africa are procyclical (see, e.g., Akinsola and Ikhide 2017; Maredza 2015). For now, we abstract from measuring the extent of this endogenous procyclicality by requiring banks to target a non-binding capital-asset ratio that is subject to precautionary/regulatory shocks.
- 20.
Adrian and Shin (2010, 2013) show that banks (in the USA) tend to actively manage their capital-assets ratios through debt rather than equity. As such, bank capital accumulation is persistent (for a more-detailed discussion see, e.g., Hollander 2017). In addition, we assume that the market value of bank equity influences the book value of common (or tier 1) bank equity (Adrian and Shin 2010, 2013).
- 21.
We assume that the stock of equity shares are fixed, thus \(\hat {\psi } = \hat {\psi }^b + \hat {\psi }^{f} = 0\).
- 22.
- 23.
- 24.
See An and Schorfheide (2007) for a review of Bayesian methods for evaluating DSGE models.
- 25.
See the Appendix for data and sources.
- 26.
Aggregate tier 1 bank capital adequacy ratio data is used to proxy regulatory/precautionary capital requirement shocks (τ t). Quarterly tier 1 capital adequacy data can be compiled from the SARB’s BA700 and DI400 bank statistics for the entire sample period. This measure therefore includes the risk-adjusted exposure of banks and describes the contemporaneous regulatory/precautionary “target”. Due to data constraints, we have not included aggregate retail interest rates to households and firms. For households, the predominate mortgage rate is benchmarked to prime lending rates which tracks the short-term rate very closely. Interestingly, we obtain a very similar result if we derive the effective rate from the aggregate bank income statement statistics collected by the SARB. For firms, there is no immediate way to weight corporate bonds listed on the JSE. It is likely, however, that long-term corporate yields are benchmarked to the 10-year government bond rate, but this requires a stricter assumption of the role of monetary policy over the term structure in our model setup (see Hollander and Liu 2016a).
- 27.
For a useful guide on the treatment of observational variables in the estimation of DSGE models see Pfeifer (2018).
- 28.
The respective prior and posterior distribution statistics for the alternative models—as well as convergence statistics—reported in the technical appendix are available upon request.
- 29.
Laubscher provides a detailed discussion of South African business cycle fluctuations in Chap. 6 in this book. Notably, household consumption expenditure from 1974Q3 to 2017Q4 was more volatile than output, and it experienced noticeably steeper contractions and expansions from 1974Q3 to 1993Q2.
- 30.
Nakamura and Steinsson (2018) note how, in such cases, moments estimated from microeconomic data can “help to discipline models” (p. 62). In our analysis, the investment sub-block of the DSGE model does not have an outsized influence on the dynamics of the model. This, however, should not be brushed under the rug. As micro data informs parameterization of the Phillips curve for monetary policy analysis, investment/capital price elasticities should inform calibration of country specific estimations where investment adjustments are applicable to the question at hand.
- 31.
The official announcement came in August 1999.
- 32.
For the period 1995Q4–2005Q3, specifically, (Ortiz and Sturzenegger 2007, p. 671) find: κ π = 1.36 [1.02, 1.67]; κ y = 0.42 [0.18, 0.65]; ρ i = 0.68, [0.59, 0.77]. The authors also find negligible relevance for any “fear of floating” bias (interest rate reactions in response to nominal exchange rate fluctuations) but admit that the Taylor-rule specification likely understates the relative importance of controls on capital flows or forward market interventions, pre-2000.
- 33.
Figure 21 shows the prior and posterior distributions for the remaining FF-NK parameters not corresponding to SW-NK.
- 34.
This is easily implemented in Dynare using the identification command.
- 35.
Information on collinearity patters showing this is observable in Fig. 7. Similar relationships are found with the other parameters.
- 36.
Figures 19 and 20 show the log-posterior likelihood functions and log-likelihood kernels. If the estimated mode is the local mode, it should be at the maximum of the posterior likelihood. We also look at the Markov-chain Monte Carlo diagnostics from the Metropolis–Hastings algorithm of the corresponding parameters. Here, we observe convergence of the estimates which eliminates bimodal or poorly identified parameter distributions (see Figs. 22, 23, and 24 on the multivariate and univariate convergence diagnostic statistics).
- 37.
As noted in Sect. 3.2, following Smets and Wouters (2007) and Steinbach et al. (2014), the measurement error is included to avoid stochastic singularity in the aggregate resource constraint given that output, consumption, and investment are all observable variables. The models therefore exclude government and net exports (with the exception of SOE-NK), as well as genuine measurement errors from the data.
- 38.
SPS14, in contrast, find that domestic labour supply shocks, not specified here, contribute a significant portion towards variation in all three variables along with domestic price mark-up shocks. For SPS14, it is not possible to decompose the short- versus long-run contributions of each shock as only the unconditional (infinite horizon) FEVD results are presented. The short- to medium-run likely exhibits results more directly comparable.
- 39.
Unfortunately, it is not clear which data series are used to estimate the model, making inference difficult regarding the source or specification of the foreign economy. In addition, model misspecification is likely if the preference shock contributes significantly to the variance of output and interest rate but either has little explanatory power for other key macroeconomic variables (inflation, stock prices, consumption, or investment) or the FEVD of key macrovariables appear to be each individually driven by one particular shock; this appears to be the case (see PG16, Table 2, p. 172).
- 40.
Figure 2 suggests that the historically strong procyclicality between equity prices and output diminished since 2013. In fact, Venter, in Chap. 9 of this book, points out that “this indicator [equity prices] disconnected somewhat from domestic economic developments and was removed as a component series of the composite leading business cycle indicator in 2015”. Here, it is important to note that the SARB’s atheoretical indicator aims to achieve empirical accuracy alone, whereas the DSGE framework attempts to match theory and data. That is, the strict theoretical assumptions of the DSGE model promote a large and persistent role for equity prices. Furthermore, because the monthly leading indicator components are revised over time, the stock market may well be included in the future.
- 41.
Table 5 gives an indication of the relative importance of these demand- and supply-side components. Detailed historical decomposition figures are available upon request.
- 42.
Historical decomposition figures for the FF-NK model, as well as detailed shock breakdowns for the other models, are available upon request.
- 43.
Ireland’s preference shock explains a significant portion of the models fluctuations over the estimated great recession period.
- 44.
The UIP condition holds from the Euler equations of the domestic and foreign sectors: \(\hat {i}^b_t = \hat {i}^{b*}_t + E_{t}[\Delta {\hat {\varepsilon }_{t+1}}] + \hat {\Phi }_t\), which implies that the real exchange rate equates the marginal utilities of consumption between the domestic and foreign households.
- 45.
\(\hat {rer}_t = \hat {\varepsilon }_{t} + \hat {p}^{f*}_t - \hat {p}_t\) and \(\hat {pr}^{f}_t = \hat {p}^{f}_t - \hat {p}_t\).
- 46.
Derived from \(\hat {rer}_{t+1} = \hat {rer}_{t} + (\hat {i}^{b}_t - \hat {\pi }_{t+1}) - (\hat {i}^{b*}_t - \hat {\pi }^{f*}_{t+1} + \hat {\Phi }_t)\), where \((\hat {i}^{b}_t - \hat {\pi }_{t+1})\) and \((\hat {i}^{b*}_t - \hat {\pi }^{f*}_{t+1})\) are the domestic and foreign real interest rates on bonds, i.e., the Fisher equations; \(\hat {\Phi }_t = \hat {\mu }^{b*}_t - \hat {\mu }^{b}_t\).
- 47.
Primarily, this is to avoid stochastic singularity in the closed economy model. In this context, we can think of this exogenous component as being exogenous government spending shocks plus shocks to the trade balance not implied by the structural model.
- 48.
Following Steinbach et al. (2014, p. 24), we fix ρ g but estimate the cross-correlation coefficient.
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Appendices
Appendix 1: Ireland’s Three-Equation New-Keynesian Model
IS Curve
New Keynesian Phillips Curve
where \(\kappa _h = \frac {(1-\theta _p)(1-\theta _p\beta )}{\theta _p(1 + \beta \gamma _p)}\).
Monetary Policy Rule
where the growth rate of output is \(\Delta {y}_t = y_t - y_{t-1} + \xi ^a_t\). The marginal utility of consumption with internal habit formation and log preferences (i.e., σ c = 1):
where \(\xi ^b_t\) is the preference shock. We do not include this shock in the other models. Instead, the dynamics and relative contribution are compared to two similar shocks in the SW-NK framework, namely the risk premium shock and the exogenous spending shock. All three of these shocks are variant aggregate demand shocks.Footnote 43 A quick comparison with the three-equation model of the foreign economy in HGW19 shows how similar these two linearized frameworks are (see Appendix 2 and Sect. 4). Another key difference is that \(\xi ^a_t\) is a shock to the growth rate of output. A number of variables therefore need to be renormalized to remove the inherited unit root. Setting β = 0 in Eq. 38 gives the marginal utility of consumption with external habit:
The efficient level of output is
where the output gap is: \(y^{gap}_{t} = y_{t} - q_{t}\). The laws of motion for the preference shock, the (renormalized) cost-push shock, and the technology shock are: \(\xi ^b_t = \rho ^b\xi ^b_{t-1} + \epsilon ^b_t\); \(\xi ^p_t = \rho ^p\xi ^p_{t-1} + \epsilon ^p_t\); \(\xi ^a_t = \epsilon ^a_t\).
Appendix 2: The Open Economy
The open economy system of equilibrium conditions shown here is a direct extension of the “benchmark” closed economy New-Keynesian model presented in Sect. 3. The corresponding SW-NK model has some slight differences particularly related to the investment-specific efficiency shock. Specifically, the shock in SW-NK directly affects the accumulation of investment and its efficiency (via an adjustment cost) in physical capital accumulation, whereas the shock in SOE-NK affects the adjustment cost only (i.e., the relative price of capital, or, as described in SW-NK, the arbitrage equation for the value of capital). Also, we shut off the capital utilization channel in SW-NK. The flexible price (efficient) equilibrium follows by setting all price and wage rigidities to zero.
1.1 Aggregate Demand
Equation 40 domestic consumption of home goods; Eq. 41 domestic consumption of foreign goods; Eq. 42 Euler equation; Eq. 43 is the international risk sharing condition (where \(\hat {c}^{*}_{t} = \hat {y}^{*}_{t}\)).Footnote 44
1.1.1 Investment Schedule
where R k = (1∕β − (1 − δ)) and \(\hat {\xi }^v_t\) is the investment-specific shock, and \(q^k_t = \kappa _v(v_t - k_t) - \tilde {\xi }^v_t\).
1.2 Aggregate Supply and Inflation
1.2.1 (Real) Wage-Setting Equation
The real wage (\(\hat {w}_t = w_t - p_t\)) setting equation can be re-written in nominal wage inflation form as:
where \(\Omega ^{*} = \frac {(1 - \theta _w)(1 - \theta _{w}\beta )}{\theta _{w}(1 + \xi ^{w}\sigma _{n})}\), \(\Omega = \frac {1}{(1 + \beta )}\), and \(\hat {mrs}_{t} = \frac {\sigma _{c}}{1 - \phi }(\hat {c}_t - {\phi }\hat {c}_{t-1}) + \sigma _{n}{\hat {n}_t}\).
1.2.2 Domestic Production and Inflation (for Consumption Goods)
where \(\hat {mc}^{h}_{t} = \hat {\lambda }_{t}\) is the real marginal cost of production, and \(\kappa _h = \frac {(1-\theta _p)(1-\theta _{p}{\beta })}{\theta _{p}(1+\gamma _{p}\beta )}\).
1.2.3 Imported Inflation (for Foreign Consumption Goods)
where \(\kappa _f = \frac {(1-\theta _f)(1-\theta _{f}{\beta })}{\theta _{f}}\), and \(\hat {\psi }^{f}_{t}\) measures the l.o.p gapFootnote 45:
1.2.4 Inflation Aggregation Equations
From the inflation aggregation equations we have
Equation 52 can be re-written as
1.2.5 Evolution of Relative Prices
where Eq. 55 is the equation of motion for the relative purchasing power parity condition.Footnote 46 Here, we can think of nominal exchange rate changes \((\Delta {\hat {\varepsilon }_{t}})\) as the price adjustment mechanism that maintains equilibrium between foreign and domestic goods markets.
1.2.6 Evolution of Capital
1.3 Policy Rule
The central bank conducts monetary policy according to a Taylor (1993) rule:
The short-term nominal interest rate rises (falls) whenever inflation and output growth rise above (fall below) their average, or steady-state.
1.4 Foreign Economy
We assume a large open economy for the foreign market. This allows us to specify the foreign rate \(\hat {i}^{b*}_t\), foreign inflation \(\hat {\pi }^{*}_{t+1} = \hat {\pi }^{f*}_{t+1}\), and foreign consumption \(\hat {y}^{*}_t = \hat {c}^{*}_t\) according to the standard three-equation New-Keynesian model, namely an IS curve, a Phillips curve, and a Taylor-type policy rate rule.
where \(\hat {mc}^{*}_{t}\) is the real marginal cost of production, and \(\kappa _{*} = \frac {(1-\theta _{*})(1-\theta _{*}{\beta })}{\theta _{*}(1+\gamma ^{*}\beta )}\).
1.5 Aggregate Equilibrium
where ξ f∗ is the foreign price elasticity of demand for domestic goods (i.e., the change in foreign demand for domestic goods given the foreign price of domestic goods relative to the foreign price of foreign goods).
1.6 Exogenous Shocks
We include 10 shocks in the model. For the domestic economy, the monetary policy shock \((\epsilon ^{i}_t)\), as given in Eq. 59, is i.i.d., whereas the domestic technology shock, the domestic price mark-up shock, the wage mark-up shock and the investment-specific shock follow AR(1) processes: \(\hat {a}_t = \rho _{a}\hat {a}_{t-1} + \epsilon ^{a}_t\); \(\hat {\xi }^{p}_t = \rho _{p}\hat {\xi }^{p}_{t-1} + \epsilon ^{p}_t\): \(\hat {\xi }^{w}_t = \rho _{w}\hat {\xi }^{w}_{t-1} + \epsilon ^{w}_t\); \(\hat {\xi }^{v}_t = \rho _{v}\hat {\xi }^{v}_{t-1} + \epsilon ^{v}_t\). Following Smets and Wouters (2007), we include the exogenous spending shock in the aggregate resource constraint: \(\hat {\xi }^{g}_t = \rho _{g}\hat {\xi }^{g}_{t-1} + \epsilon ^{g}_t + \rho _{g,y}\epsilon ^{a}_t\).Footnote 47 Notably, ρ g,y captures spillover effects from domestic technology shocks.Footnote 48 The foreign economy follows with an i.i.d. monetary policy shock \((\epsilon ^{i*}_t)\) and the following supply shock: \(\hat {a}^{*}_t = \rho _{a*}\hat {a}^{*}_{t-1} + \epsilon ^{a*}_t\). In addition, the risk premium shocks on domestic-currency assets relative to the policy rate and for foreign-currency borrowing abroad (equivalent to negative demand shocks) are described as follows: \(\hat {\mu }^{b*}_t = \rho _{b}\hat {\mu }^{b*}_{t-1} + \epsilon ^{b*}_{t}\) and \(\hat {\mu }^{b}_t = \rho _{b}\hat {\mu }^{b}_{t-1} + \epsilon ^{b}_{t}\).
Appendix 3: The Financial Frictions Model
1.1 Households
Households’ Euler equation
Safe-assets demand
where 1∕(1 − β hR) is the asset-consumption ratio of households and is calibrated from the data as 0.856.
Equity price
where Γψ = ((1∕R h) − β h)ν h(1 − ϕ w).
Borrowing constraint
The household flow of funds constraint
Note that the household’s optimality condition for labour supply enters in the union wage-setting equation (see Eq. 84). Specifically, in the flexible price (efficient) equilibrium the marginal rate of substitution will equal the real wage. To therefore accommodate the efficient equilibrium in our setup, we must use Eq. 72 to close the model.
1.2 Entrepreneurs in Firm Production
Labour demand
Entrepreneurs’ Euler equation
Investment schedule
where Υk = ((1∕R e) − β e)ν eϕ k and the shadow price of capital is
where Υk = 0 is the same as Iacoviello (2005, p. 760 (A3)).
Production function
Borrowing constraint
Capital accumulation
The entrepreneur flow of funds constraint
1.3 Retailers and Labour Unions
The forward-looking Phillips curve with price indexation
The forward-looking sticky (real) wage equation with price indexation (where \(\hat {w}_t = w_t - p_t\))
where \(\Phi ^{*} = \frac {(1 - \theta _w)(1 - \theta _{w}\beta )}{\theta _{w}(1 + \xi ^{w}\sigma _{n})}\), \(\Phi = \frac {1}{(1 + \beta )}\). The marginal rate of substitution (mrs t) follows from the households optimality condition for labour supply:
where aa is \({1}/{(1 + \big (\frac {1}{R^h} - \beta _h\big ){\nu _h}{\phi _w})}\), and \({\hat {w}}_t\) is the real wage. Under no borrowing constraints, \({mrs}_{t} = \frac {\gamma }{1 - \phi }(c_t - \phi {c}_{t-1}) + \eta {h_t}\).
The real wage (\(\hat {w}_t = w_t - p_t\)) setting equation can be re-written in nominal wage inflation form as:
1.4 Banking Sector
Interbank rate
Bank capital accumulation
Profit function
Retail loan rate setting to households
where \(\mu _{h,t} = {\varepsilon ^h_t}/({\varepsilon ^h_t-1})\) is the stochastic markup shock.
Retail loan rate setting to entrepreneurs
where \(\mu _{e,t} = {\varepsilon ^e_t}/({\varepsilon ^e_t-1})\) is the stochastic markup shock.
Interbank spread and retail spread definitions (indexed by z = h, e)
1.5 Monetary Policy and Market Clearing Conditions
Appendix 4: Tables and Figures
See Table 6 and Figs. 19, 20, 21, 22, 23, and 24.
Appendix 5: Data and Sources
Data sources retrieved from the Federal Reserve Bank of St. Louis (FRED), the South African Reserve Bank (SARB), Eurostat, and OECD.stat:
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1.
Consumer Price Index of All Items in the USA [CPIAUCSL], UK [GBRCPIALL], Euro area [EZCCM086NEST], Japan [JPNCPIALL], and South Africa [ZAFCPIALL] retrieved from FRED (Copyright, 2018, OECD)
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2.
Real Gross Domestic Product by Expenditure for the USA [GDPC1], UK [GBQ661S], Euro area [EURSCAB1GQEA19], Japan [JPQ661S], and South Africa [ZAQ661S] retrieved from FRED (Copyright, 2018, OECD)
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3.
Interest Rates, Government Securities, 3-Month Treasury Bills for the USA [Gs3M], UK [GBM193N], Euro area [EZQ193N], Japan [JPM193N], and South Africa [ZAM193N] retrieved from FRED (Copyright, 2018, IMF and Eurostat)
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4.
Population: USA (Civilian Noninstitutional Population) [CNP16OV], Japan (15 and over) [JPQ647S], UK (Total) [POPNC; GBQ647S], and Euro area (Total) [POPNC; EZQ647S] (Copyright, 2018, OECD)
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5.
SARB, Final consumption expenditure by households: Total (PCE) [KBP6007L]
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6.
SARB, Gross fixed capital formation (Investment) [KBP6009L]
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7.
SARB, Nominal unit labour costs in the non-agricultural sectors [KBP7015L]
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8.
SARB, Total employment in the non-agricultural sectors [KBP7009L]
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9.
SARB, Balance of payments statistics [KBP5000L - KBP5010L]
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10.
SARB, Banking statistics
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Hollander, H., van Lill, D. (2020). On the Estimation and Application of Structural Decompositions of the South African Business Cycle. In: Boshoff, W. (eds) Business Cycles and Structural Change in South Africa. Advances in African Economic, Social and Political Development. Springer, Cham. https://doi.org/10.1007/978-3-030-35754-2_7
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