A SMALL-SCALE DSGE MODEL FOR FORECASTING THE SOUTH AFRICAN ECONOMY

This paper uses a version of Hansen's (1985) Dynamic Stochastic General Equilibrium (DSGE) model to forecast the South African economy. The calibrated model, based on annual data over the period of 1970‐2000, is used to generate one‐ to eight‐quarters‐ahead out‐of‐sample forecast errors for the period of 2001:1 to 2005:4. The forecast errors are then compared with the unrestricted versions of the Classical and Bayesian VARs. A Bayesian VAR with relatively loose priors outperforms both the classical VAR and the DSGE model.     

A BVAR MODEL FOR THE SOUTH AFRICAN ECONOMY

The paper develops a Bayesian vector autoregressive (BVAR) model of the South African economy for the period of 1970:1‐2000:4 and forecasts GDP, consumption, investment, short‐term and long term interest rates, and the CPI. We find that a tight prior produces relatively more accurate forecasts than a loose one. The out‐of‐sample‐forecast accuracy resulting from the BVAR model is compared with the same generated from the univariate and unrestricted VAR models. The BVAR model is found to produce the most accurate out of sample forecasts. The same is also capable of correctly predicting the direction of change in the chosen macroeconomic indicators.     

FORECASTING THE SOUTH AFRICAN ECONOMY WITH VARs AND VECMs

The paper develops a Bayesian Vector Error Correction Model (BVECM) of the South African economy for the period 1970:1‐2000:4 and forecasts GDP, consumption, investment, short and long term interest rates, and the CPI. We find that a tight prior produces relatively more accurate forecasts than a loose one. The out‐of‐sample‐forecast accuracy resulting from the BVECM is compared with those generated from the Classical variant of the VAR and VECM and the Bayesian VAR. The BVECM is found to produce the most accurate out of sample forecasts. It also correctly predicts the direction of change in the chosen macroeconomic indicators.     

SPATIAL BAYESIAN METHODS OF FORECASTING HOUSE PRICES IN SIX METROPOLITAN AREAS OF SOUTH AFRICA

This paper estimates Spatial Bayesian Vector Autoregressive (SBVAR) models, based on the First-Order Spatial Contiguity and the Random Walk Averaging priors, for six metropolitan areas of South Africa, using monthly data over the period of 1993:07 to 2005:06. We then forecast one- to six-months-ahead house prices over the forecast horizon of 2005:07 to 2007:06. When we compare forecasts generated from the SBVARs with those from an unrestricted Vector Autoregressive (VAR) and the Bayesian Vector Autoregressive (BVAR) models based on the Minnesota prior, we find that the spatial models tend to outperform the other models for large middle-segment houses; while the VAR and the BVAR models tend to produce lower average out-of-sample forecast errors for middle and small-middle segment houses, respectively. In addition, based on the priors used to estimate the Bayesian models, our results also suggest that prices tend to converge for both large- and middle-sized houses, but no such evidence could be obtained for the small-sized houses.     

BAYESIAN METHODS OF FORECASTING INVENTORY INVESTMENT

This paper develops a Bayesian Vector Error Correction Model (BVECM) for forecasting inventory investment. The model is estimated using South African quarterly data on actual sales, production, unfilled orders, price level and interest rate, for the period 1978 to 2000. The out-of-sample-forecast accuracy obtained from the BVECM over the forecasting horizon of 2001:1 to 2003:4, is compared with those generated from the classical variant of the Vector Autoregresssive (VAR) model and the VECM, the Bayesian VAR, and the recently developed ECM by Smith et al. , for the South African economy. The BVECM with the most-tight prior outperforms all the other models, except for a relatively tight BVAR which also correctly predicts the direction of change of inventory investment over the period of 2004:1 to 2006:3.     

Monetary Policy and Natural Disasters in a DSGE Model

In the immediate aftermath of Hurricane Katrina, speculation arose that the Federal Reserve might respond by easing monetary policy. This article uses a dynamic stochastic general equilibrium (DSGE) model to investigate the appropriate monetary policy response to a natural disaster. We show that the standard Taylor rule response in models with and without nominal rigidities is to increase the nominal interest rate. That finding is unchanged when we consider the optimal policy response to a disaster. A nominal interest rate increase following a disaster mitigates both temporary inflation effects and output distortions that are attributable to nominal rigidities.     

Bayesian analysis of time-varying parameter vector autoregressive model for the Japanese economy and monetary policy

This paper analyzes the time-varying parameter vector autoregressive (TVP–VAR) model for the Japanese economy and monetary policy. The parameters are allowed to follow a random walk process and estimated using the Markov chain Monte Carlo method. The empirical result reveals the time-varying structure of the Japanese economy and monetary policy during the period from 1981 to 2008. The marginal likelihoods of the TVP–VAR model and other fixed parameter VAR models are estimated for model comparison. The estimated marginal likelihoods indicate that the TVP–VAR model best fits the Japanese economic data.     

Are structural parameters of DSGE models stable in Korea?

This paper examines whether the structural parameters of dynamic stochastic general equilibrium (DSGE) models are stable over time in Korea. By estimating a DSGE model, we find evidence that the 1997 Asian financial crisis did not change structural parameters in spite of significant policy changes and institutional reforms. This empirical finding has important significance for Korea's policymakers, as they can no longer rely on a DSGE modeling strategy for policy analysis and forecasting without structural parameter stability. Moreover, this paper shows that the current DSGE model is superior to simple time series models in forecasting key macroeconomic variables in most cases. Finally, the current model successfully reproduces the relative volatilities of consumption, investment and hours worked with respect to output as well as the pattern of contemporaneous correlations of output with other variables.     

Inflation Forecast Targeting: An Alternative Approach to Estimating the Inflation‐Output Variability Tradeoff

We suggest a new way of computing the inflation‐output variability tradeoff under inflation forecast targeting. Our approach is based on dynamic, stochastic simulations of the average inflation rate over a two‐year horizon using the moving average representation of a vector autoregressive (VAR) model. Using real‐time data over two samples, we estimate the inflation‐output variability tradeoff for the United States and show that it has shifted favorably over time. We analyze the policy interventions required to achieve target inflation in each sample and compare these interventions over time.     

我国通货膨胀成因解析--基于开放经济体DSGE模型的研究

为定量探讨国内外各种经济冲击如何影响我国通货膨胀及其解释力大小,本文构建出一个符合中国经济特性的开放经济体系动态随机一般均衡（ DSGE）模型,并基于1997-2013年季度数据进行贝斯估计。研究结果表明本文模型可以很好地匹配主要宏观变量的数据特性。通过模型对通货膨胀的方差分解发现,我国通货膨胀波动最主要解释因素依次为生产技术冲击、货币政策冲击、及国外价格冲击。藉由模型对通货膨胀的历史拆解发现,样本期间内2002Q2-2004Q3、2006Q3-2008Q1、及2009Q2-2011Q3三轮通胀上升周期中最主要推动因素分别为投资效率冲击、国外价格冲击、及货币政策冲击。     

Risk and Macroeconomic Activity

This article explores the macroeconomic role that risk plays using the BAA‐AAA spread as the measure of risk. First, it shows that meaningful upward movements in this spread are associated with recessions and their severity. Second, it includes the BAA‐AAA spread in a structural vector‐autoregression (VAR) to identify a shock‐to‐risk and finds that it causes a statistically significant and economically important decrease in output as well as increased holdings of real‐money balances. Third, it uses historical decompositions to show that the shock‐to‐risk explains an important part of the declines in output during four post‐1970 recessions. Notably, the shock‐to‐risk explains almost none of the decline in output during 2001 prior to the September 11, 2001, terrorist attacks but does clarify why the recovery was relatively weak afterwards, and it explains the bulk of the decline in output during 2008 and 2009.     

Financial Factors in the Business Cycle of a Small Open Economy: The Case of Korea

Financial factors influencing the business cycle have received considerable attention in recent years in the aftermath of the global financial crisis in 2008. This paper examines the role of financial factors in the business cycle by considering Korea, a small open economy, that experienced a severe financial crisis in 1997 as well as the recent global financial crisis. We estimate small open economy Bayesian DSGE (dynamic stochastic general equilibrium) models with financial factors and analyze the role of these financial factors in the business cycle in the context of Korea. The results indicate that the model based on an endogenous financial accelerator and a modified monetary policy rule provides a better explanation to the data than that without the financial factors and justify the recent attention to financial factors influencing the business cycle.     

How Useful are DSGE Macroeconomic Models for Forecasting?

A review of the literature shows that forecasts from DSGE models are not more accurate than either times series models or official forecasts, but neither are they any worse. Further, all three types of forecast failed to predict the recession that started in 2007 and continued to forecast poorly even after the recession was known to have begun. The aim of this paper is to investigate why these results occur by examining the structure of the solution of DSGE models and compare this with pure time series models. The main factor seems to be the dynamic structure of DSGE models. Their backward-looking dynamics gives them a similar forecasting structure to time series models and their forward-looking dynamics, which consists of expected values of future exogenous variables, is difficult to forecast accurately. This suggests that DSGE models should not be tested through their forecasting ability.     

Interdependence between The Netherlands and Germany: Forecasting with VAR models

Summary The German economy is usually assumed to take a leading position. In principle this gives smaller countries, which are dependent on Germany, the opportunity to predict their own economic future conditional on the state of the German economy. This paper uses this opportunity for The Netherlands by applying a Vector Auto Regressive model on Dutch and German series. Because the traditional VAR models appear to be overparameterized, their forecast performance can be improved significantly by using shrinkage estimators based on the so-called Minnesota prior. Such a Bayesian VAR forecasts well and confirms the interdependence between Germany and The Netherlands. Variance decomposition of forecast errors and impulse response simulations strengthen the impression that the BVAR model properties are plausible.The author works at the Econometric Research and Special Studies Department of the Nederlandsche Bank. He is grateful to M.M.G. Fase, C.C.A. Winder and two anonymous referees for their useful comments and to R.B.M. Vet for his assistance in various calculations.     

What Drives Ireland’s Housing Market? A Bayesian DSGE Approach

We study the drivers of fluctuations in the Irish housing market by developing and estimating a dynamic stochastic general equilibrium (DSGE) model of Ireland as a member of the European Economic Monetary Union (EMU). We estimate the model with Bayesian methods using time series for both Ireland and the rest of the EMU for the period from 1997:Q1 to 2008:Q2. We find that housing preference and technology shocks are the main drivers of fluctuations in house prices and residential investment. A standard regression analysis shows that a good part of the variation of housing preference shocks can be explained by unmodeled demand factors that have been considered in the empirical literature as important determinants of Irish house prices.     

On the choice of monetary policy rules for China: A Bayesian DSGE approach

Motivated by the institutional features of China's monetary policy, this paper aims at identifying the most data favored monetary policy rule for China within a dynamic stochastic general equilibrium (DSGE) model framework. In a canonical New-Keynesian DSGE model, we carry out a positive analysis by employing Bayesian methods to estimate three main categories of monetary policy rules, namely a Taylor-type interest rate rule, a money growth rule and an expanded Taylor rule with money. Based on China's quarterly data from 1996Q2 to 2015Q4, our estimation shows that the expanded Taylor rule obtains the best empirical fit to the data. Moreover, impulse responses and forecast error variance decompositions demonstrate that monetary policy rules with or without money provide very different implications for the policy behavior. Our results ultimately suggest that money has so far been more closely targeted than nominal interest rate and still plays an important role as a monetary policy target in China. Furthermore, a conventional Taylor-type interest rate rule is not good enough yet to describe China's monetary policy behavior.     

Monetary policy and inflation in South Africa: A VECM augmented with foreign variables

We develop a structural cointegrated vector autoregressive (VAR) model with weakly exogenous foreign variables, known as an augmented VECM or VECX*, suitable for a small open economy like South Africa. This model is novel for South Africa in two ways: it is the first VECX* developed to analyse monetary policy and the first model that uses time‐varying trade weights to create the foreign series. We impose three significant long‐run relations (augmented purchasing power parity, uncovered interest parity and Fisher parity) to investigate the effect of a monetary policy shock on inflation. The results suggest the effective transmission of monetary policy.     

IMPLEMENTING A ROBUST RISK MODEL FOR SOUTH AFRICAN EQUITY MARKETS: A PEAK‐OVER‐THRESHOLD APPROACH

This paper implements a market risk model for the South African equity market using daily returns of the Johannesburg Stock Exchange All Share Index. Firstly, we separate positive returns from negative returns and model them using the peak‐over‐threshold (POT) method in order to compute the downside as well as upside risk measures separately. We thereafter compute the value‐at‐risk (VAR) and the expected shortfall (ES) estimates corresponding to upside and downside risks. We bootstrap these risk measures and compute their standard errors and confidence intervals (CIs) to see whether they fall inside these CIs. Secondly, we compute out‐sample forecasts of VAR estimates using the POT method and the generalised autogressive conditional heteroscedasticity process. Three backtesting methodologies are employed: the unconditional and conditional coverage tests and the counting of number of exceptions according to Basel II green zone. We find that all our VAR and ES estimates are well inside their CIs and that at lower quantiles, parametric ES estimates are equal to POT‐ES estimates, although the difference between the two is more pronounced at higher quantiles (99% or higher). Furthermore, our market risk model falls into the Basel II green zone, as it produces fewer exceptions in out‐sample space.     

FORECASTING THE SOUTH AFRICAN ECONOMY WITH GIBBS SAMPLED BVECMs

The paper uses the Gibbs sampling technique to estimate a heteroscedastic Bayesian Vector Error Correction Model (BVECM) of the South African economy for the period 1970:1‐2000:4, and then forecasts GDP, consumption, investment, short and long term interest rates, and the CPI over the period of 2001:1 to 2005:4. We find that a tight prior produces relatively more accurate forecasts than a loose one. The out‐of‐sample‐forecast accuracy resulting from the Gibbs sampled BVECM is compared with those generated from a Classical VECM and a homoscedastic BVECM. The homoscedastic BVECM is found to produce the most accurate out of sample forecasts.     

Business cycle transmission and interdependence between Japan and Australia

This paper investigates business cycle transmission and interdependence between Australia and Japan over the period 1961.1–1994.4. Vector autoregression (VAR) and vector error correction (VEC) models were constructed utilizing GDP/GNPs, producer prices, interest rates and money supplies. The model is tested for cointegration. Two cointegrating vectors are found, and a vector error correction (VEC) model is estimated. The coefficients and the F-tests of the VEC are used to measure the effect of one economy upon the other. Impulse responses from a VAR are examined for evidence of business cycle transmission, and recursive least squares estimates are used to check for structural change in the relationship. Figures are used to graphically demonstrate these relationships and have been collected in an appendix, which can be found at the end of the text.While the two countries engage in a close trading relationship, the two economies are found to be only somewhat interdependent in macroeconometric terms. Japan is found to transmit some of its business cycle fluctuations to Australia, but there is little reverse transmission.