Do we need a global VAR model to forecast inflation and output in South Africa?

This study determines whether the global vector autoregressive (GVAR) approach provides better forecasts of key South African variables than a vector error correction model (VECM) and a Bayesian vector autoregressive (BVAR) model augmented with foreign variables. The article considers both a small GVAR model and a large GVAR model in determining the most appropriate model for forecasting South African variables. We compare the recursive out-of-sample forecasts for South African GDP and inflation from six types of models: a general 33 country (large) GVAR, a customized small GVAR for South Africa, a VECM for South Africa with weakly exogenous foreign variables, a BVAR model, autoregressive (AR) models and random walk models. The results show that the forecast performance of the large GVAR is generally superior to the performance of the customized small GVAR for South Africa. The forecasts of both the GVAR models tend to be better than the forecasts of the augmented VECM, especially at longer forecast horizons. Importantly, however, on average, the BVAR model performs the best when it comes to forecasting output, while the AR(1) model outperforms all the other models in predicting inflation. We also conduct ex ante forecasts from the BVAR and AR(1) models over 2010:Q1–2013:Q4 to highlight their ability to track turning points in output and inflation, respectively.     

The limited usefulness of macroeconomic Bayesian VARs when estimating the probability of a US recession

The Bayesian VAR model provides a convenient tool for generating predictive densities and making probability statements regarding the future development of economic variables. This paper investigates the usefulness of standard macroeconomic Bayesian VAR models to estimate the probability of a US recession. Defining a recession as two quarters in a row of negative GDP growth, the probability is estimated for two quarters of the most recent US recession, namely 2008Q3–2008Q4. In contrast to judgemental probabilities from this point in time, it is found that the BVAR assigns a very low probability to such an event. This is true also when survey data, which generally are considered as good leading indicators, are included in the models. We conclude that while Bayesian VAR models are good forecasting tools in many cases, the results in this paper raise question marks regarding their usefulness for predicting recessions.     

Measuring the output gap in Switzerland with linear opinion pools

We use the recently proposed linear opinion pool methodology of Garratt et al. (2014) to construct real-time output gap estimates for Switzerland over the out-of-sample period from 2003:Q1 to 2015:Q4. The model space consists of a large number of bivariate VAR specifications for the output gap and inflation, with each VAR specification using a different estimate of the output gap, lag order, and structural break information. We find that the linear opinion pool performs rather poorly. Real-time estimates of the output gap are no more accurate than those from some simple benchmark models, no more robust to ex post revisions than the real-time estimates of the individual univariate output gaps, and do not produce more accurate forecasts of inflation. The key driver of ‘good’ forecast performance is structural break information. Once the same structural break information is conditioned upon in all prediction models, the gain from averaging over many different pools of models that utilize various output gap estimates or lag structures in the VAR specification is of negligible magnitude.     

Forecasting the Australian economy with DSGE and BVAR models

Reflecting the importance of commodities for the Australian economy, we construct a dynamic stochastic general equilibrium (DSGE) model of the Australian economy with a commodity sector. We assess whether its forecasts can be improved by using it as a prior for an empirical Bayesian vector autoregression (BVAR). We find that the forecasts from the BVAR tend to be more accurate than those from the DSGE model. Nevertheless, for output growth these forecasts do not outperform benchmark models, such as a small open economy BVAR estimated using the standard priors for forecasting. A Bayesian factor augmented vector autoregression produces the most accurate near-term inflation forecasts.     

Comparative accuracy of forecasts of inflation: a Canadian study

An inaccurate forecast of inflation is costlier to economic agents when the inflation rate is high and volatile. In this situation, the use of more sophisticated and information-oriented forecasting models become economically efficient. We test this hypothesis by analysing the forecasting accuracy of vector auto-regressive (VAR), auto-regressive integrated moving average (ARIMA) and static expectation models. We use Canadian data and divide the post-sample forecasting period into four sub-periods, based on high/low and volatile/stable inflation. Prediction errors are compared for both short-term and long-term forecasts. Finally, the paper proposes a portfolio approach for obtaining a more accurate forecast of inflation.     

A small-scale DSGE-VAR model for the Romanian economy

This paper describes the theoretical structure and the estimation results for a DSGE-VAR model for the Romanian economy, an inflation targeting country since 2005. Having as benchmark the New-Keynesian model of Rabanal and Rubio-Ramirez (2005), the main additional feature introduced refers to the extension to a small open economy setting in order to account for this specific aspect of the Romanian economy.Within the inflation targeting monetary policy regime, forecasts of central macro variables, inflation in particular, play an important part. Because inflation reacts to monetary measures with a considerable lag, the central bank's policy has to be forward-looking. Based on univariate measures of forecast performance, it is shown that the VAR with DSGE model prior produces forecasts that improve on those obtained using an unrestricted VAR model and the popular Minnesota prior in case of inflation, real exchange rate and nominal interest rate. Moreover, the DSGE-VAR model is informative about the structure of the economy and can help the “story-telling” in the central banks.     

Forecasting with a state space time-varying parameter VAR model: Evidence from the Euro area

Standard VAR and Bayesian VAR models are proven to be reliable tools for modeling and forecasting, yet they are still linear and they do not consider time-variation in parameters. VAR modeling is subject to the Lucas critique and fails to take into account the inherent nonlinearities of the economy, while it can only be utilized in the analysis of stationary series and in many cases stationarity assumptions are too restrictive. A novel time-varying multivariate state-space estimation method for vector autoregression models is introduced. For the time-varying parameter model (TVP-VAR), the parameters are estimated using a multivariate specification of the standard Kalman filter (Harvey, 1990) combined with a suitable extension of the univariate methodology framework of Kim and Nelson (1999). The TVP-VAR model as well as standard VARs and Bayesian VARs, are used in a comparative investigation of their predicting performance for the monthly IP, CPI and Euribor rate of the EU economy. The total period covers 1999:1–2011:2 with an out-of-sample testing period of 2007:2 to 2011:2, which included the US sub-prime and the EU debt crisis sub-periods. The results varied across the investigated time series and indicated that the TVP-VAR model consistently outperforms the other models in case of the EU monthly CPI, while different specifications of the VAR and BVAR models for the IP and Euribor series provide with better forecasting performance. Interestingly, the robustness analysis showed that the TVP-VAR model provided with enhanced predictability in particular during “crisis times”.     

Short-term inflation forecasting models for Turkey and a forecast combination analysis

In this paper, we produce short term forecasts for the inflation in Turkey, using a large number of econometric models. In particular, we employ univariate models, decomposition based approaches (both in frequency and time domain), a Phillips curve motivated time varying parameter model, a suite of VAR and Bayesian VAR models and dynamic factor models. Our findings suggest that the models which incorporate more economic information outperform the benchmark random walk, and the relative performance of forecasts are on average 30% better for the first two quarters ahead. We further combine our forecasts by means of several weighting schemes. Results reveal that, the forecast combination leads to a reduction in forecast error compared to most of the models, although some of the individual models perform alike in certain horizons.     

Implementing and interpreting indicators of core inflation: the case of France

This paper presents a comparison of alternative indicators of underlying or “core” inflation in the French case. Four broad measures are considered and implemented. The first two are inflation excluding food and energy, and the trimmed inflation indicator. We then implement two methods relying on time-series models: the Dynamic Factor Index and the structural VAR approach.  Each indicator stresses on a particular type of shock on the inflation rate, so that no simple ranking of the measures emerges. Combining the various indicators conveys valuable information for appraising short term inflation developments. As regards theoretical interpretation, no indicator is fully satisfactory, lacking an explicit representation of monetary policy. However, comparing forecast performance with respect to inflation provides some specific support in favor of trimmed mean indicators. First version received: January 2000/Final version received: March 2001     

Asymptotic confidence bands for the estimated autocovariance and autocorrelation functions of vector autoregressive models

This paper provides closed-form formulae for computing the asymptotic covariance matrices of the estimated autocovariance and autocorrelation functions of stable VAR models by means of the delta method. These covariance matrices can be used to construct asymptotic confidence bands for the estimated autocovariance and autocorrelation functions to assess the underlying estimation uncertainty. The usefulness of the formulae for empirical work is illustrated by an application to inflation and output gap data for the U.S. economy indicating the existence of a significant short-run Phillips-curve tradeoff.First version received: November 2002/Final version received: September 2003     

When did unsystematic monetary policy have an effect on inflation?

An important stylized fact to emerge from VAR estimates is that exogenous monetary policy shocks (also labelled unsystematic monetary policy) have a delayed, persistent, hump-shaped effect on inflation. I argue that this empirical pattern is fragile. In particular, it disappears when one examines periods without large shifts in the level of inflation (such as 1984–2005). An important consequence is that the hump-shaped VAR estimated response of inflation is not appropriate to fit stylized models of the response of inflation around a stable steady state inflation level.     

超额工资、外部成本渠道与 中国通货膨胀非线性关系研究 ——基于技术进步方式理论下的MSIAH VAR模型实证分析

本文利用MSIAH VAR模型就超额工资、外部成本、石油冲击等成本渠道对中国通货膨胀影响进行研究。实证结果发现,工资增长率超过产出增长率的超额工资加强了对中国的通货膨胀的推升作用,2003年后超额工资增长率与通胀率形成了相互推进的“超额工资增长-通胀”螺旋。在技术进步不足以消化成本上升时,外部输入成本和石油冲击对于国内的通胀作用也将逐步加强。但M2的高速增长率似乎没有对通胀带来明显的拉动作用,随着房地产等资产投机市场的回调,流动性回流到实体经济可能存在催生通胀的隐患。     

Forecasting house prices for the four census regions and the aggregate US economy in a data-rich environment

This article considers the ability of large-scale (involving 145 fundamental variables) time-series models, estimated by dynamic factor analysis and Bayesian shrinkage, to forecast real house price growth rates of the four US census regions and the aggregate US economy. Besides the standard Minnesota prior, we also use additional priors that constrain the sum of coefficients of the VAR models. We compare 1- to 24-months-ahead forecasts of the large-scale models over an out-of-sample horizon of 1995:01–2009:03, based on an in-sample of 1968:02–1994:12, relative to a random walk model, a small-scale VAR model comprising just the five real house price growth rates and a medium-scale VAR model containing 36 of the 145 fundamental variables besides the five real house price growth rates. In addition to the forecast comparison exercise across small-, medium- and large-scale models, we also look at the ability of the ‘optimal’ model (i.e. the model that produces the minimum average mean squared forecast error) for a specific region in predicting ex ante real house prices (in levels) over the period of 2009:04 till 2012:02. Factor-based models (classical or Bayesian) perform the best for the North East, Mid-West, West census regions and the aggregate US economy and equally well to a small-scale VAR for the South region. The ‘optimal’ factor models also tend to predict the downward trend in the data when we conduct an ex ante forecasting exercise. Our results highlight the importance of information content in large number of fundamentals in predicting house prices accurately.     

Oil windfall,public spending and price stability: modelling inflation in Chad

This article examines the determinants of inflation in Chad using quarterly data from 1983:Q1 to 2009:Q3. The analysis is based on a single-equation model, completed by a Structural Vector Autoregression (SVAR) model to capture inflation persistence. The results show that the main determinants of inflation in Chad are rainfall, foreign prices, exchange rate movements and particularly public spending, which soared following the onset of oil production in 2003. The effects of rainfall shocks and changes in foreign prices on inflation persist during six quarters. Changes in public spending and the nominal exchange rate affect inflation during three and four quarters, respectively.     

FORECASTING GLOBAL EQUITY INDICES USING LARGE BAYESIAN VARS

This paper proposes a large Bayesian Vector Autoregressive (BVAR) model with common stochastic volatility to forecast global equity indices. Using a monthly dataset on global stock indices, the BVAR model controls for co‐movement commonly observed in global stock markets. Moreover, the time‐varying specification of the covariance structure accounts for sudden shifts in the level of volatility. In an out‐of‐sample forecasting application we show that the BVAR model with stochastic volatility significantly outperforms the random walk both in terms of point as well as density predictions. The BVAR model without stochastic volatility, on the other hand, shows some merits relative to the random walk for forecast horizons greater than six months ahead. In a portfolio allocation exercise we moreover provide evidence that it is possible to use the forecasts obtained from our model with common stochastic volatility to set up simple investment strategies. Our results indicate that these simple investment schemes outperform a naive buy‐and‐hold strategy.     

Business Cycles in an Estimated DSGE Model of China

A small-scale New-Keynesian dynamic stochastic general equilibrium model is estimated by maximum likelihood method using quarterly data of China. Model specifications and parameter equalities between various competing model variants are addressed by formal statistical hypothesis tests, while implications for business cycle fluctuations are evaluated via a variance decomposition experiment, second-moments matching, and some out-of-sample forecast exercises. It is highlighted that both forward and backward components are important for the dynamics of output, inflation and real balances. The monetary authority will take a sufficient aggressive stance, with a significant lagged response, to the current inflation pressure, while leaving less attention to changes in aggregate output. Variance decomposition reveals that large percentages of variations in real and nominal variables are explained by the highly volatile preference shock and potential output shock, respectively. When nominal and real frictions as well as additional shocks are included, our estimated model overall can successfully reproduce the stylized facts of business cycles in the actual data of China and even frequently outperform those forecasts from an unconstrained VAR.     

An Evaluation of Alternative Methods of Forecasting Australian Inflation

The purpose of this paper is to evaluate the forecast of Australian inflation based on four alternative procedures: a univariate time series model, an interest rate model, an error correction model and a public survey of inflation forecasts. We derive estimates of expected and unexpected inflation from each of the methods and compare the out-of-sample forecasting results. Based on a range of evaluation criteria, the time series model dominates the other models, with the interest rate model, the error correction model and the survey forecasts following in that order.     

Financial integration and the term structure of interest rates

This paper presents a model of the term structure for an open economy. A flexible VAR approach is used to model macroeconomic growth, inflation, short rate and the yield spread. Then the term structure is built given restrictions implied by the no-arbitrage condition. Contrary to previously proposed macrofinance models of the term structure, the model suggested here explicitly accounts for financial and real spillovers between economies. As documented in the paper, foreign macroeconomic factors contain a lot of information about the domestic term structure of yields. Put to data, the model explains the dynamics of yields very well. It provides better out-of-sample forecasting results than the closed economy models. Openness induces more variability in the estimated term premia of yields with shorter maturities.     

Evaluating the effect of monetary policy on unemployment with alternative inflation forecasts

This paper explores the role that inflation forecasts play in the uncertainty surrounding the estimated effects of alternative monetary rules on unemployment dynamics in the euro area and the US. We use the inflation forecasts of 8 competing models in a standard Bayesian VAR to analyse the size and the timing of these effects, as well as to quantify the uncertainty relative to the different inflation models under two rules. The results suggest that model uncertainty can be a serious issue and strengthen the case for a policy strategy that takes into account several sources of information. We find that combining inflation forecasts from many models not only yields more accurate forecasts than those of any specific model, but also reduces the uncertainty associated with the real effects of policy decisions. These results are in line with the model-combination approach that central banks already follow when conceiving their strategy.     

Post-stabilization inflation dynamics in Slovenia

This paper investigates the inflation process in Slovenia through an examination of some commonly used determinants of inflation in transition economies. Granger causality tests and an analysis of unrestricted VAR models suggest a strong linkage between both growth in broader monetary aggregates and changes in the tolar-deutsche mark exchange rate on retail price inflation. While the growth in wages affects inflation, it appears that both changes in the exchange rate and growth in monetary aggregates provide the initial impulse. A discussion of the present money-exchange rate policy framework and its influence on inflation is also provided.