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A state space representation of a linearized DSGE model implies a VAR in terms of observable variables. The model is said be non-invertible if there exists no linear rotation of the VAR innovations which can recover the economic shocks. Non-invertibility arises when the observed variables fail to perfectly reveal the state variables of the model. The imperfect observation of the state drives a wedge between the VAR innovations and the deep shocks, potentially invalidating conclusions drawn from structural impulse response analysis in the VAR. The principal contribution of this chapter is to show that non-invertibility should not be thought of as an “either/or” proposition – even when a model has a non-invertibility, the wedge between VAR innovations and economic shocks may be small, and structural VARs may nonetheless perform reliably. As an increasingly popular example, so-called “news shocks” generate foresight about changes in future fundamentals – such as productivity, taxes, or government spending – and lead to an unassailable missing state variable problem and hence non-invertible VAR representations. Simulation evidence from a medium scale DSGE model augmented with news shocks about future productivity reveals that structural VAR methods often perform well in practice, in spite of a known non-invertibility. Impulse responses obtained from VARs closely correspond to the theoretical responses from the model, and the estimated VAR responses are successful in discriminating between alternative, nested specifications of the underlying DSGE model. Since the non-invertibility problem is, at its core, one of missing information, conditioning on more information, for example through factor augmented VARs, is shown to either ameliorate or eliminate invertibility problems altogether.

This paper aims to study the monetary transmission mechanism of China from January 1996 to December 2009 under endogenous structural breaks.

The study constructs a benchmark VAR model and then adds the proxy variables for four channels of monetary policy transmission as endogenous or exogenous variables in the model to study the transmission mechanism in China. Considering a number of reforms carried out in the economic and financial field in the past two decades and the possibility of structural changes in the monetary transmission mechanism, the methodology proposed by Qu and Perron is employed to allow for endogenous structural changes in the model.

By conducting a comparative analysis, conclusions can be drawn from this paper that bank lending is always the dominating channel for monetary policy to influence economy in China and the roles of the interest rate channel and the exchange rate channel have been improved in recent years. However, the role of the asset price channel in monetary policy transmission has weakened since late 2001.

This paper combines the quasi‐maximum likelihood procedure proposed by Qu and Perron in 2007 with a benchmark VAR model, thus providing a new approach to study monetary transmission mechanism and the conclusions can be more sensible.

In seminal work, Den Haan et al. (2007, 2010, 2011) show business loans respond in the opposite direction of what may be intended by monetary policy action in the United States and Canada. Based on various approaches, identification schemes, and samples, we document evidence this loan puzzle is not exclusive to developed economies but is also pervasive in emerging markets. We find business loans generally decline following expansionary monetary policy shocks. A preponderance of statistical and structural evidence indicates important transmissions of this puzzle from the United States to emerging markets.

The development of models for variables sampled at different frequencies has attracted substantial interest in the recent literature. In this article, we discuss classical and Bayesian methods of estimating mixed-frequency VARs, and use them for forecasting and structural analysis. We also compare mixed-frequency VARs with other approaches to handling mixed-frequency data.

The purpose of this paper is to provide a method for computing the spillover index first proposed by Diebold and Yilmaz (2009), with empirical application on Asian stock markets.

It is based on a VAR-structural-GARCH model.

The results clearly show that the main driver of fluctuations in Asian financial markets is the USA, with China having little connection with other markets. Further, evidence of financial contagion is found during both the 1997 Asian financial crisis and the 2008 global financial crisis.

The method has two advantages: it is both uniquely determined and dynamic.

The purpose of this paper is to examine the monetary policy transmission mechanism for the Fiji Islands using a structural vector autoregressive (SVAR) model for the period 1975 to 2005.

The SVAR model investigates how a monetary policy shock – defined as a temporary and exogenous rise in the short‐term interest rate – affects real and nominal macro variables; namely real output, prices, exchange rates, and money supply.

The results suggest that a monetary policy shock statistically significantly reduces output initially, but then output is able to recover to its pre‐shock level. A monetary policy shock generates inflationary pressure, leads to an appreciation of the Fijian currency and reduces the demand for money. The paper also analysed the impact of a nominal effective exchange rate (NEER) shock (an appreciation) on real output and found that it leads to a statistically significant negative effect on real output.

The findings of this study should be of direct relevance to the research and policy work undertaken at the Reserve Bank of Fiji.

For a small economy, such as Fiji, where monetary policy is key to sustainable macroeconomic management, this is the first paper that undertakes a dynamic analysis of monetary policy transmission. The paper uses time series data over three decades and builds a structural VAR model, rooted in theory. This paper will be of direct relevance to the Reserve Bank of Fiji. The approach and model proposed will also be useful for applied monetary policy researchers in other developing countries where inflation rate targeting is a key element of the monetary policy setting.

The purpose of this paper is to examine the effects of inflation uncertainty on real economic activity using data from four industrialised countries.

The paper uses the econometric framework developed by Elder in the context of a multivariate framework in which a structural vector autoregression (VAR) is modified to accommodate multivariate GARCH‐in‐mean (MGARCH‐M) errors. It calculates the impulse response functions for the multivariate GARCH(1,1)‐in‐mean VAR in order to see whether the specification captures the fundamental dynamics.

The results show that inflation uncertainty has differential effects on output growth across these countries.

In the context of multivariate GARCH(1,1)‐in‐mean VAR, this paper uses a non‐recursive identification scheme and separate identification for the large and small economies.

This paper aims to examine the interrelationships of monetary policy's structural shocks, the real exchange rate and stock prices.

According to quarterly data, variables such as gross domestic product, consumer price index, the real exchange rate, stock price and monetary policy indices in the structural vector autoregressions model are estimated. These variables' volatility is attributed to other variables’ structural shocks separately, and analysis of variance tables for all variables is presented.

The results show that structural shock on the exchange rate does not affect the stock price, but the monetary policy's structural shock positively impacts the real exchange rate. Moreover, the real exchange rate and monetary policy's structural shocks have a negative impact on the stock price index. However, no significant effect is found pertain to the real exchange rate structural shock, statistically.

To the best of the authors’ knowledge, the current study model is relatively novel in developing countries, and the study sought strength to develop knowledge on the subject of the study.

The purpose of this study is to investigate the interactive relationships between oil price shocks and the Nigeria stock market.

The paper applied the multivariate vector auto‐regression that employed the generalized impulse response function and the forecast variance decomposition error.

Empirical evidence reveals that stock market returns exhibit insignificant positive response to oil price shocks but reverts to negative effects after a period of time depending on the nature of the oil price shocks. The results are similar even with the inclusion of other variables. Also, the asymmetric effect of oil price shocks on the Nigerian stock returns indices is not supported by statistical evidences.

This is the first study to examine the dynamic linkages between stock market behaviour and oil price shocks in Nigeria.