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Identification of shocks of interest is a central problem in structural vector autoregressive (SVAR) modeling. Identification is often achieved by imposing restrictions on the impact or long-run effects of shocks or by considering sign restrictions for the impulse responses. In a number of articles changes in the volatility of the shocks have also been used for identification. The present study focuses on the latter device. Some possible setups for identification via heteroskedasticity are reviewed and their potential and limitations are discussed. Two detailed examples are considered to illustrate the approach.

The authors propose a new frequentist approach to sign restrictions in structural vector autoregressive models. By making efficient use of non-Gaussianity in the data, point identification is achieved which facilitates standard asymptotic inference and, hence, the assessment of theoretically implied signs and labelling of the statistically identified structural shocks. The authors illustrate the benefits of their approach in an empirical application to the US labour market.

For over three decades, vector autoregressions have played a central role in empirical macroeconomics. These models are general, can capture sophisticated dynamic behavior, and can be extended to include features such as structural instability, time-varying parameters, dynamic factors, threshold-crossing behavior, and discrete outcomes. Building upon growing evidence that the assumption of linearity may be undesirable in modeling certain macroeconomic relationships, this article seeks to add to recent advances in VAR modeling by proposing a nonparametric dynamic model for multivariate time series. In this model, the problems of modeling and estimation are approached from a hierarchical Bayesian perspective. The article considers the issues of identification, estimation, and model comparison, enabling nonparametric VAR (or NPVAR) models to be fit efficiently by Markov chain Monte Carlo (MCMC) algorithms and compared to parametric and semiparametric alternatives by marginal likelihoods and Bayes factors. Among other benefits, the methodology allows for a more careful study of structural instability while guarding against the possibility of unaccounted nonlinearity in otherwise stable economic relationships. Extensions of the proposed nonparametric model to settings with heteroskedasticity and other important modeling features are also considered. The techniques are employed to study the postwar U.S. economy, confirming the presence of distinct volatility regimes and supporting the contention that certain nonlinear relationships in the data can remain undetected by standard models.

Advocates of quantitative easing (QE) policies have emphasized some evidence that structural models do not predict long-term asset yields as well as naive forecasts, implying that predictions of price reversals cannot be profitable and that QE effects are not transitory. The purpose of this study is to reconsider the out-of-sample forecasting performance of structural time series processes relative to that of a random walk with or without drift.

This study uses bivariate vector autoregression and Markov switching representations to generate out-of-sample forecasts of ten-year sovereign bond yields, when the information set is augmented by including the growth rate of the monetary base, and the estimation relies on monthly data from countries that have pursued unconventional policies over the last decade.

The results show that naive forecasts are not better than those of structural time series models, based on root mean squared errors, while the Markov model provides additional information on price reversals, through probabilistic inferences regarding policy regime switches, which can induce agents to counteract QE interventions and reduce their effectiveness.

The novel features of this work are the use of a large information set including the instrument of unconventional monetary policy, the use of a structural model (Markov process) that can really inform about potential asset price reversals and the use of a large sample over which QE policies have been pursued.

Oil market VAR models have become the standard tool for understanding the evolution of the real price of oil and its impact on the macro economy. As this literature has expanded at a rapid pace, it has become increasingly difficult for mainstream economists to understand the differences between alternative oil market models, let alone the basis for the sometimes divergent conclusions reached in the literature. The purpose of this survey is to provide a guide to this literature. Our focus is on the econometric foundations of the analysis of oil market models with special attention to the identifying assumptions and methods of inference.

The purpose of this study is to examine the effects of monetary policy on equity returns by applying an alternative econometric approach. Campbell and Ammer (1993) decomposed unexpected equity excess returns into three news components: risk premium news, real interest rate news and cash-flow news. The literature has determined the monetary policy (MP) effects on these news components. The authors propose an alternative MP shock identification approach to analyze the MP effects on the above-mentioned news components under a structural vector autoregression (SVAR) setup. Under this approach, one can apply an MP indicator in the SVAR, which helps forecast equity excess returns along with its external instruments for identification. Further, this study uses the various recently proposed measures of exogenous MP shocks and Fed information shocks as external instruments, and shows the different patterns of the news components' responses depending on the information in the applied instruments.

This chapter proposes a vector autoregressive VAR model with structural shocks (SVAR) that are identified using sign restrictions, and whose distribution is subject to time varying skewness. The authors also present an efficient Bayesian algorithm to estimate the model. The model allows tracking joint asymmetric risks to macroeconomic variables included in the SVAR, and provides a structural narrative to the evolution of those risks. When faced with euro area data, our estimation suggests that there has been a significant variation in the skewness of demand, supply and monetary policy shocks. Such variation can explain a significant proportion of the joint dynamics of real GDP growth and inflation, and also generates important asymmetric tail risks in those macroeconomic variables. Finally, compared to the literature on growth- and inflation-at-risk, the authors find that financial stress indicators are not enough to explain all the macroeconomic tail risks.

As technical barriers gradually become the important tools of trade protection, it is important to understand whether intensified enforcement of border controls is adopted as a hidden tool of trade protectionism and differs across periods and industries.

This article applies a panel structural vector autoregression (PSVAR) model to investigate the potential role of trade protectionism motives in Food and Drug Administration (FDA) import refusals on China's agricultural exports, utilizing newly constructed monthly data at the industry level.

The results show that import refusal is mainly driven by the inspection history, highlighting the importance of the intrinsic product quality and maintaining an excellent inspection history in border inspection. The novel finding is that US employment contractions would also lead to a small increase in FDA import refusals, especially those taking place within ten months and made without sampling tests. Such an association is driven by industry-specific employment shocks and becomes stronger after the financial crisis. It is also more evident in industries where the US lacks competitiveness against China, being manufactured without mandatory safety regulations, and with negative skewness of employment growth.

This research is one of the preliminary attempts to understand whether the de facto border controls are worked as a hidden tool of protectionism to agricultural products, and what the specific trajectory and duration of the impacts at the monthly level. This study provides empirical evidence showing the role of protectionism motives in FDA import refusals and is heterogeneous across industries, which generate new insights and policy implications to predict and cope with additional barriers on agricultural trade.

This study examines the dynamic responses of five different daily energy prices to a pulse shock affecting the daily price of oil.

Daily data for energy prices from the Federal Reserve Economic Data (FRED) database for January 7, 1997, through February 8, 2021, are analyzed. A bivariate structural vector error correction model and generalized autoregressive conditionally heteroscedastic model are combined and extended by adding the volatility of the growth rate of daily oil prices as an explanatory variable for the growth rates of energy prices. This model is estimated and used to generate impulse responses for energy prices.

The empirical results show that the levels of the daily energy prices examined have unit roots, are integrated of order one, are cointegrated, and generally revert slowly to their long-term equilibrium relationships with the price of oil. The growth rates for the daily energy prices have autoregressive conditional heteroscedasticity, generally are positively related to the volatility of daily oil prices, respond quickly to a pulse shock to daily oil prices, and have cumulative responses that last at least one month.

This paper allows for simultaneous estimation of extended bivariate structural vector error correction and generalized autoregressive conditionally heteroscedastic models that include the volatility of oil as an explanatory variable and uses these models to generated cumulative impulse responses for the growth rates of daily energy prices to oil price shocks.

All over the world, the role of central banks is being redefined following the outbreak of the global financial crisis and subsequent breakdown of the “great moderation” consensus. Consequently, most advanced economies adopted non-conventional approaches of monetary policy which resulted in spill-overs to emerging markets and developing countries with implications on their financial system and monetary policy transmission. This, coupled with, internal developments in the financial systems of developing countries necessitated modifications of not only monetary policy frameworks but also responsibilities of most central banks. This chapter acknowledges possible evolutions of the financial structure variables in developing countries and uses data from Kenya to analyze the dynamic linkages between financial sector variables and monetary policy transmission in the light of the financial crisis. The study used structural vector autoregression to examine the relationship between financial structure variables and monetary policy as well as assess the relative importance of various monetary transmission channels in Kenya. The results show that the changing financial structure represented by credit to the private sector and stock market indicators in Kenya only slightly altered relative importance of monetary policy transmission. The insignificance of credit to the private sector suggests that the importance attached to the bank lending channel in previous studies is waning while the marginal significance of the stock market indicator signals the potential for asset price channel. The results also indicate that the interest rate and exchange rate channels are relatively more important in Kenya while the asset prices is only marginally significant and bank lending channel is the weakest in the intermediate stage of monetary policy transmission. However, transmission of monetary policy to the ultimate objectives is somewhat slow and weak to inflation and almost absent to output. The result implies a limited role of monetary policy on growth and questions the wisdom of pursuing multiple objectives.