Estimation of Structural Impulse Responses: Short-Run Versus Long-Run Identifying Restrictions

There is evidence that estimates of long-run impulse responses of structural vector autoregressive (VAR) models based on long-run identifying restrictions may not be very accurate. This finding suggests that using short-run identifying restrictions may be preferable. We compare structural VAR impulse response estimates based on long-run and short-run identifying restrictions and find that long-run identifying restrictions can result in much more precise estimates for the structural impulse responses than restrictions on the impact effects of the shocks

Structural Vector Autoregressions: Checking Identifying Long-Run Restrictions via Heteroskedasticity

Long-run restrictions have been used extensively for identifying structural shocks in vector autoregressive (VAR) analysis. Such restrictions are typically just-identifying but can be checked by utilizing changes in volatility. This paper reviews and contrasts the volatility models that have been used for this purpose. Three main approaches have been used, exogenously generated changes in the unconditional residual covariance matrix, changing volatility modelled by a Markov switching mechanism and multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models. Using changes in volatility for checking long-run identifying restrictions in structural VAR analysis is illustrated by reconsidering models for identifying fundamental components of stock prices

Structural Vector Autoregressions with Smooth Transition in Variances: The Interaction between U.S. Monetary Policy and the Stock Market

In structural vector autoregressive analysis identifying the shocks of interest via heteroskedasticity has become a standard tool. Unfortunately, the approaches currently used for modelling heteroskedasticity all have drawbacks. For instance, assuming known dates for variance changes is often unrealistic while more exible models based on GARCH or Markov switching residuals are difficult to handle from a statistical and computational point of view. Therefore we propose a modelbased on a smooth change in variance that is exible as well as relatively easy to estimate. The model is applied to a five-dimensional system of U.S. variables to explore the interaction between monetary policy and the stock market. It is found that previously used conventional identification schemes in this context are rejected by the data if heteroskedasticity is allowed for. Shocks identified via heteroskedasticity have a different economic interpretation than the shocks identified using conventional methods

Financial Crises and Information Transfer - An Empirical Analysis of the Lead-Lag Relationship between Equity and CDS iTraxx Indices

This study examines the lead-lag-relationship between European equity and CDS markets in the context of the financial crisis. Previous research identified the stock market to lead the CDS market in an ordinary economic environment. Against the background of our study this lead-lag-relationship strengthens when moving from the non-crisis- to the crisisscenario on a daily as well as on a weekly basis. Hence, we conclude that information transfer from stock to CDS markets widens during the financial crisis. In addition and in contrast to the literature we find an extraordinary day-of-the-week-effect on weekly returns as an anomaly for information processing

Robust Implementation of a Parsimonious Dynamic Factor Model to Nowcast GDP

Quarterly GDP figures usually are published with a delay of some weeks. A common way to generate GDP series of higher frequency, i.e. to nowcast GDP, is to use available indicators to calculate a single index by means of a common factor derived from a dynamic factor model (DFM). This paper deals with the implementation stage of this practice. We propose a two-tiered mechanism consisting in the identification of variables highly correlated with GDP as “core” indicators and a check of robustness of these variables in the sense of extreme bounds analysis. Accordingly selected indicators are used in an approximate DFM framework to exemplarily nowcast Spanish GDP growth. We show that our implementation produces more accurate nowcasts than both a benchmark stochastic process and the implementation based on the total set of core indicators

Inference for Impulse Responses

Poor identification of individual impulse response coefficients does not necessarily mean that an impulse response is imprecisely estimated. This paper introduces a three-pronged approach on how to communicate uncertainty of impulse response estimates: (1) withWald tests of joint significance; (2) with conditional t-tests of individual marginal coefficient significance; and (3) with fan charts based on the percentiles of the joint Wald statistics. The paper also shows how to anchor the impulse response analysis with a priori economic restrictions that can be formally tested and used to tighten structural identification. These methods are universal and do not depend on how the impulse responses are estimated. An empirical application illustrates the techniques in practice