40 datasets found

This paper proposes Bayesian forecasting in a vector autoregression using a democratic prior. This prior is chosen to match the predictions of survey respondents. In particular,...

Financial time series are often non-negative-valued (volumes, trades, durations, realized volatility, daily range) and exhibit clustering. When joint dynamics is of interest,...

We develop a structural model of the global market for crude oil that for the first time explicitly allows for shocks to the speculative demand for oil as well as shocks to flow...

Sign restrictions have become increasingly popular for identifying shocks in structural vector autoregressive (SVAR) models. So far there are no techniques for validating the...

We use Bayesian time-varying parameter structural vector autoregressions with stochastic volatility to investigate changes in reduced-form and structural correlations between...

The mismatch between the timescale of DSGE (dynamic stochastic general equilibrium) models and the data used in their estimation translates into identification problems,...

The likelihood of the parameters in structural macroeconomic models typically has non-identification regions over which it is constant. When sufficiently diffuse priors are...

In this paper we discuss how the point and density forecasting performance of Bayesian vector autoregressions (BVARs) is affected by a number of specification choices. We adopt...

This work investigates how the state of credit markets affects the impact of fiscal policies. We estimate a threshold vector autoregression (TVAR) model on US quarterly data for...

This paper proposes a Bayesian, graph-based approach to identification in vector autoregressive (VAR) models. In our Bayesian graphical VAR (BGVAR) model, the contemporaneous...

We use counterfactual simulations based on an estimated dynamic stochastic general equilibrium model to demonstrate why China was affected less than other major countries during...

We estimate the low-frequency relationship between fiscal deficits and inflation and pay special attention to its potential time variation by estimating a time-varying vector...

We propose a Bayesian shrinkage approach for vector autoregressions (VARs) that uses short-term survey forecasts as an additional source of information about model parameters....

The proposed panel Markov-switching VAR model accommodates changes in low and high data frequencies and incorporates endogenous time-varying transition matrices of...

We consider Bayesian analysis of the noncausal vector autoregressive model that is capable of capturing nonlinearities and effects of missing variables. Specifically, we devise...

Many forecasts are conditional in nature. For example, a number of central banks routinely report forecasts conditional on particular paths of policy instruments. Even though...

In this paper, we derive restrictions for Granger noncausality in MS-VAR models and show under what conditions a variable does not affect the forecast of the hidden Markov...

Small vector autoregressions are commonly used in macroeconomics for forecasting and evaluating shock transmission. This requires VAR parameters to be stable over the evaluation...

Starting from the dynamic factor model for nonstationary data we derive the factor-augmented error correction model (FECM) and its moving-average representation. The latter is...

The PPP puzzle refers to the wide swings of nominal exchange rates around their long-run equilibrium values whereas the excess return puzzle represents the persistent deviation...