9 research outputs found
Forecasting US inflation using Markov dimension switching
This study considers Bayesian variable selection in the Phillips curve context by using the Bernoulli approach of Korobilis (Journal of Applied Econometrics, 2013, 28(2), 204â230). The Bernoulli model, however, is unable to account for model change over time, which is important if the set of relevant predictors changes. To tackle this problem, this paper extends the Bernoulli model by introducing a novel modeling approach called Markov dimension switching (MDS). MDS allows the set of predictors to change over time. It turns out that only a small set of predictors is relevant and that the relevant predictors exhibit a sizable degree of time variation for which the Bernoulli approach is not able to account, stressing the importance and benefit of the MDS approach. In addition, this paper provides empirical evidence that allowing for changing predictors over time is crucial for forecasting inflation
Data-based priors for vector error correction models
We propose two data-based priors for vector error correction models. Both
priors lead to highly automatic approaches which require only minimal user
input. An empirical investigation reveals that Bayesian vector error correction
(BVEC) models equipped with our proposed priors turn out to scale well to
higher dimensions and to forecast well. In addition, we find that exploiting
information in the level variables has the potential for improving long-term
forecasts. Thus, working with VARs in first differences may ignore valuable
information. A simulation study reveals that it is beneficial, in terms of estimation
accuracy, to use BVEC in the presence of cointegration. But if there is
no cointegration, the proposed priors provide a sufficient amount of shrinkage
so that the BVEC model has a similar estimation accuracy compared to the
Bayesian vector autoregressive (BVAR) estimated in first differences
A global-local prior for time-varying parameter VARs and monetary policy
Time-varying parameter VARs have become the workhorse models in empirical
macroeconomics. These models are usually equipped with tightly
parametrized prior distributions which favor a small and gradual change in
parameters. Do such prior distributions suppress some degree of time variation
in the VAR coefficients? We address this question by proposing a
exible global-local prior. It turns out that the conventional prior may suppress economically
relevant patterns of time variation. Using the global-local prior,
we observe that parameter change can be abrupt rather than smooth. We
find that, during the chairmanship of Paul Volcker, the Fed has been fighting
inflation pressures by raising the interest rate in response to a negative supply
shock. However, during the chairmanship of Alan Greenspan, this policy
came to an end. In contrast, using the conventional prior, we do not detect
this pattern
On the time-varying effects of economic policy uncertainty on the US economy
We study the impact of Economic Policy Uncertainty (EPU) on the US Economy by using a VAR with timeâvarying coefficients. The coefficients are allowed to evolve gradually over time which allows us to discover structural changes without imposing them a priori. We find three different regimes, which match the three major periods of the US economy, namely the Great Inflation, the Great Moderation and the Great Recession. The initial impact on real GDP ranges between â0.2% for the Great Inflation and Great Recession and â0.15% for the Great Moderation. In addition, the adverse effects of EPU are more persistent during the Great Recession providing an explanation for the slow recovery. This regime dependence is unique for EPU as the macroeconomic consequences of Financial Uncertainty turn out to be rather time invariant
Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions
Modeling and predicting extreme movements in GDP is notoriously difficult and
the selection of appropriate covariates and/or possible forms of nonlinearities
are key in obtaining precise forecasts. In this paper, our focus is on using
large datasets in quantile regression models to forecast the conditional
distribution of US GDP growth. To capture possible non-linearities, we include
several nonlinear specifications. The resulting models will be huge dimensional
and we thus rely on a set of shrinkage priors. Since Markov Chain Monte Carlo
estimation becomes slow in these dimensions, we rely on fast variational Bayes
approximations to the posterior distribution of the coefficients and the latent
states. We find that our proposed set of models produces precise forecasts.
These gains are especially pronounced in the tails. Using Gaussian processes to
approximate the nonlinear component of the model further improves the good
performance, in particular in the right tail
Estimating Fiscal Multipliers by Combining Statistical Identification with Potentially Endogenous Proxies
Different proxy variables used in fiscal policy SVARs lead to contradicting
conclusions regarding the size of fiscal multipliers. In this paper, we show
that the conflicting results are due to violations of the exogeneity
assumptions, i.e. the commonly used proxies are endogenously related to the
structural shocks. We propose a novel approach to include proxy variables into
a Bayesian non-Gaussian SVAR, tailored to accommodate potentially endogenous
proxy variables. Using our model, we show that increasing government spending
is a more effective tool to stimulate the economy than reducing taxes. We
construct new exogenous proxies that can be used in the traditional proxy VAR
approach resulting in similar estimates compared to our proposed hybrid SVAR
model.Comment: 10 figure