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), 204–230). 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 time‐varying 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 −0.2% for the Great Inflation and Great Recession and −0.15% 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

Forty years of carabid beetle research in Europe - from taxonomy, biology, ecology and population studies to bioindication, habitat assessment and conservation

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