Flexible Mixture Priors for Large Time-varying Parameter Models

Time-varying parameter (TVP) models often assume that the TVPs evolve according to a random walk. This assumption, however, might be questionable since it implies that coefficients change smoothly and in an unbounded manner. In this paper, we relax this assumption by proposing a flexible law of motion for the TVPs in large-scale vector autoregressions (VARs). Instead of imposing a restrictive random walk evolution of the latent states, we carefully design hierarchical mixture priors on the coefficients in the state equation. These priors effectively allow for discriminating between periods where coefficients evolve according to a random walk and times where the TVPs are better characterized by a stationary stochastic process. Moreover, this approach is capable of introducing dynamic sparsity by pushing small parameter changes towards zero if necessary. The merits of the model are illustrated by means of two applications. Using synthetic data we show that our approach yields precise parameter estimates. When applied to US data, the model reveals interesting patterns of low-frequency dynamics in coefficients and forecasts well relative to a wide range of competing models.Comment: JEL: C11, C30, C53, E44, E47 KEYWORDS: Time-varying parameter vector autoregressions, hierarchical modeling, clustering, forecastin

On the Dynamic Effects of Government Stimulus Measures in a Changing Economy.

No abstract available

Model instability in predictive exchange rate regressions

In this paper we aim to improve existing empirical exchange rate models by accounting for uncertainty with respect to the underlying structural representation. Within a flexible Bayesian non-linear time series framework, our modeling approach assumes that different regimes are characterized by commonly used structural exchange rate models, with their evolution being driven by a Markov process. We assume a time-varying transition probability matrix with transition probabilities depending on a measure of the monetary policy stance of the central bank at the home and foreign country. We apply this model to a set of eight exchange rates against the US dollar. In a forecasting exercise, we show that model evidence varies over time and a model approach that takes this empirical evidence seriously yields improvements in accuracy of density forecasts for most currency pairs considered.Series: Department of Economics Working Paper Serie

Implications of macroeconomic volatility in the Euro area

In this paper we estimate a Bayesian vector autoregressive model with factor stochastic volatility in the error term to assess the effects of an uncertainty shock in the Euro area. This allows us to treat macroeconomic uncertainty as a latent quantity during estimation. Only a limited number of contributions to the literature estimate uncertainty and its macroeconomic consequences jointly, and most are based on single country models. We analyze the special case of a shock restricted to the Euro area, where member states are highly related by construction. We find significant results of a decrease in real activity for all countries over a period of roughly a year following an uncertainty shock. Moreover, equity prices, short-term interest rates and exports tend to decline, while unemployment levels increase. Dynamic responses across countries differ slightly in magnitude and duration, with Ireland, Slovakia and Greece exhibiting different reactions for some macroeconomic fundamentals.Comment: Keywords: Bayesian vector autoregressive models, factor stochastic volatility, uncertainty shocks; JEL: C30, F41, E3

Spending rises are more effective in expanding the economy by as much as 20 percent compared to tax cuts

Both fiscal and monetary authorities have engaged in ‘unconventional’ policies over the past few years in order to bring the Great Recession under control. But, have these actions been intentionally coordinated, and what has been their economic impact? More fundamentally, has there ever been a systematic or regular coordination between fiscal and monetary policy in the US? Eddie Gerba and Klemens Hauzenberger find sufficient evidence for an implicit coordination of policies, and demonstrate how it has changed over the past three decades. They find that spending and tax stimuli have notably been more efficient in expanding the economy during the Volcker chairmanship (1979-84) and the Great Recession (2008-12). Taking into account that the current interest rate is constrained by the zero lower bound, fiscal authorities have an unprecedented window of opportunity to pursue activist policies aimed at expanding output, if and only if they carefully manage the expectations of private agents

Bayesian state-space modeling for analyzing heterogeneous network effects of US monetary policy

Understanding disaggregate channels in the transmission of monetary policy is of crucial importance for effectively implementing policy measures. We extend the empirical econometric literature on the role of production networks in the propagation of shocks along two dimensions. First, we allow for industry-specific responses that vary over time, reflecting non-linearities and cross-sectional heterogeneities in direct transmission channels. Second, we allow for time-varying network structures and dependence. This feature captures both variation in the structure of the production network, but also differences in cross-industry demand elasticities. We find that impacts vary substantially over time and the cross-section. Higher-order effects appear to be particularly important in periods of economic and financial uncertainty, often coinciding with tight credit market conditions and financial stress. Differentials in industry-specific responses can be explained by how close the respective industries are to end-consumers.Comment: JEL: C11, C23, C32, C58, E52; Keywords: production networks, monetary policy shocks, high-frequency identification, spatio-temporal modelin

Drone Detection using Audio Analysis

Drones used for illegal purposes is a growing problem and a way to detect these is needed. This thesis has evaluated the possibility of using sound analysis as the detection mechanism. A solution using linear predictive coding, the slope of the frequency spectrum and the zero crossing rate was evaluated. The results showed that a solution using linear predictive coding and the slope of the frequency spectrum give a good result for the distance it is calibrated for. The zero crossing rate on the other hand does not improve the result and was not part of the final solution. The amount of false positives increases when calibrating for longer distances, and a compromise between detecting drones at long distances and the number of false positives need to be made in the implemented solution. It was concluded that drone detection using audio analysis is possible, and that the implemented solution, with linear predictive coding and slope of the frequency spectrum, could with further improvements become a useable product

Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods

Time-varying parameter (TVP) regression models can involve a huge number of coefficients. Careful prior elicitation is required to yield sensible posterior and predictive inferences. In addition, the computational demands of Markov Chain Monte Carlo (MCMC) methods mean their use is limited to the case where the number of predictors is not too large. In light of these two concerns, this paper proposes a new dynamic shrinkage prior which reflects the empirical regularity that TVPs are typically sparse (i.e. time variation may occur only episodically and only for some of the coefficients). A scalable MCMC algorithm is developed which is capable of handling very high dimensional TVP regressions or TVP Vector Autoregressions. In an exercise using artificial data we demonstrate the accuracy and computational efficiency of our methods. In an application involving the term structure of interest rates in the eurozone, we find our dynamic shrinkage prior to effectively pick out small amounts of parameter change and our methods to forecast well.Comment: Keywords: Time-varying parameter regression, dynamic shrinkage prior, global-local shrinkage prior, Bayesian variable selection, scalable Markov Chain Monte Carlo JEL Codes: C11, C30, E3, D3