Michael Pfarrhofer

Michael Pfarrhofer
University of Salzburg · Department of Economics

PhD
Bayesian econometrics, time series, predictive inference, monetary economics, business cycles

About

40
Publications
4,988
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91
Citations
Introduction
My research interests include econometric methods for dynamic models, mainly in the context of macroeconomics and finance. In particular, I am interested in monetary economics, business cycles and forecasting. The focus of my work is on econometrics, machine learning techniques and Bayesian data analysis. mpfarrhofer.com
Education
October 2018 - October 2019
Wirtschaftsuniversität Wien
Field of study
  • Econometrics
October 2016 - June 2018
Wirtschaftsuniversität Wien
Field of study
  • Economics

Publications

Publications (40)
Article
Full-text available
This paper develops Bayesian econometric methods for posterior inference in non-parametric mixed frequency VARs using additive regression trees. We argue that regression tree models are ideally suited for macroeconomic nowcasting in the face of extreme observations, for instance those produced by the COVID-19 pandemic of 2020. This is due to their...
Preprint
Full-text available
We develop a Bayesian non-parametric quantile panel regression model. Within each quantile, the response function is a convex combination of a linear model and a non-linear function, which we approximate using Bayesian Additive Regression Trees (BART). Cross-sectional information at the pth quantile is captured through a conditionally heteroscedast...
Article
Full-text available
In this paper, we investigate the effectiveness of conventional and unconventional monetary policy measures by the European Central Bank (ECB) conditional on the prevailing level of uncertainty. To obtain exogenous variation in central bank policy, we rely on high-frequency surprises in financial market data for the euro area (EA) around policy ann...
Preprint
Full-text available
We investigate the consequences of legal rulings on the conduct of monetary policy. Several unconventional monetary policy measures of the European Central Bank have come under scrutiny before national courts and the European Court of Justice. These lawsuits have the potential to severely impact the scope and flexibility of central bank policies, a...
Article
Full-text available
This paper proposes methods for Bayesian inference in time-varying parameter (TVP) quantile regressions (QRs) featuring conditional heteroskedasticity. I use data augmentation schemes to render the model conditionally Gaussian and develop an efficient sampling algorithm. Regularization of the high-dimensional parameter space is achieved via dynamic...
Preprint
Full-text available
In this paper, we forecast euro area inflation and its main components using an econometric model which exploits a massive number of time series on survey expectations for the European Commission's Business and Consumer Survey. To make estimation of such a huge model tractable, we use recent advances in computational statistics to carry out posteri...
Article
Panel Vector Autoregressions (PVARs) are a popular tool for analyzing multi‐country datasets. However, the number of estimated parameters can be enormous, leading to computational and statistical issues. In this paper, we develop fast Bayesian methods for estimating PVARs using integrated rotated Gaussian approximations. We exploit the fact that do...
Article
This study investigates the time-varying effects of international uncertainty shocks. I use a global vector autoregressive model with drifting coefficients and factor stochastic volatility in the errors to model the G7 economies jointly. The measure of uncertainty is constructed by estimating a time-varying scalar driving the innovation variances o...
Preprint
Full-text available
Panel Vector Autoregressions (PVARs) are a popular tool for analyzing multi-country datasets. However, the number of estimated parameters can be enormous, leading to computational and statistical issues. In this paper, we develop fast Bayesian methods for estimating PVARs using integrated rotated Gaussian approximations. We exploit the fact that do...
Preprint
Full-text available
This paper proposes methods for Bayesian inference in time-varying parameter (TVP) quantile regression (QR) models featuring conditional heteroskedasticity. I use data augmentation schemes to render the model conditionally Gaussian and develop an efficient Gibbs sampling algorithm. Regularization of the high-dimensional parameter space is achieved...
Article
Full-text available
We extend the econometric literature on the role of production networks in the propagation of monetary policy shocks along two dimensions. First, we allow for time‐varying industry‐specific responses, reflecting non‐linearities and heterogeneity in direct transmission channels. Second, we allow for time‐varying network structures and dependence. Th...
Article
Full-text available
We develop novel multivariate time series models using Bayesian additive regression trees that posit nonlinear relationships among macroeconomic variables, their lags, and possibly the lags of the errors. The variance of the errors can be stable, driven by stochastic volatility (SV), or follow a novel nonparametric specification. Estimation is carr...
Preprint
Full-text available
Time-varying parameter (TVP) regressions commonly assume that time-variation in the coefficients is determined by a simple stochastic process such as a random walk. While such models are capable of capturing a wide range of dynamic patterns, the true nature of time variation might stem from other sources, or arise from different laws of motion. In...
Article
Full-text available
The COVID‐19 recession that started in March 2020 led to an unprecedented decline in economic activity across the globe. To fight this recession, policy makers in central banks engaged in expansionary monetary policy. This paper asks whether the measures adopted by the US Federal Reserve (Fed) have been effective in boosting real activity and calmi...
Article
Full-text available
Successful forecasting models strike a balance between parsimony and flexibility. This is often achieved by employing suitable shrinkage priors that penalize model complexity but also reward model fit. In this note, we modify the stochastic volatility in mean (SVM) model proposed in Chan (2017) by introducing state‐of‐the‐art shrinkage techniques t...
Preprint
Full-text available
In this paper, we investigate the effectiveness of conventional and unconventional monetary policy measures by the European Central Bank (ECB) conditional on the prevailing level of uncertainty. To obtain exogenous variation in central bank policy, we rely on high-frequency surprises in financial market data for the euro area (EA) around policy ann...
Preprint
Full-text available
In this paper we propose a time-varying parameter (TVP) vector error correction model (VECM) with heteroscedastic disturbances. We combine a set of econometric techniques for dynamic model specification in an automatic fashion. We employ continuous global-local shrinkage priors for pushing the parameter space towards sparsity. In a second step, we...
Preprint
Full-text available
This paper develops Bayesian econometric methods for posterior and predictive inference in a non-parametric mixed frequency VAR using additive regression trees. We argue that regression tree models are ideally suited for macroeconomic nowcasting in the face of the extreme observations produced by the pandemic due to their flexibility and ability to...
Preprint
Full-text available
The COVID-19 recession that started in March 2020 led to an unprecedented decline in economic activity across the globe. To fight this recession, policy makers in central banks engaged in expansionary monetary policy. This paper asks whether the measures adopted by the US Federal Reserve (Fed) have been effective in boosting real activity and calmi...
Preprint
Full-text available
Successful forecasting models strike a balance between parsimony and flexibility. This is often achieved by employing suitable shrinkage priors that penalize model complexity but also reward model fit. In this note, we modify the stochastic volatility in mean (SVM) model proposed in Chan (2017) by introducing state-of-the-art shrinkage techniques t...
Preprint
Full-text available
This paper investigates the sensitivity of forecast performance measures to taking a real time versus pseudo out-of-sample perspective. We use monthly vintages for the United States (US) and the Euro Area (EA) and estimate a set of vector autoregressive (VAR) models of different sizes with constant and time-varying parameters (TVPs) and stochastic...
Article
This paper proposes a hierarchical modeling approach to perform stochastic model specification in Markov switching vector error correction models. We assume that a common distribution gives rise to the regime-specific regression coefficients. The mean as well as the variances of this distribution are treated as fully stochastic and suitable shrinka...
Preprint
Full-text available
Researchers increasingly wish to estimate time-varying parameter (TVP) regressions which involve a large number of explanatory variables. Including prior information to mitigate over-parameterization concerns has led to many using Bayesian methods. However, Bayesian Markov Chain Monte Carlo (MCMC) methods can be very computationally demanding. In t...
Article
Full-text available
This paper develops a dynamic factor model that uses euro area (EA) country‐specific information on output and inflation to estimate an area‐wide measure of the output gap. Our model assumes that output and inflation can be decomposed into country‐specific stochastic trends and a common cyclical component. Comovement in the trends is introduced by...
Preprint
Full-text available
This paper develops a dynamic factor model that uses euro area (EA) country-specific information on output and inflation to estimate an area-wide measure of the output gap. Our model assumes that output and inflation can be decomposed into country-specific stochastic trends and a common cyclical component. Comovement in the trends is introduced by...
Chapter
This chapter provides a thorough introduction to panel, global, and factor augmented vector autoregressive models. These models are typically used to capture interactions across units (i.e., countries) and variable types. Since including a large number of countries and/or variables increases the dimension of the models, all three approaches aim to...
Preprint
Full-text available
We explore the international transmission of monetary policy and central bank information shocks by the Federal Reserve and the European Central Bank. Identification of these shocks is achieved by using a combination of high-frequency market surprises around announcement dates of policy decisions and sign restrictions. We propose a high-dimensional...
Preprint
Full-text available
Understanding disaggregate channels in the transmission of monetary policy to the real and financial sectors 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 set forth a Bayesian spatia...
Preprint
Full-text available
This paper investigates the time-varying impacts of international macroeconomic uncertainty shocks. We use a global vector autoregressive (GVAR) specification with drifting coefficients and factor stochastic volatility in the errors to model six economies jointly. The measure of uncertainty is constructed endogenously by estimating a scalar driving...
Preprint
Full-text available
We forecast S&P 500 excess returns using a flexible Bayesian econometric state space model with non-Gaussian features at several levels. More precisely, we control for overparameterization via novel global-local shrinkage priors on the state innovation variances as well as the time-invariant part of the state space model. The shrinkage priors are c...
Article
Full-text available
This paper explores the relationship between household income inequality and macro-economic uncertainty in the United States. Using a novel large-scale macroeconometric model, we shed light on regional disparities of inequality responses to a national uncertainty shock. The results suggest that income inequality decreases in most states, with a pro...
Article
Full-text available
This paper uses a factor‐augmented vector autoregressive model to examine the impact of monetary policy shocks on housing prices. To simultaneously estimate the model parameters and unobserved factors we rely on Bayesian estimation and inference. Policy shocks are identified using high‐frequency surprises around policy announcements as an external...
Article
Full-text available
Several recent empirical studies, particularly in the regional economic growth literature, emphasize the importance of explicitly accounting for uncertainty surrounding model specification. Standard approaches to deal with the problem of model uncertainty involve the use of Bayesian model-averaging techniques. However, Bayesian model-averaging for...
Preprint
Full-text available
This paper proposes a hierarchical modeling approach to perform stochastic model specification in Markov switching vector error correction models. We assume that a common distribution gives rise to the regime-specific regression coefficients. The mean as well as the variances of this distribution are treated as fully stochastic and suitable shrinka...
Preprint
In this paper, we explore the relationship between state-level household income inequality and macroeconomic uncertainty in the United States. Using a novel large-scale macroeconometric model, we shed light on regional disparities of inequality responses to a national uncertainty shock. The results suggest that income inequality decreases in most s...
Preprint
Full-text available
This article introduces two absolutely continuous global-local shrinkage priors to enable stochastic variable selection in the context of high-dimensional matrix exponential spatial specifications. Existing approaches as a means to dealing with overparameterization problems in spatial autoregressive specifications typically rely on computationally...
Preprint
In this study interest centers on regional differences in the response of housing prices to monetary policy shocks in the US. We address this issue by analyzing monthly home price data for metropolitan regions using a factor-augmented vector autoregression (FAVAR) model. Bayesian model estimation is based on Gibbs sampling with Normal-Gamma shrinka...
Preprint
Full-text available
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 a...

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Projects

Projects (2)
Project
In this project 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. This model approach provides a joint representation of the exchange rate as well as other latent components like the output gap or trend inflation, quantities that are typically approximated through different observed measures like the unemployment rate.