Michael Pfarrhofer
Dados Biográficos
| ID | 5935582 |
|---|---|
| NOME | Michael Pfarrhofer |
| PRENOMES | Michael |
| SOBRENOME | Pfarrhofer |
| ASSINATURA | PFARRHOFER M |
| AFILIAÇÕES | Vienna University of Economics and Business |
| ORCID | 0000-0002-0168-688X |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 6 |
| TOTAL DE CITAÇÕES | 0 |
| TOTAL COMO AUTOR | 6 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2021 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2025 |
| ÍNDICE H | 0 |
Belief Shocks and Implications of Expectations About Growth‐at‐Risk
This paper revisits the question of how shocks to expectations of market participants can cause business cycle fluctuations. We use a vector autoregression to estimate dynamic causal effects of belief shocks which are extracted from nowcast errors about output growth. In a first step, we replicate and corroborate the findings of Enders, Kleemann, and Müller (2021). The second step computes nowcast errors about growth‐at‐risk at various quantiles.…
Introducing shrinkage in heavy-tailed state space models to predict equity excess returns
We forecast excess returns of the S &P 500 index using a flexible Bayesian econometric state space model with non-Gaussian features at several levels. More precisely, we control for overparameterization via 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 complemented by heavy tailed state innovations that cater for potential large breaks in the l…
Investigating Growth-at-Risk Using a Multicountry Nonparametric Quantile Factor Model
We develop a nonparametric quantile panel regression model. Within each quantile, the quantile function is a combination of linear and nonlinear parts, which we approximate using Bayesian Additive Regression Trees (BART). Cross-sectional information is captured through a conditionally heteroscedastic latent factor. The nonparametric feature enhances flexibility, while the panel feature increases the number of observations in the tails. We develop…
General Bayesian time‐varying parameter vector autoregressions for modeling government bond yields
US yield curve dynamics are subject to time‐variation, but there is ambiguity about its precise form. This paper develops a vector autoregressive (VAR) model with time‐varying parameters and stochastic volatility, which treats the nature of parameter dynamics as unknown. Coefficients can evolve according to a random walk, a Markov switching process, observed predictors, or depend on a mixture of these. To decide which form is supported by the dat…
Dynamic shrinkage in time‐varying parameter stochastic volatility in mean models
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 article, we modify the stochastic volatility in mean (SVM) model by introducing state‐of‐the‐art shrinkage techniques that allow for time variation in the degree of shrinkage. Using a real‐time inflation forecast exercise, we show that empl…
Measuring the effectiveness of US monetary policy during the Covid‐19 recession
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 calming financial markets. To measure these effects at high frequencies, we propose a novel mixed frequen…
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Dynamic shrinkage in time‐varying parameter stochastic volatility in mean models
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 article, we modify the stochastic volatility in mean (SVM) model by introducing state‐of‐the‐art shrinkage techniques that allow for time variation in the degree of shrinkage. Using a real‐time inflation forecast exercise, we show that empl…
Measuring the effectiveness of US monetary policy during the Covid‐19 recession
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 calming financial markets. To measure these effects at high frequencies, we propose a novel mixed frequen…
General Bayesian time‐varying parameter vector autoregressions for modeling government bond yields
US yield curve dynamics are subject to time‐variation, but there is ambiguity about its precise form. This paper develops a vector autoregressive (VAR) model with time‐varying parameters and stochastic volatility, which treats the nature of parameter dynamics as unknown. Coefficients can evolve according to a random walk, a Markov switching process, observed predictors, or depend on a mixture of these. To decide which form is supported by the dat…
Investigating Growth-at-Risk Using a Multicountry Nonparametric Quantile Factor Model
We develop a nonparametric quantile panel regression model. Within each quantile, the quantile function is a combination of linear and nonlinear parts, which we approximate using Bayesian Additive Regression Trees (BART). Cross-sectional information is captured through a conditionally heteroscedastic latent factor. The nonparametric feature enhances flexibility, while the panel feature increases the number of observations in the tails. We develop…
Belief Shocks and Implications of Expectations About Growth‐at‐Risk
This paper revisits the question of how shocks to expectations of market participants can cause business cycle fluctuations. We use a vector autoregression to estimate dynamic causal effects of belief shocks which are extracted from nowcast errors about output growth. In a first step, we replicate and corroborate the findings of Enders, Kleemann, and Müller (2021). The second step computes nowcast errors about growth‐at‐risk at various quantiles.…
Introducing shrinkage in heavy-tailed state space models to predict equity excess returns
We forecast excess returns of the S &P 500 index using a flexible Bayesian econometric state space model with non-Gaussian features at several levels. More precisely, we control for overparameterization via 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 complemented by heavy tailed state innovations that cater for potential large breaks in the l…
Monetary Policy and Economic Impact (6 obras) · Econometrics (5 obras) · Mathematics (5 obras) · Statistics (5 obras) · Bayesian probability (4 obras) · Economics (4 obras) · Computer Science (3 obras) · Financial Risk and Volatility Modeling (3 obras) · Market Dynamics and Volatility (3 obras) · Prior probability (3 obras)