Michael Pfarrhofer

AP @WU_econ, Bayesian econometrics and empirical macroeconomics (monetary, business cycles, now/forecasting)

[Michael Pfarrhofer @mpfarrho]

@HallaMartin Ride the Lightning oder in live:
https://t.co/6Y5s2E9Kbf

[Michael Pfarrhofer @mpfarrho]

➡️ Nonparametric observation equation, specified via Gaussian process for each series
➡️ Factors modeled with a VAR (easy computation and interpretation)
➡️ Applications: (1) forecasting with FRED-QD, (2) extracting drivers of global inflation, measuring international asymmetries

[Michael Pfarrhofer @mpfarrho]

New working paper "A Bayesian Gaussian Process Dynamic Factor Model" with T. Chernis (BMA), N. Hauzenberger (Uni Strathclyde), and H. Mumtaz (QMUL): https://t.co/zpujxcBcXy

Proposes a DFM where the latent factors are linked to observed variables with unknown nonlinear functions

[SUERF - The European Money and Finance Forum @suerf_org]

#SUERFpolicybrief “Monetary policy under economic uncertainty: Evidence from the Euro Area" by Niko Hauzenberger (@UniStrathclyde), @mpfarrho (@wu_vienna), and Anna Stelzer (@oenb)

📄https://t.co/c0Vfqp6sgP

[CEPR @cepr_org]

New CEPR Discussion Paper - DP20442
Nonparametric Mixed Frequency Monitoring Macro-at-Risk
Massimiliano Marcellino @Unibocconi, @mpfarrho @wu_vienna @WU_econ
https://t.co/6A6MvjBPA7
#CEPR_MEF #EconTwitter

[CEPR @cepr_org]

New CEPR Discussion Paper - DP19965
Bayesian Nowcasting with Mixed Frequency Data Using Gaussian Processes
@NHauzenberger @Strath_Econ, Massimiliano Marcellino @Unibocconi, @mpfarrho @WU_econ, Anna Stelzer @oenb
https://t.co/7e3SQfsZ54
#CEPR_MEF #EconTwitter

[Michael Pfarrhofer @mpfarrho]

"Scenario analysis with multivariate Bayesian machine learning models"
▶️ Conditional forecasts, generalized IRFs in dynamic nonparametric models
▶️ Macroeconomic stress testing; macroeconomic risk and financial conditions; financial shock transmission
https://t.co/VdKUWS3igj

[Michael Pfarrhofer @mpfarrho]

“Interpretable Bayesian machine learning for assessing the effects of climate news shocks on firm-level returns”:
▶️ Bayesian asset pricing framework & machine learning
▶️ text-based climate risk indicators
▶️ asymmetric impacts across firms
https://t.co/STLRVF42bD

[Martin Halla @HallaMartin]

#Econ Job Vacancy — Post Doc, 2 years at WU Vienna

Are you interested in collaborating with us (@WFrimmel , @HO2604 ) on research papers focused on intergenerational mobility in Austria?
- Vienna University of Economics & Business (@wu_vienna)
- Fulltime research position at the Department of Econ (@WU_econ)
- Starting date in spring 2025
- Application Deadline: March 4, 2025
- More info: https://t.co/DsLyZxcVoO
- Pls share/RT

[Martin Halla @HallaMartin]

You want to do your #PhD in #Economics at one of Europe’s largest and most modern business & economics universities. Join us @wu_vienna, @WU_econ. Click her https://t.co/lS5tD1Bqyk
#Vienna 🇦🇹

[WU Economics Dept. @WU_econ]

WE ARE HIRING!

Want to do a PhD in economics? Stop searching and click here: https://t.co/yTYims1pge

[Michael Pfarrhofer @mpfarrho]

In this new (significantly revised) WP, @annstelzer and I (@WU_econ) discuss how to extract multiple structural shocks (monetary policy and central bank information) from multiple external instruments across multiple economies (US, EA and a few more) https://t.co/b3ONVKpu0e

[Michael Pfarrhofer @mpfarrho]

"Bayesian nonparametric methods for macroeconomic forecasting" (chapter 5), co-authored by Massimiliano Marcellino (@Unibocconi) and me (@WU_econ) #econometrics #forecasting
https://t.co/WKDmehp3f5

[Elgar Economics @Elgar_Economics]

📣 NEW: Handbook of Research Methods and Applications in Macroeconomic Forecasting by Michael P. Clements & Ana Beatriz Galvão

Chapter 14 is #OpenAccess at: https://t.co/jMsY2BVmO1

More info: https://t.co/a4tHkc2ZJC

#MacroeconomicForecasting #Econometrics @AndrewBMartinez

[Michael Pfarrhofer @mpfarrho]

Final few days left (deadline on November 20) to apply for the non-tenure track Assistant Professor position (post-doc) in Macroeconomics at @WU_econ at @wu_vienna #EconJobMarket https://t.co/D7ZdpxIqLI

[Michael Pfarrhofer @mpfarrho]

See also on @EconJobMarket, for the macro AP position (deadline on Nov 20, 2024) @wu_vienna: https://t.co/D7ZdpxIqLI

[Michael Pfarrhofer @mpfarrho]

See also on @EconJobMarket, for the macro AP position (deadline on Nov 20, 2024) @wu_vienna: https://t.co/D7ZdpxIqLI

[WU Economics Dept. @WU_econ]

WE ARE HIRING! We offer three positions as Assistant Professor, non-tenure track: IO, macroeconomics, and public economics. Details at
https://t.co/uni1g3gzZR

[WU Economics Dept. @WU_econ]

Welcome to our new colleagues: Jonathan Fitter, Nicolas Göller, Maximilian Heinze, Sannah Tijani (PhD program) & Kentaro Asai, Federica Braccioli (@fede_braccioli), Davide Cerruti (@dcerruti), Shahroo Malik, Bernhard Schmidpeter (@BernhardEcon), Nina Xue (Assistant Professors)!

[Michael Pfarrhofer @mpfarrho]

➡ Time-varying parameter (TVP) vector error correction model (VECM) with heteroskedastic errors
➡ Automatic dynamic model specification with cointegrated time series: global–local priors and postprocessing to achieve sparsity
➡ Applied to modeling European electricity prices

[Michael Pfarrhofer @mpfarrho]

“Sparse time-varying parameter VECMs with an application to modeling electricity prices” by N. Hauzenberger (@UniStrathclyde), L. Rossini (@LaStatale) and me (@wu_vienna, @WU_econ) published in the International Journal of Forecasting (IJF, @IIForecasters)
https://t.co/CCt2edlFYB

[WU Economics Dept. @WU_econ]

New research by @mpfarrho (@wu_vienna) and co-authors develops Bayesian machine learning tools, harnessing macroeconomic big data to accurately monitor the current state of the economy. https://t.co/W5VSMpdo7M

[Michael Pfarrhofer @mpfarrho]

Our proposed framework leverages macroeconomic Big Data in a computationally efficient way and offers gains in predictive accuracy compared to other machine learning approaches.

[Michael Pfarrhofer @mpfarrho]

"Nowcasting with Mixed Frequency Data Using Gaussian Processes" by N Hauzenberger (@UniStrathclyde), M Marcellino (@Unibocconi), A Stelzer (@oenb) and me (@WU_econ).
We develop Bayesian machine learning methods for mixed data sampling (MIDAS) regressions.
https://t.co/QK189mG8ht

[Michael Pfarrhofer @mpfarrho]

We evaluate predictions in short-horizon now- and forecasting exercises with both simulated data and data on quarterly US output growth and inflation in the GDP deflator.