Florian Huber
Biographic Data
| ID | 5737747 |
|---|---|
| NAME | Florian Huber |
| GIVEN NAMES | Florian |
| FAMILY NAME | Huber |
| SIGNATURE | HUBER F |
| AFFILIATIONS | University of Salzburg |
| ORCID | 0000-0002-2896-7921 |
| VERIFIED | Yes |
| TOTAL WORKS | 21 |
| TOTAL CITATIONS | 9 |
| WORKS AS AUTHOR | 20 |
| WORKS AS EDITOR | 1 |
| FIRST PUBLICATION YEAR | 2013 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Forecasting Natural Gas Prices in Real Time
This paper provides a comprehensive analysis of the forecastability of the real price of natural gas in the United States at the monthly frequency considering a universe of models that differ in complexity and economic content. We find that considerable reductions in mean‐squared prediction error relative to a no‐change benchmark can be achieved in real time for horizons of up to 2 years. A particularly promising model is a vector autoregressive …
DBT-K for Adolescents
Background : Mental disorders and emotion dysregulation are highly prevalent in adolescence, yet access to intensive, age-appropriate treatments remains limited. This pilot study examined the feasibility and preliminary effects of a creative, 8-week, DBT-based transdiagnostic skills group for adolescents (DBT-K). Methods : 53 outpatients (aged 13-18, 50 female) with heterogeneous psychiatric diagnoses completed baseline self-report measures of in…
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…
Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty
We develop a nonparametric multivariate time series model that remains agnostic on the precise relationship between a (possibly) large set of macroeconomic time series and their lagged values. The main building block of our model is a Gaussian process prior on the functional relationship that determines the conditional mean of the model, hence, the name of Gaussian process vector autoregression (GP-VAR). A flexible stochastic volatility specifica…
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 nonlinearities, we include several nonlinear specifications. The resulting models will be …
Fast and order‐invariant inference in Bayesian VARs with nonparametric shocks
The shocks that hit macroeconomic models such as Vector Autoregressions (VARs) have the potential to be non‐Gaussian, exhibiting asymmetries and fat tails. This consideration motivates the VAR developed in this paper that uses a Dirichlet process mixture (DPM) to model the reduced‐form shocks. However, we do not follow the obvious strategy of simply modeling the VAR errors with a DPM as this would lead to computationally infeasible Bayesian infer…
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…
Subspace shrinkage in conjugate Bayesian vector autoregressions
Macroeconomists using large datasets often face the choice of working with either a large vector autoregression (VAR) or a factor model. In this paper, we develop a conjugate Bayesian VAR with a subspace shrinkage prior that combines the two. This prior shrinks towards the subspace which is defined by a factor model. Our approach allows for estimating the strength of the shrinkage and the number of factors. After establishing the theoretical prop…
Fast and Flexible Bayesian Inference in Time-varying Parameter Regression Models
In this article, we write the time-varying parameter (TVP) regression model involving K explanatory variables and T observations as a constant coefficient regression model with KT explanatory variables. In contrast with much of the existing literature which assumes coefficients to evolve according to a random walk, a hierarchical mixture model on the TVPs is introduced. The resulting model closely mimics a random coefficients specification which …
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…
Combining shrinkage and sparsity in conjugate vector autoregressive models
Conjugate priors allow for fast inference in large dimensional vector autoregressive (VAR) models. But at the same time, they introduce the restriction that each equation features the same set of explanatory variables. This paper proposes a straightforward means of postprocessing posterior estimates of a conjugate Bayesian VAR to effectively perform equation‐specific covariate selection. Compared with existing techniques using shrinkage alone, ou…
Inducing Sparsity and Shrinkage in Time-Varying Parameter Models
Time-varying parameter (TVP) models have the potential to be over-parameterized, particularly when the number of variables in the model is large. Global-local priors are increasingly used to induce shrinkage in such models. But the estimates produced by these priors can still have appreciable uncertainty. Sparsification has the potential to remove this uncertainty and improve forecasts. In this paper, we develop computationally simple methods whi…
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…
Country-Level Relationships of the Human Intake of N and P, Animal and Vegetable Food, and Alcoholic Beverages with Cancer and Life Expectancy
Our analyses, thus, yielded four generally consistent conclusions. First, the excessive intake of terrestrial animal food, especially the levels of protein, N, and P, is associated with higher prevalence of cancer, whereas equivalent intake from vegetables is associated with lower prevalence. Second, no consistent relationship was found for food N:P ratio and cancer prevalence. Third, the consumption of alcoholic beverages correlates with prevale…
Trend Fundamentals and Exchange Rate Dynamics
We estimate a multivariate unobserved components stochastic volatility model to explain the dynamics of a panel of six exchange rates against the US dollar. The empirical model is based on the assumption that two countries’ monetary policy strategies may be well described by Taylor rules with a time‐varying inflation target, a time‐varying natural rate of unemployment, and interest rate smoothing. Compared to the existing literature, our model si…
Ggir
Recent technological advances have transformed the research on physical activity initially based on questionnaire data to the most recent objective data from accelerometers. The shift to availability of raw accelerations has increased measurement accuracy, transparency, and the potential for data harmonization. However, it has also shifted the need for considerable processing expertise to the researcher. Many users do not have this expertise. The…
Adaptive Shrinkage in Bayesian Vector Autoregressive Models
Vector autoregressive (VAR) models are frequently used for forecasting and impulse response analysis. For both applications, shrinkage priors can help improving inference. In this article, we apply the Normal-Gamma shrinkage prior to the VAR with stochastic volatility case and derive its relevant conditional posterior distributions. This framework imposes a set of normally distributed priors on the autoregressive coefficients and the covariance p…
Milieu
Fragen, die das Verhältnis vom Lokalen zum Globalen zum Inhalt haben, die sich mit den Beziehungen des Subjekts zu seiner Umwelt beschäftigen und daher an einer Vertiefung von Umgebungswissen interessiert sind, können als Kernthemen moderner Wissenschaft gelten. Im Mittelpunkt steht dabei zumeist ein Begriff, der historisch an prominenter Stelle zwischen diesen Themen und Problemstellungen vermittelt hat: Milieu. Milieu – Umgebungen des Lebendige…
Migrant Businesses And The Symbolic Transformation Of Urban Neighborhoods
Migrant businesses play a crucial role not only in the economies of Western European cities, but also in the revaluation of urban space. Alongside their relevance to physical neighborhood development, migrant businesses seem to be crucial in transforming urban spaces symbolically. Although there is some evidence from migrant business research and the literature on gentrification, the phenomenon has not yet been studied systematically. We therefor…
Spiegelbilder vom Meeresgrund. Leopold Blaschkas marine Aquarien
Reflections from the Ocean Floor. Leopold Blaschka's Marine Aquaria. The paper focuses on the glass models of marine invertebrates by Leopold Blaschka (1822–1895). In the beginning of his career, Blaschka did not produce stand‐alone scientific models. Instead, he chose to create “marine aquaria”, i.e. dry fish tanks filled with artificial sea creatures, to promote and present his glass objects to the public. This concept was clearly inspired by t…
Migrant Businesses And The Symbolic Transformation Of Urban Neighborhoods
Migrant businesses play a crucial role not only in the economies of Western European cities, but also in the revaluation of urban space. Alongside their relevance to physical neighborhood development, migrant businesses seem to be crucial in transforming urban spaces symbolically. Although there is some evidence from migrant business research and the literature on gentrification, the phenomenon has not yet been studied systematically. We therefor…
Spiegelbilder vom Meeresgrund. Leopold Blaschkas marine Aquarien
Reflections from the Ocean Floor. Leopold Blaschka's Marine Aquaria. The paper focuses on the glass models of marine invertebrates by Leopold Blaschka (1822–1895). In the beginning of his career, Blaschka did not produce stand‐alone scientific models. Instead, he chose to create “marine aquaria”, i.e. dry fish tanks filled with artificial sea creatures, to promote and present his glass objects to the public. This concept was clearly inspired by t…
Migrant Businesses And The Symbolic Transformation Of Urban Neighborhoods
Migrant businesses play a crucial role not only in the economies of Western European cities, but also in the revaluation of urban space. Alongside their relevance to physical neighborhood development, migrant businesses seem to be crucial in transforming urban spaces symbolically. Although there is some evidence from migrant business research and the literature on gentrification, the phenomenon has not yet been studied systematically. We therefor…
Milieu
Fragen, die das Verhältnis vom Lokalen zum Globalen zum Inhalt haben, die sich mit den Beziehungen des Subjekts zu seiner Umwelt beschäftigen und daher an einer Vertiefung von Umgebungswissen interessiert sind, können als Kernthemen moderner Wissenschaft gelten. Im Mittelpunkt steht dabei zumeist ein Begriff, der historisch an prominenter Stelle zwischen diesen Themen und Problemstellungen vermittelt hat: Milieu. Milieu – Umgebungen des Lebendige…
Ggir
Recent technological advances have transformed the research on physical activity initially based on questionnaire data to the most recent objective data from accelerometers. The shift to availability of raw accelerations has increased measurement accuracy, transparency, and the potential for data harmonization. However, it has also shifted the need for considerable processing expertise to the researcher. Many users do not have this expertise. The…
Adaptive Shrinkage in Bayesian Vector Autoregressive Models
Vector autoregressive (VAR) models are frequently used for forecasting and impulse response analysis. For both applications, shrinkage priors can help improving inference. In this article, we apply the Normal-Gamma shrinkage prior to the VAR with stochastic volatility case and derive its relevant conditional posterior distributions. This framework imposes a set of normally distributed priors on the autoregressive coefficients and the covariance p…
Country-Level Relationships of the Human Intake of N and P, Animal and Vegetable Food, and Alcoholic Beverages with Cancer and Life Expectancy
Our analyses, thus, yielded four generally consistent conclusions. First, the excessive intake of terrestrial animal food, especially the levels of protein, N, and P, is associated with higher prevalence of cancer, whereas equivalent intake from vegetables is associated with lower prevalence. Second, no consistent relationship was found for food N:P ratio and cancer prevalence. Third, the consumption of alcoholic beverages correlates with prevale…
Trend Fundamentals and Exchange Rate Dynamics
We estimate a multivariate unobserved components stochastic volatility model to explain the dynamics of a panel of six exchange rates against the US dollar. The empirical model is based on the assumption that two countries’ monetary policy strategies may be well described by Taylor rules with a time‐varying inflation target, a time‐varying natural rate of unemployment, and interest rate smoothing. Compared to the existing literature, our model si…
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…
Combining shrinkage and sparsity in conjugate vector autoregressive models
Conjugate priors allow for fast inference in large dimensional vector autoregressive (VAR) models. But at the same time, they introduce the restriction that each equation features the same set of explanatory variables. This paper proposes a straightforward means of postprocessing posterior estimates of a conjugate Bayesian VAR to effectively perform equation‐specific covariate selection. Compared with existing techniques using shrinkage alone, ou…
Inducing Sparsity and Shrinkage in Time-Varying Parameter Models
Time-varying parameter (TVP) models have the potential to be over-parameterized, particularly when the number of variables in the model is large. Global-local priors are increasingly used to induce shrinkage in such models. But the estimates produced by these priors can still have appreciable uncertainty. Sparsification has the potential to remove this uncertainty and improve forecasts. In this paper, we develop computationally simple methods whi…
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…
Fast and Flexible Bayesian Inference in Time-varying Parameter Regression Models
In this article, we write the time-varying parameter (TVP) regression model involving K explanatory variables and T observations as a constant coefficient regression model with KT explanatory variables. In contrast with much of the existing literature which assumes coefficients to evolve according to a random walk, a hierarchical mixture model on the TVPs is introduced. The resulting model closely mimics a random coefficients specification which …
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…
Subspace shrinkage in conjugate Bayesian vector autoregressions
Macroeconomists using large datasets often face the choice of working with either a large vector autoregression (VAR) or a factor model. In this paper, we develop a conjugate Bayesian VAR with a subspace shrinkage prior that combines the two. This prior shrinks towards the subspace which is defined by a factor model. Our approach allows for estimating the strength of the shrinkage and the number of factors. After establishing the theoretical prop…
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 nonlinearities, we include several nonlinear specifications. The resulting models will be …
Fast and order‐invariant inference in Bayesian VARs with nonparametric shocks
The shocks that hit macroeconomic models such as Vector Autoregressions (VARs) have the potential to be non‐Gaussian, exhibiting asymmetries and fat tails. This consideration motivates the VAR developed in this paper that uses a Dirichlet process mixture (DPM) to model the reduced‐form shocks. However, we do not follow the obvious strategy of simply modeling the VAR errors with a DPM as this would lead to computationally infeasible Bayesian infer…
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…
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…
Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty
We develop a nonparametric multivariate time series model that remains agnostic on the precise relationship between a (possibly) large set of macroeconomic time series and their lagged values. The main building block of our model is a Gaussian process prior on the functional relationship that determines the conditional mean of the model, hence, the name of Gaussian process vector autoregression (GP-VAR). A flexible stochastic volatility specifica…
Forecasting Natural Gas Prices in Real Time
This paper provides a comprehensive analysis of the forecastability of the real price of natural gas in the United States at the monthly frequency considering a universe of models that differ in complexity and economic content. We find that considerable reductions in mean‐squared prediction error relative to a no‐change benchmark can be achieved in real time for horizons of up to 2 years. A particularly promising model is a vector autoregressive …
DBT-K for Adolescents
Background : Mental disorders and emotion dysregulation are highly prevalent in adolescence, yet access to intensive, age-appropriate treatments remains limited. This pilot study examined the feasibility and preliminary effects of a creative, 8-week, DBT-based transdiagnostic skills group for adolescents (DBT-K). Methods : 53 outpatients (aged 13-18, 50 female) with heterogeneous psychiatric diagnoses completed baseline self-report measures of in…
Econometrics (13 works) · Bayesian probability (12 works) · Computer Science (12 works) · Mathematics (12 works) · Monetary Policy and Economic Impact (11 works) · Statistics (11 works) · Artificial Intelligence (8 works) · Market Dynamics and Volatility (8 works) · Bayesian inference (7 works) · Prior probability (7 works)