Zobrazeno 1 - 10
of 91
pro vyhledávání: '"Hauzenberger, Niko"'
We develop Bayesian machine learning methods for mixed data sampling (MIDAS) regressions. This involves handling frequency mismatches and specifying functional relationships between many predictors and the dependent variable. We use Gaussian processe
Externí odkaz:
http://arxiv.org/abs/2402.10574
Autor:
Hauzenberger, Niko, Huber, Florian
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
Externí odkaz:
http://epub.wu.ac.at/6770/1/wp276.pdf
In this paper, we estimate a Bayesian vector autoregressive (VAR) model with factor stochastic volatility in the error term to assess the effects of an uncertainty shock in the Euro area (EA). This allows us to incorporate uncertainty directly into t
Externí odkaz:
http://epub.wu.ac.at/6246/1/wp261.pdf
Bayesian predictive synthesis (BPS) provides a method for combining multiple predictive distributions based on agent/expert opinion analysis theory and encompasses a range of existing density forecast pooling methods. The key ingredient in BPS is a `
Externí odkaz:
http://arxiv.org/abs/2311.12671
Macroeconomic data is characterized by a limited number of observations (small T), many time series (big K) but also by featuring temporal dependence. Neural networks, by contrast, are designed for datasets with millions of observations and covariate
Externí odkaz:
http://arxiv.org/abs/2211.04752
In light of widespread evidence of parameter instability in macroeconomic models, many time-varying parameter (TVP) models have been proposed. This paper proposes a nonparametric TVP-VAR model using Bayesian additive regression trees (BART) that mode
Externí odkaz:
http://arxiv.org/abs/2209.11970
Publikováno v:
In International Journal of Forecasting January-March 2025 41(1):361-376
We develop a non-parametric 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 proce
Externí odkaz:
http://arxiv.org/abs/2112.01995
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
Externí odkaz:
http://arxiv.org/abs/2102.13393
In this paper, we assess whether using non-linear dimension reduction techniques pays off for forecasting inflation in real-time. Several recent methods from the machine learning literature are adopted to map a large dimensional dataset into a lower
Externí odkaz:
http://arxiv.org/abs/2012.08155