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Found 30 papers, 2 papers with code
On Convolutional Vision Transformers for Yield Prediction
While a variety of methods offer good yield prediction on histogrammed remote sensing data, vision Transformers are only sparsely represented in the literature.
Nowcasting economic activity in European regions using a mixed-frequency dynamic factor model
Timely information about the state of regional economies can be essential for planning, implementing and evaluating locally targeted economic policies.
Bayesian Nonlinear Regression using Sums of Simple Functions
In a real-data exercise, we forecast US macroeconomic aggregates and consider the nonlinear effects of financial shocks in a large-scale nonlinear VAR.
Predictive Density Combination Using a Tree-Based Synthesis Function
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.
Fast and Order-invariant Inference in Bayesian VARs with Non-Parametric Shocks
The shocks which hit macroeconomic models such as Vector Autoregressions (VARs) have the potential to be non-Gaussian, exhibiting asymmetries and fat tails.
Coarsened Bayesian VARs -- Correcting BVARs for Incorrect Specification
Model mis-specification in multivariate econometric models can strongly influence quantities of interest such as structural parameters, forecast distributions or responses to structural shocks, even more so if higher-order forecasts or responses are considered, due to parameter convolution.
Grouping Shapley Value Feature Importances of Random Forests for explainable Yield Prediction
Explainability in yield prediction helps us fully explore the potential of machine learning models that are already able to achieve high accuracy for a variety of yield prediction scenarios.
A tale of two tails: 130 years of growth-at-risk
In this context, the paper also adds to the financial cycle literature by completing the picture of drivers (and risks) for both booms and recessions over time.
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.
Bayesian Forecasting in Economics and Finance: A Modern Review
The Bayesian statistical paradigm provides a principled and coherent approach to probabilistic forecasting.
Bayesian Neural Networks for Macroeconomic Analysis
Neural networks, by contrast, are designed for datasets with millions of observations and covariates.
Bayesian Modeling of TVP-VARs Using Regression Trees
This leads to great flexibility in the nature and extent of parameter change, both in the conditional mean and in the conditional variance.
Extreme Gradient Boosting for Yield Estimation compared with Deep Learning Approaches
While the accuracies reached with those approaches are promising, the needed amounts of data and the ``black-box'' nature can restrict the application of Deep Learning methods.
Forecasting euro area inflation using a huge panel of survey expectations
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.
Unsupervised Features Ranking via Coalitional Game Theory for Categorical Data
Unsupervised feature selection aims to reduce the number of features, often using feature importance scores to quantify the relevancy of single features to the task at hand.
Forecasting US Inflation Using Bayesian Nonparametric Models
The relationship between inflation and predictors such as unemployment is potentially nonlinear with a strength that varies over time, and prediction errors error may be subject to large, asymmetric shocks.
Measuring Shocks to Central Bank Independence using Legal Rulings
We investigate the consequences of legal rulings on the conduct of monetary policy.
Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty
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.
Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model
We develop a Bayesian non-parametric quantile panel regression model.
Subspace Shrinkage in Conjugate Bayesian Vector Autoregressions
In a forecasting exercise involving a large macroeconomic data set we find that combining VARs with factor models using our prior can lead to forecast improvements.
Approximate Bayesian inference and forecasting in huge-dimensional multi-country VARs
Panel Vector Autoregressions (PVARs) are a popular tool for analyzing multi-country datasets.
General Bayesian time-varying parameter VARs for predicting government bond yields
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.
Global martingale solutions for a stochastic Shigesada-Kawasaki-Teramoto population model
The existence of global nonnegative martingale solutions to a cross-diffusion system of Shigesada-Kawasaki-Teramoto type with multiplicative noise is proven.
Probability Analysis of PDEs 60H15, 35R60, 35Q92
Real-time Inflation Forecasting Using Non-linear Dimension Reduction Techniques
Among the techniques considered, the Autoencoder and squared principal components yield factors that have high predictive power for one-month- and one-quarter-ahead inflation.
Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs
This paper develops Bayesian econometric methods for posterior inference in non-parametric mixed frequency VARs using additive regression trees.
Category-Level 3D Non-Rigid Registration from Single-View RGB Images
This is done by training a CNN that infers a deformation field for the visible parts of the canonical model and by employing a learned shape (latent) space for inferring the deformations of the occluded parts.
Inference in Bayesian Additive Vector Autoregressive Tree Models
The resulting Bayesian additive vector autoregressive tree (BAVART) model is capable of capturing arbitrary non-linear relations between the endogenous variables and the covariates without much input from the researcher.
Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods
Time-varying parameter (TVP) regression models can involve a huge number of coefficients.
Fast and Flexible Bayesian Inference in Time-varying Parameter Regression Models
In this paper, 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.
A Bayesian panel VAR model to analyze the impact of climate change on high-income economies
In this paper, we assess the impact of climate shocks on futures markets for agricultural commodities and a set of macroeconomic quantities for multiple high-income economies.
