Cover Image

Maximally Forward-Looking Core Inflation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Coulombe, Philippe Goulet, Klieber, Karin, Barrette, Christophe, Goebel, Maximilian
Format:	Preprint
Published:	2024
Subjects:	Econometrics Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Dual Interpretation of Machine Learning Forecasts
by: Coulombe, Philippe Goulet, et al.
Published: (2024)

Opening the Black Box of Local Projections
by: Coulombe, Philippe Goulet, et al.
Published: (2025)

Maximally Machine-Learnable Portfolios
by: Coulombe, Philippe Goulet, et al.
Published: (2023)

From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks
by: Coulombe, Philippe Goulet, et al.
Published: (2023)

An Adaptive Moving Average for Macroeconomic Monitoring
by: Coulombe, Philippe Goulet, et al.
Published: (2025)

To Bag is to Prune
by: Coulombe, Philippe Goulet
Published: (2020)

A Neural Phillips Curve and a Deep Output Gap
by: Coulombe, Philippe Goulet
Published: (2022)

LGB+: A Macroeconomic Forecasting Road Test
by: Coulombe, Philippe Goulet
Published: (2026)

Time-Varying Parameters as Ridge Regressions
by: Coulombe, Philippe Goulet
Published: (2020)

From Reactive to Proactive Volatility Modeling With Hemisphere Neural Networks
by: Philippe Goulet Coulombe, et al.
Published: (2026)

Ordinary Least Squares as an Attention Mechanism
by: Coulombe, Philippe Goulet
Published: (2025)

Quantifying the Risk-Return Tradeoff in Forecasting
by: Coulombe, Philippe Goulet
Published: (2026)

Bayesian Neural Networks for Macroeconomic Analysis
by: Hauzenberger, Niko, et al.
Published: (2022)

Asymmetries in Financial Spillovers
by: Huber, Florian, et al.
Published: (2024)

fmeffects: An R Package for Forward Marginal Effects
by: Löwe, Holger, et al.
Published: (2023)

A Job I Like or a Job I Can Get: Designing Job Recommender Systems Using Field Experiments
by: Bied, Guillaume, et al.
Published: (2026)

Machine Learning the Macroeconomic Effects of Financial Shocks
by: Hauzenberger, Niko, et al.
Published: (2024)

Navigating Inflation in Ghana: How Can Machine Learning Enhance Economic Stability and Growth Strategies
by: Baidoo, Theophilus G., et al.
Published: (2024)

Probabilistic Quantile Factor Analysis
by: Korobilis, Dimitris, et al.
Published: (2022)

Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring
by: Sullivan, Hué, et al.
Published: (2022)

Adaptive maximization of social welfare
by: Cesa-Bianchi, Nicolo, et al.
Published: (2023)

Mixing it up: Inflation at risk
by: Schröder, Maximilian
Published: (2024)

From Cross-Validation to SURE: Asymptotic Risk of Tuned Regularized Estimators
by: Adusumilli, Karun, et al.
Published: (2026)

Bridging the Gap between Empirical Welfare Maximization and Conditional Average Treatment Effect Estimation in Policy Learning
by: Kato, Masahiro
Published: (2025)

Inference for Regression with Variables Generated by AI or Machine Learning
by: Battaglia, Laura, et al.
Published: (2024)

Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study
by: Bach, Philipp, et al.
Published: (2024)

High-Dimensional Tail Index Regression
by: Sasaki, Yuya, et al.
Published: (2024)

Calibrating doubly-robust estimators with unbalanced treatment assignment
by: Ballinari, Daniele
Published: (2024)

Efficient Computation of Confidence Sets Using Classification on Equidistributed Grids
by: Zhou, Lujie
Published: (2024)

Nowcasting with Mixed Frequency Data Using Gaussian Processes
by: Hauzenberger, Niko, et al.
Published: (2024)

An economically-consistent discrete choice model with flexible utility specification based on artificial neural networks
by: Hernandez, Jose Ignacio, et al.
Published: (2024)

CAVIAR: Categorical-Variable Embeddings for Accurate and Robust Inference
by: Mukherjee, Anirban, et al.
Published: (2024)

Statistical Inference of Optimal Allocations I: Regularities and their Implications
by: Feng, Kai, et al.
Published: (2024)

Higher-Order Causal Message Passing for Experimentation with Complex Interference
by: Bayati, Mohsen, et al.
Published: (2024)

Model Averaging and Double Machine Learning
by: Ahrens, Achim, et al.
Published: (2024)

Inference for an Algorithmic Fairness-Accuracy Frontier
by: Liu, Yiqi, et al.
Published: (2024)

Semiparametric Bayesian Difference-in-Differences
by: Breunig, Christoph, et al.
Published: (2024)

Distilling interpretable causal trees from causal forests
by: Rehill, Patrick
Published: (2024)

Difference-in-Differences with Time-varying Continuous Treatments using Double/Debiased Machine Learning
by: Haddad, Michel F. C., et al.
Published: (2024)

Improving the Finite Sample Estimation of Average Treatment Effects using Double/Debiased Machine Learning with Propensity Score Calibration
by: Ballinari, Daniele, et al.
Published: (2024)