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Found 13 papers, 2 papers with code
Maximally Forward-Looking Core Inflation
Timely monetary policy decision-making requires timely core inflation measures.
From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks
We reinvigorate maximum likelihood estimation (MLE) for macroeconomic density forecasting through a novel neural network architecture with dedicated mean and variance hemispheres.
Maximally Machine-Learnable Portfolios
We develop a collaborative machine learning algorithm that optimizes portfolio weights so that the resulting synthetic security is maximally predictable.
Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice: Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models
We use "glide charts" (plots of sequences of root mean squared forecast errors as the target date is approached) to evaluate and compare fixed-target forecasts of Arctic sea ice.
When Will Arctic Sea Ice Disappear? Projections of Area, Extent, Thickness, and Volume
Rapidly diminishing Arctic summer sea ice is a strong signal of the pace of global climate change.
A Neural Phillips Curve and a Deep Output Gap
Also, HNN captures out-of-sample the 2021 upswing in inflation and attributes it first to an abrupt and sizable disanchoring of the expectations component, followed by a wildly positive gap starting from late 2020.
Slow-Growing Trees
Random Forest's performance can be matched by a single slow-growing tree (SGT), which uses a learning rate to tame CART's greedy algorithm.
Can Machine Learning Catch the COVID-19 Recession?
Based on evidence gathered from a newly built large macroeconomic data set for the UK, labeled UK-MD and comparable to similar datasets for the US and Canada, it seems the most promising avenue for forecasting during the pandemic is to allow for general forms of nonlinearity by using machine learning (ML) methods.
Time-Varying Parameters as Ridge Regressions
Time-varying parameters (TVPs) models are frequently used in economics to capture structural change.
How is Machine Learning Useful for Macroeconomic Forecasting?
This suggests that Machine Learning is useful for macroeconomic forecasting by mostly capturing important nonlinearities that arise in the context of uncertainty and financial frictions.
Macroeconomic Data Transformations Matter
In a low-dimensional linear regression setup, considering linear transformations/combinations of predictors does not alter predictions.
The Macroeconomy as a Random Forest
I develop Macroeconomic Random Forest (MRF), an algorithm adapting the canonical Machine Learning (ML) tool to flexibly model evolving parameters in a linear macro equation.
