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Found 8 papers, 0 papers with code
Forecasting inflation using disaggregates and machine learning
This paper examines the effectiveness of several forecasting methods for predicting inflation, focusing on aggregating disaggregated forecasts - also known in the literature as the bottom-up approach.
Forecasting Large Realized Covariance Matrices: The Benefits of Factor Models and Shrinkage
We propose a model to forecast large realized covariance matrices of returns, applying it to the constituents of the S\&P 500 daily.
Modeling and Forecasting Intraday Market Returns: a Machine Learning Approach
In this paper we examine the relation between market returns and volatility measures through machine learning methods in a high-frequency environment.
The Proper Use of Google Trends in Forecasting Models
It is widely known that Google Trends have become one of the most popular free tools used by forecasters both in academics and in the private and public sectors.
Bridging factor and sparse models
Factor and sparse models are two widely used methods to impose a low-dimensional structure in high-dimensions.
Machine Learning Advances for Time Series Forecasting
In this paper we survey the most recent advances in supervised machine learning and high-dimensional models for time series forecasting.
Do We Exploit all Information for Counterfactual Analysis? Benefits of Factor Models and Idiosyncratic Correction
The data consist of daily sales and prices of five different products over more than 400 municipalities.
Regularized Estimation of High-Dimensional Vector AutoRegressions with Weakly Dependent Innovations
There has been considerable advance in understanding the properties of sparse regularization procedures in high-dimensional models.
