Zobrazeno 1 - 10
of 1 123
pro vyhledávání: '"Medeiros,Marcelo"'
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. Taking the Brazilian case as an application, we c
Externí odkaz:
http://arxiv.org/abs/2308.11173
We propose a model to forecast large realized covariance matrices of returns, applying it to the constituents of the S\&P 500 daily. To address the curse of dimensionality, we decompose the return covariance matrix using standard firm-level factors (
Externí odkaz:
http://arxiv.org/abs/2303.16151
In this paper we examine the relation between market returns and volatility measures through machine learning methods in a high-frequency environment. We implement a minute-by-minute rolling window intraday estimation method using two nonlinear model
Externí odkaz:
http://arxiv.org/abs/2112.15108
This paper has the goal of evaluating how changes in mobility has affected the infection spread of Covid-19 throughout the 2020-2021 years. However, identifying a "clean" causal relation is not an easy task due to a high number of non-observable (beh
Externí odkaz:
http://arxiv.org/abs/2110.00597
In this paper, we apply statistical methods for functional data to explain the heterogeneity in the evolution of number of deaths of Covid-19 over different regions. We treat the cumulative daily number of deaths in a specific region as a curve (func
Externí odkaz:
http://arxiv.org/abs/2109.01952
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. There are many papers, from several different fields, concluding that Google Trends impr
Externí odkaz:
http://arxiv.org/abs/2104.03065
Factor and sparse models are two widely used methods to impose a low-dimensional structure in high-dimensions. However, they are seemingly mutually exclusive. We propose a lifting method that combines the merits of these two models in a supervised le
Externí odkaz:
http://arxiv.org/abs/2102.11341
Publikováno v:
In Wear 15 February 2024 538-539
Publikováno v:
In Cement and Concrete Composites January 2024 145
In this paper we survey the most recent advances in supervised machine learning and high-dimensional models for time series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods we pay special attention to penalize
Externí odkaz:
http://arxiv.org/abs/2012.12802