Which One Predicts Better?: Comparing Different GDP Nowcasting Methods Using Brazilian Data

The objective of this paper is to develop a basic framework for the implementation of a GDP nowcasting strategy using Brazilian data. Our goal is to identify a scalable strategy that allows us to project the Brazilian GDP in real time at any point during the current quarter. In the paper we detail the survey of classical techniques and also of techniques usually known by market practitioners as "machine learning methods". We survey the literature since the first work on estimating business cycles and document the evolution of this literature until the insertion of machine learning methods. Additionally, we perform backtesting exercises, estimate several candidate models for GDP nowcasting. Finally, we evaluate the forecasting power of all models against a naive model and a market expectations model. We demonstrate that a combination of machine learning models based on the distance of forecasts to the average market expectations defeats the fully informed market expectations, while the same is not possible for selected classical nowcasting models.

Saved in:
Bibliographic Details
Main Author: Inter-American Development Bank
Other Authors: Lucas Gabriel Martins de Oliveira
Language:English
Published: Inter-American Development Bank
Subjects:Inflation, Potential Output, Output Gap, Macroeconomic Policy, Development Bank, Debtor Finance, Prediction Market, Gross Domestic Product, Economy, Machine Learning, Learning Strategy, Industrial Productivity, Learning, Economic Development, C53 - Forecasting and Prediction Methods • Simulation Methods, C45 - Neural Networks and Related Topics, E17 - Forecasting and Simulation: Models and Applications, Macroeconometrics;machine learning;Forecasting;Nowcasting;GDP;Brazil,
Online Access:http://dx.doi.org/10.18235/0005004
https://publications.iadb.org/en/which-one-predicts-better-comparing-different-gdp-nowcasting-methods-using-brazilian-data
Tags: Add Tag
No Tags, Be the first to tag this record!
id dig-bid-node-33911
record_format koha
spelling dig-bid-node-339112023-08-02T17:13:29ZWhich One Predicts Better?: Comparing Different GDP Nowcasting Methods Using Brazilian Data 2023-07-14T00:07:00+0000 http://dx.doi.org/10.18235/0005004 https://publications.iadb.org/en/which-one-predicts-better-comparing-different-gdp-nowcasting-methods-using-brazilian-data Inter-American Development Bank Inflation Potential Output Output Gap Macroeconomic Policy Development Bank Debtor Finance Prediction Market Gross Domestic Product Economy Machine Learning Learning Strategy Industrial Productivity Learning Economic Development C53 - Forecasting and Prediction Methods • Simulation Methods C45 - Neural Networks and Related Topics E17 - Forecasting and Simulation: Models and Applications Macroeconometrics;machine learning;Forecasting;Nowcasting;GDP;Brazil The objective of this paper is to develop a basic framework for the implementation of a GDP nowcasting strategy using Brazilian data. Our goal is to identify a scalable strategy that allows us to project the Brazilian GDP in real time at any point during the current quarter. In the paper we detail the survey of classical techniques and also of techniques usually known by market practitioners as "machine learning methods". We survey the literature since the first work on estimating business cycles and document the evolution of this literature until the insertion of machine learning methods. Additionally, we perform backtesting exercises, estimate several candidate models for GDP nowcasting. Finally, we evaluate the forecasting power of all models against a naive model and a market expectations model. We demonstrate that a combination of machine learning models based on the distance of forecasts to the average market expectations defeats the fully informed market expectations, while the same is not possible for selected classical nowcasting models. Inter-American Development Bank Lucas Gabriel Martins de Oliveira IDB Publications Brazil Brazil en
institution BID
collection DSpace
country Estados Unidos
countrycode US
component Bibliográfico
access En linea
databasecode dig-bid
tag biblioteca
region America del Norte
libraryname Biblioteca Felipe Herrera del BID
language English
topic Inflation
Potential Output
Output Gap
Macroeconomic Policy
Development Bank
Debtor Finance
Prediction Market
Gross Domestic Product
Economy
Machine Learning
Learning Strategy
Industrial Productivity
Learning
Economic Development
C53 - Forecasting and Prediction Methods • Simulation Methods
C45 - Neural Networks and Related Topics
E17 - Forecasting and Simulation: Models and Applications
Macroeconometrics;machine learning;Forecasting;Nowcasting;GDP;Brazil
Inflation
Potential Output
Output Gap
Macroeconomic Policy
Development Bank
Debtor Finance
Prediction Market
Gross Domestic Product
Economy
Machine Learning
Learning Strategy
Industrial Productivity
Learning
Economic Development
C53 - Forecasting and Prediction Methods • Simulation Methods
C45 - Neural Networks and Related Topics
E17 - Forecasting and Simulation: Models and Applications
Macroeconometrics;machine learning;Forecasting;Nowcasting;GDP;Brazil
spellingShingle Inflation
Potential Output
Output Gap
Macroeconomic Policy
Development Bank
Debtor Finance
Prediction Market
Gross Domestic Product
Economy
Machine Learning
Learning Strategy
Industrial Productivity
Learning
Economic Development
C53 - Forecasting and Prediction Methods • Simulation Methods
C45 - Neural Networks and Related Topics
E17 - Forecasting and Simulation: Models and Applications
Macroeconometrics;machine learning;Forecasting;Nowcasting;GDP;Brazil
Inflation
Potential Output
Output Gap
Macroeconomic Policy
Development Bank
Debtor Finance
Prediction Market
Gross Domestic Product
Economy
Machine Learning
Learning Strategy
Industrial Productivity
Learning
Economic Development
C53 - Forecasting and Prediction Methods • Simulation Methods
C45 - Neural Networks and Related Topics
E17 - Forecasting and Simulation: Models and Applications
Macroeconometrics;machine learning;Forecasting;Nowcasting;GDP;Brazil
Inter-American Development Bank
Which One Predicts Better?: Comparing Different GDP Nowcasting Methods Using Brazilian Data
description The objective of this paper is to develop a basic framework for the implementation of a GDP nowcasting strategy using Brazilian data. Our goal is to identify a scalable strategy that allows us to project the Brazilian GDP in real time at any point during the current quarter. In the paper we detail the survey of classical techniques and also of techniques usually known by market practitioners as "machine learning methods". We survey the literature since the first work on estimating business cycles and document the evolution of this literature until the insertion of machine learning methods. Additionally, we perform backtesting exercises, estimate several candidate models for GDP nowcasting. Finally, we evaluate the forecasting power of all models against a naive model and a market expectations model. We demonstrate that a combination of machine learning models based on the distance of forecasts to the average market expectations defeats the fully informed market expectations, while the same is not possible for selected classical nowcasting models.
author2 Lucas Gabriel Martins de Oliveira
author_facet Lucas Gabriel Martins de Oliveira
Inter-American Development Bank
topic_facet Inflation
Potential Output
Output Gap
Macroeconomic Policy
Development Bank
Debtor Finance
Prediction Market
Gross Domestic Product
Economy
Machine Learning
Learning Strategy
Industrial Productivity
Learning
Economic Development
C53 - Forecasting and Prediction Methods • Simulation Methods
C45 - Neural Networks and Related Topics
E17 - Forecasting and Simulation: Models and Applications
Macroeconometrics;machine learning;Forecasting;Nowcasting;GDP;Brazil
author Inter-American Development Bank
author_sort Inter-American Development Bank
title Which One Predicts Better?: Comparing Different GDP Nowcasting Methods Using Brazilian Data
title_short Which One Predicts Better?: Comparing Different GDP Nowcasting Methods Using Brazilian Data
title_full Which One Predicts Better?: Comparing Different GDP Nowcasting Methods Using Brazilian Data
title_fullStr Which One Predicts Better?: Comparing Different GDP Nowcasting Methods Using Brazilian Data
title_full_unstemmed Which One Predicts Better?: Comparing Different GDP Nowcasting Methods Using Brazilian Data
title_sort which one predicts better?: comparing different gdp nowcasting methods using brazilian data
publisher Inter-American Development Bank
url http://dx.doi.org/10.18235/0005004
https://publications.iadb.org/en/which-one-predicts-better-comparing-different-gdp-nowcasting-methods-using-brazilian-data
work_keys_str_mv AT interamericandevelopmentbank whichonepredictsbettercomparingdifferentgdpnowcastingmethodsusingbraziliandata
_version_ 1809108439513694208