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Forecasting economic activity using a neural network in uncertain times : Monte Carlo evidence and application to the German GDP

Holtemöller, Oliver; Kozyrev, Boris - Leibniz-Institut für Wirtschaftsforschung Halle - 2024

). We provide evidence from Monte Carlo simulations for the relative forecast performance of GRNN depending on the data … GRNN to forecast quarterly German GDP growth by extending univariate GRNN to multivariate and mixed-frequency settings. We … such as during the COVID-19 recession and recovery. GRNN was superior in terms of root mean forecast errors compared to an …

Persistent link: https://www.econbiz.de/10014496850

Parameter-efficient deep probabilistic forecasting

Sprangers, Olivier; Schelter, Sebastian; Rijke, Maarten de - In: International journal of forecasting 39 (2023) 1, pp. 332-345

Persistent link: https://www.econbiz.de/10014462783

Comparing out-of-sample performance of machine learning methods to forecast U.S. GDP growth

Chu, Ba; Qureshi, Shafiullah - In: Computational economics 62 (2023) 4, pp. 1567-1609

Persistent link: https://www.econbiz.de/10014437505

‘Seeing’ the Future : Improving Macroeconomic Forecasts with Spatial Data Using Recurrent Convolutional Neural Networks

Leslie, Jonathan - 2023

I evaluate whether incorporating sub-national trends improves macroeconomic fore-casting accuracy in a deep machine learning framework. Speciﬁcally, I adopt a computer vision setting by transforming U.S. economic data into a ‘video’ series of geographic ‘images’ and utilizing a...

Persistent link: https://www.econbiz.de/10014256632

Using neural networks to predict microspatial economic growth

Khachiyan, Arman; Thomas, Anthony; Zhou, Huye; Hanson, … - In: American Economic Review Insights 4 (2022) 4, pp. 491-506

Persistent link: https://www.econbiz.de/10014230258

Benchmarking econometric and machine learning methodologies in nowcasting GDP

Hopp, Daniel - In: Empirical economics : a quarterly journal of the … 66 (2024) 5, pp. 2191-2247

Persistent link: https://www.econbiz.de/10014520141

Forecasting Dutch GDP and inflation using alternative factor model specifications based on large and small datasets

Reijer, Ard H. J. den - In: Empirical economics : a journal of the Institute for … 44 (2013) 2, pp. 435-453

Persistent link: https://www.econbiz.de/10009724186

Forecasting dutch GDP using large scale factor models

Reijer, Ard H. J. den - 2005

Persistent link: https://www.econbiz.de/10002850181

The Oxford handbook of economic forecasting

Clements, Michael P. (ed.); Hendry, David F. (ed.) - 2011

Greater data availability has been coupled with developments in statistical theory and economic theory to allow more elaborate and complicated models to be entertained. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models.

Persistent link: https://www.econbiz.de/10012669772

A large Bayesian VAR of the United States Economy

Crump, Richard K.; Eusepi, Stefano; Giannone, Domenico; … - 2021

We model the United States macroeconomic and financial sectors using a formal and unified econometric model. Through shrinkage, our Bayesian VAR provides a flexible framework for modeling the dynamics of thirty-one variables, many of which are tracked by the Federal Reserve. We show how the...

Persistent link: https://www.econbiz.de/10012613922