Combining Model‐Based Near‐Term GDP Forecasts and Judgmental Forecasts: A Real‐Time Exercise for the G7 Countries

Jansen, W. Jos; Winter, Jasper M. de

doi:10.1111/obes.12250

Cited by 5 publications

(1 citation statement)

References 66 publications

(186 reference statements)

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“…Economists and policy makers rely on a wide range of macroeconomic indicators to guide decisions and social policies that impact the economies of the world [57]. Among macroeconomic indicators, gross domestic product (GDP) is the most widely used and well-known.…”

Section: Introductionmentioning

confidence: 99%

Recursive deep learning framework for forecasting the decadal world economic outlook

Wang¹,

Beard²,

Hawkins³

et al. 2023

Preprint

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Gross domestic product (GDP) is the most widely used indicator in macroeconomics and the main tool for measuring a country's economic ouput. Due to the diversity and complexity of the world economy, a wide range of models have been used, but there are challenges in making decadal GDP forecasts given unexpected changes such as pandemics and wars. Deep learning models are well suited for modeling temporal sequences have been applied for time series forecasting. In this paper, we develop a deep learning framework to forecast the GDP growth rate of the world economy over a decade. We use Penn World Table as the source of our data, taking data from 1980 to 2019, across 13 countries, such as Australia, China, India, the United States and so on. We test multiple deep learning models, LSTM, BD-LSTM, ED-LSTM and CNN, and compared their results with the traditional time series model (ARIMA,VAR). Our results indicate that ED-LSTM is the best performing model. We present a recursive deep learning framework to predict the GDP growth rate in the next ten years. We predict that most countries will experience economic growth slowdown, stagnation or even recession within five years; only China, France and India are predicted to experience stable, or increasing, GDP growth.

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Section: Introductionmentioning

confidence: 99%

Recursive deep learning framework for forecasting the decadal world economic outlook

Wang¹,

Beard²,

Hawkins³

et al. 2023

Preprint

View full text Add to dashboard Cite

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Incorporating judgment in forecasting models in times of crisis

Franses

2024

Futures & Foresight Science

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This paper introduces a simple and reproducible method to modify model forecasts using expert forecasts which is useful in crisis times. The idea is to add the expert forecast as an additional observation of the dependent variable, and to extend the model with an additional explanatory variable such as the square of a deterministic trend. Next, the new model forecast is combined with the expert forecast using equal weights. We show that it works well for gross domestic product growth forecasts for 2020 for 12 countries and that it improves upon an equal‐weighted combination of the original model forecast and expert forecast.

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Enhancing the accuracy and precision of forecasting the productivity of a factory: a fuzzified feedforward neural network approach

Chen

Lin

2021

Complex Intell. Syst.

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Most existing methods for forecasting the productivity of a factory cannot estimate the range of productivity reliably, especially when future conditions are distinct from those in the past. To address this issue, a fuzzified feedforward neural network (FFNN) approach is proposed in this study. The FFNN approach improves the forecasting precision after generating accurate fuzzy productivity forecasts. In addition, the acceptable range of a fuzzy productivity forecast is specified, based on which the sum of the memberships of actual values is maximized. In this way, the range of productivity can be precisely estimated. After applying the FFNN approach to a real case, the experimental results revealed the superiority of the FFNN approach by improving the forecasting precision, in terms of the hit rate, by 25%. Such an improvement also contributed to a better forecasting accuracy. The superiority of the FFNN approach is in the context that the accuracy of forecasting productivity is optimized only after the range of productivity has been precisely estimated. In contrast, most state-of-the-art methods focus on optimizing the forecasting accuracy, but may be ineffective without information about the range of productivity when future conditions are distinct from the past.

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Combining Model‐Based Near‐Term GDP Forecasts and Judgmental Forecasts: A Real‐Time Exercise for the G7 Countries

Cited by 5 publications

References 66 publications

Recursive deep learning framework for forecasting the decadal world economic outlook

Recursive deep learning framework for forecasting the decadal world economic outlook

Incorporating judgment in forecasting models in times of crisis

Enhancing the accuracy and precision of forecasting the productivity of a factory: a fuzzified feedforward neural network approach

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