Benchmarking Econometric and Machine Learning Methodologies in Nowcasting

Hopp, Daniel

doi:10.2139/ssrn.4101997

Cited by 2 publications

(2 citation statements)

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“…They found that the ensembled forecast method was superior to the three methods individually, and each individual model outperformed the benchmark Dynamic Factor Model. Hopp (2022) provides comparative results for nowcasting US GDP growth across three different volatile periods in US economic history. It examines the performance of 12 different methodologies including all the methods most commonly employed in nowcasting, as well as some of the most popular traditional machine learning approaches.…”

Section: Nowcasting Gdp Using Machine Learningmentioning

confidence: 99%

Nowcasting GDP with Machine Learning: The Case of Indonesia

Utama,

Firinda

2024

Preprint

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This paper develops robust economic models for nowcasting Indonesia's GDP growth. Utilizing a comprehensive dataset comprising monthly economic and finance indicators from 2013 to 2023, we explore their association with GDP growth. The study period spans from January 2013 to December 2022 for in-sample data, with an extension to September 2023 for out-sample analysis. We employ a suite of machine learning models - Elastic Net, Random Forest, XGBoost, and SVM - selected for their complementary strengths. The models are trained and tested, yielding an RMSE range of 0.01 to 0.41 for the training period and a narrower range of 0.11 to 0.17 for the testing period, indicating robust performance across different economic conditions, including normal and pandemic periods. A key aspect of our study is the model interpretation, which includes decomposition and Shapley value analysis, providing insights into the driving factors behind GDP fluctuations. Our findings highlight the significant impact of several indicators on Indonesia's GDP. Additionally, our nowcasting exercise for Q4 2023 projects an average GDP growth of 5.13%, ranging from 5.01–5.26%. JEL classification: C53, C63, E37, E58, O40

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Section: Nowcasting Gdp Using Machine Learningmentioning

confidence: 99%

Nowcasting GDP with Machine Learning: The Case of Indonesia

Utama,

Firinda

2024

Preprint

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“…Machine Learning algorithms have now become a valuable tool in economic modelling, demonstrating remarkable efficacy in the challenging task of nowcasting and forecasting GDP across diverse global contexts. This effectiveness is evident in advanced economies (e.g., Canada [54], China [67,69], Finland [26], Italy [21], Netherlands [42], New Zealand [55,56,60], South Africa [17], Sweden [40], USA [31,45], multiple European countries [23]), emerging markets and developing countries (e.g., Albania [66], Bangladesh [32], Belize and El Savador [5], Brazil [57], Egypt [1], Georgia [46], India [28], Indonesia [62], Lebanon [64], Malaysia [38], Peru [63])). Moreover, Machine learning algorithms are also proved to be very competitive with respect to standard econometric methods.…”

Section: Introductionmentioning

confidence: 99%

Nowcasting Madagascar's real GDP using machine learning algorithms

Ramaharo,

Rasolofomanana

2023

Preprint

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We investigate the predictive power of different machine learning algorithms to nowcast Madagascar's gross domestic product (GDP). We trained popular regression models, including linear regularized regression (Ridge, Lasso, Elastic-net), dimensionality reduction model (principal component regression), k-nearest neighbors algorithm (k-NN regression), support vector regression (linear SVR), and tree-based ensemble models (Random forest and XGBoost regressions), on 10 Malagasy quarterly macroeconomic leading indicators over the period 2007Q1-2022Q4, and we used simple econometric models as a benchmark. We measured the nowcast accuracy of each model by calculating the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Our findings reveal that the Ensemble Model, formed by aggregating individual predictions, consistently outperforms traditional econometric models. We conclude that machine learning models can deliver more accurate and timely nowcasts of Malagasy economic performance and provide policymakers with additional guidance for data-driven decision making.

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Benchmarking Econometric and Machine Learning Methodologies in Nowcasting

Cited by 2 publications

References 0 publications

Nowcasting GDP with Machine Learning: The Case of Indonesia

Nowcasting GDP with Machine Learning: The Case of Indonesia

Nowcasting Madagascar's real GDP using machine learning algorithms

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