Macroeconomic forecasting using factor models and machine learning: an application to Japan

Maehashi, Kohei; Shintani, Mototsugu

doi:10.1016/j.jjie.2020.101104

Cited by 29 publications

(12 citation statements)

References 36 publications

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“…The success of machine learning stemmed from the generalization approach based on regression trees. As a result, factor models and the machine learning approach were suitable for macroeconomic forecasts such as the interest rate (Maehashi & Shintani, 2020).…”

Section: Literaturementioning

confidence: 99%

Data mining application with machine learning algorithms to manage interest rate risk

KOÇOĞLU

Ersöz²

2022

bmij

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In trade, the risks taken may increase the expected income; however, they may also cause large amounts of losses as well. Banks transfer the capital and the deposits they collect from their clients to the individuals or institutions in need of profit, taking certain risks into account. One of the important risks taken in this process of capital transfer is the market's change in interest or profit share rates. If the bank transfers the deposit collected with a certain commitment to the market at a lower rate, it will make a loss. Models for predicting future interest or profit share rates gain importance for preventing this situation. The aim of this study is to determine which variables will be taken into account for the loan interest rate that banks will offer to their customers during the lending process, and to create a machine learning model that can predict the loan interest rate that the bank will offer to its customers by using these variables. Multiple Linear Regression analysis was performed to demonstrate the relationship between the variables selected based on the literature review, expert opinions, and the interest rate. In order to facilitate decision-makers in practice, Random Forests, Decision Trees, K-Nearest Neighbours (KNN), Artificial Neural Networks (ANN), and Support Vector Machine (SVM) algorithms from machine learning algorithms were compared by using the prediction model. Accuracy Rate, Cohen's Kappa, Precision, Sensitivity, and F-Measure measurements were used to compare the algorithms used in the study. According to the analysis results, it was observed that the Random Forest algorithm was more successful on the first model consisting of weekly data. The Decision Tree algorithm succeeded more on the second model consisting of monthly data prediction performance. In the model consisting of weekly data, USD Selling Price, Stock Index (BIST100), and Central Bank Gold Reserve from the Multiple Linear Regression variables were found significant in affecting the interest rate.

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Section: Literaturementioning

confidence: 99%

Data mining application with machine learning algorithms to manage interest rate risk

KOÇOĞLU

Ersöz²

2022

bmij

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“…To exploit non-traditional and large-scale data sources, researchers have recently begun utilizing ML models for economic nowcasting (Richardson et al 2020;Maehashi and Shintani 2020;Chapman and Desai 2020). ML models have been shown to handle wide-and large-scale data efficiently and can manage collinearity.…”

Section: Machine Learning Models For Nowcastingmentioning

confidence: 99%

“…econometricians have begun using ML models for macroeconomic nowcasting (Chakraborty and Joseph 2017;Richardson et al 2020;Maehashi and Shintani 2020;Chapman and Desai 2020). The cited articles suggest that ML models complement traditional econometric tools and are useful in extracting economic value from non-traditional data sources.…”

Section: Introductionmentioning

confidence: 99%

Macroeconomic Predictions using Payments Data and Machine Learning

Chapman

Desai

2021

SSRN Journal

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“…Some of them are [9][10][11][12][13][14][15][16]. Similarly, many researchers have evaluated the penalization techniques under time series set up such as Mol et al [17], Inoue and Kilian [18], Bai and Ng [19], Kim and Swanson [20,21], Luciani [22], Swanson and Xiong [8,23], Swanson et al [24]; and Maehashi and Shintani [25].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Autometrics and Penalization Techniques under Various Error Distributions: Evidence from Monte Carlo Simulation

et al. 2021

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This work compares Autometrics with dual penalization techniques such as minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) under asymmetric error distributions such as exponential, gamma, and Frechet with varying sample sizes as well as predictors. Comprehensive simulations, based on a wide variety of scenarios, reveal that the methods considered show improved performance for increased sample size. In the case of low multicollinearity, these methods show good performance in terms of potency, but in gauge, shrinkage methods collapse, and higher gauge leads to overspecification of the models. High levels of multicollinearity adversely affect the performance of Autometrics. In contrast, shrinkage methods are robust in presence of high multicollinearity in terms of potency, but they tend to select a massive set of irrelevant variables. Moreover, we find that expanding the data mitigates the adverse impact of high multicollinearity on Autometrics rapidly and gradually corrects the gauge of shrinkage methods. For empirical application, we take the gold prices data spanning from 1981 to 2020. While comparing the forecasting performance of all selected methods, we divide the data into two parts: data over 1981–2010 are taken as training data, and those over 2011–2020 are used as testing data. All methods are trained for the training data and then are assessed for performance through the testing data. Based on a root-mean-square error and mean absolute error, Autometrics remain the best in capturing the gold prices trend and producing better forecasts than MCP and SCAD.

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Macroeconomic forecasting using factor models and machine learning: an application to Japan

Cited by 29 publications

References 36 publications

Data mining application with machine learning algorithms to manage interest rate risk

Data mining application with machine learning algorithms to manage interest rate risk

Macroeconomic Predictions using Payments Data and Machine Learning

A Comparison of Autometrics and Penalization Techniques under Various Error Distributions: Evidence from Monte Carlo Simulation

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