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on Forecasting |
| By: | Yang Liu; Ran Pan; Rui Xu |
| Abstract: | Forecasting inflation has become a major challenge for central banks since 2020, due to supply chain disruptions and economic uncertainty post-pandemic. Machine learning models can improve forecasting performance by incorporating a wider range of variables, allowing for non-linear relationships, and focusing on out-of-sample performance. In this paper, we apply machine learning (ML) models to forecast near-term core inflation in Japan post-pandemic. Japan is a challenging case, because inflation had been muted until 2022 and has now risen to a level not seen in four decades. Four machine learning models are applied to a large set of predictors alongside two benchmark models. For 2023, the two penalized regression models systematically outperform the benchmark models, with LASSO providing the most accurate forecast. Useful predictors of inflation post-2022 include household inflation expectations, inbound tourism, exchange rates, and the output gap. |
| Keywords: | Core inflation; forecasting; machine learning models; LASSO; Japan |
| Date: | 2024–09–27 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2024/206 |
| By: | Zongwu Cai (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA); Gunawan (Faculty of Economics and Business, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia); Yuying Sun (Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China) |
| Abstract: | This paper proposes a new nonparametric forecasting procedure based on a weighted local linear estimator for a nonparametric model with structural breaks. The proposed method assigns a weight based on both the distance of observations to the predictor covariates and their location in time and the weight is chosen using multifold forward-validation to account for time series data. We investigate the asymptotic properties of the proposed estimator and show that the weight estimated by the multifold forward-validation is asymptotically optimal in the sense of achieving the lowest possible out-of-sample prediction risk. Additionally, a nonparametric method is adopted to estimate the break date and the proposed approach allows for different features of predictors before and after break. A Monte Carlo simulation study is conducted to provide evidence for the forecasting outperformance of the proposed method over the regular nonparametric post-break and full-sample estimators. Finally, an empirical application to volatility forecasting compares several popular parametric and nonparametric methods, including the proposed weighted local linear estimator, demonstrating its superiority over other alternative methods. |
| Keywords: | Combination Forecasting; Model Averaging; multifold forward-validation; Nonparametric Model; Structural Break Model; Weighted Local Linear Fitting |
| JEL: | C14 C22 C53 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:kan:wpaper:202412 |
| By: | Ali Mehrabani (Department of Economics, University of Kansas, Lawrence, KS 66045); Shahnaz Parsaeian (Department of Economics, University of Kansas, Lawrence, KS 66045); Aman Ullah (Department of Economics, University of California at Riverside, CA 92521) |
| Abstract: | This paper introduces a Stein-like shrinkage method for estimating slope coefficients and forecasting in first order dynamic regression models under structural breaks. The model allows for unit root and non-stationary regressors. The proposed shrinkage estimator is a weighted average of a restricted estimator that ignores the break in the slope coefficients, and an unrestricted estimator that uses the observations within each regime. The restricted estimator is the most efficient estimator but inconsistent when there is a break. However, the unrestricted estimator is consistent but not efficient. Therefore, the proposed shrinkage estimator balances the trade-off between the bias and variance efficiency of the restricted estimator. The averaging weight is proportional to the weighted distance of the restricted estimator, and the unrestricted estimator. We derive the analytical large-sample approximation of the bias, mean squared error, and risk for the shrinkage estimator, the unrestricted estimator, and the restricted estimator. We show that the risk of the shrinkage estimator is lower than the risk of the unrestricted estimator under any break size and break points. Moreover, we extend the results for the model with a unit root and non-stationary regressors. We evaluate the finite sample performance of our proposed method via extensive simulation study, and empirically in forecasting output growth |
| Keywords: | ARX-model; Asymptotic approximation; Dynamic regressions; Forecasting; Moment approximation; Non-stationary regressors; Structural breaks; Unit root. |
| JEL: | C13 C22 C53 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:kan:wpaper:202410 |
| By: | Theophilus G. Baidoo; Ashley Obeng |
| Abstract: | Inflation remains a persistent challenge for many African countries. This research investigates the critical role of machine learning (ML) in understanding and managing inflation in Ghana, emphasizing its significance for the country's economic stability and growth. Utilizing a comprehensive dataset spanning from 2010 to 2022, the study aims to employ advanced ML models, particularly those adept in time series forecasting, to predict future inflation trends. The methodology is designed to provide accurate and reliable inflation forecasts, offering valuable insights for policymakers and advocating for a shift towards data-driven approaches in economic decision-making. This study aims to significantly advance the academic field of economic analysis by applying machine learning (ML) and offering practical guidance for integrating advanced technological tools into economic governance, ultimately demonstrating ML's potential to enhance Ghana's economic resilience and support sustainable development through effective inflation management. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2410.05630 |
| By: | Liu, Yirui; Qiao, Xinghao; Pei, Yulong; Wang, Liying |
| Abstract: | This paper introduces the Deep Functional Factor Model (DF2M), a Bayesian nonparametric model designed for analysis of high-dimensional functional time series. DF2M is built upon the Indian Buffet Process and the multi-task Gaussian Process, incorporating a deep kernel function that captures non-Markovian and nonlinear temporal dynamics. Unlike many black-box deep learning models, DF2M offers an explainable approach to utilizing neural networks by constructing a factor model and integrating deep neural networks within the kernel function. Additionally, we develop a computationally efficient variational inference algorithm to infer DF2M. Empirical results from four real-world datasets demonstrate that DF2M provides better explainability and superior predictive accuracy compared to conventional deep learning models for high-dimensional functional time series. |
| JEL: | C1 |
| Date: | 2024–07–21 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:125587 |
| By: | Allen, Sam (ETH Zurich); Koh, Jonathan (University of Bern); Segers, Johan (Université catholique de Louvain, LIDAM/ISBA, Belgium); Ziegel, Johanna (ETH Zurich) |
| Abstract: | Probabilistic forecasts comprehensively describe the uncertainty in the unknown future outcome, making them essential for decision making and risk management. While several methods have been introduced to evaluate probabilistic forecasts, existing evaluation techniques are ill-suited to the evaluation of tail properties of such forecasts. However, these tail properties are often of particular interest to forecast users due to the severe impacts caused by extreme outcomes. In this work, we introduce a general notion of tail calibration for probabilistic forecasts, which allows forecasters to assess the reliability of their predictions for extreme outcomes. We study the relationships between tail calibration and standard notions of forecast calibration, and discuss connections to peaks-over-threshold models in extreme value theory. Diagnostic tools are introduced and applied in a case study on European precipitation forecasts. |
| Keywords: | Extreme event ; proper scoring rule ; forecast evaluation ; tail calibration diagnostic plot ; precipitation forecast |
| Date: | 2024–07–04 |
| URL: | https://d.repec.org/n?u=RePEc:aiz:louvad:2024018 |