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on Forecasting |
| By: | Markus Bibinger (Faculty of Mathematics and Computer Science, Institute of Mathematics, University of Würzburg); Jun Yu (Faculty of Business Administration, University of Macau); Chen Zhang (Faculty of Business Administration, University of Macau) |
| Abstract: | A multivariate fractional Brownian motion (mfBm) with component-wise Hurst exponents is used to model and forecast realized volatility. We investigate the interplay between correlation coefficients and Hurst exponents and propose a novel estimation method for all model parameters, establishing consistency and asymptotic normality of the estimators. Additionally, we develop a time-reversibility test, which is typically not rejected by real volatility data. When the data-generating process is a time-reversible mfBm, we derive optimal forecasting formulae and analyze their properties. A key insight is that an mfBm with different Hurst exponents and non-zero correlations can reduce forecasting errors compared to a one-dimensional model. Consistent with optimal forecasting theory, out-of-sample forecasts using the time-reversible mfBm show improvements over univariate fBm, particularly when the estimated Hurst exponents differ significantly. Empirical results demonstrate that mfBm-based forecasts outperform the (vector) HAR model. |
| Keywords: | Forecasting, Hurst exponent, multivariate fractional Brownian motion, realized volatility, rough volatility |
| JEL: | C12 C58 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:boa:wpaper:202528 |
| By: | Simon Hirsch |
| Abstract: | Probabilistic electricity price forecasting (PEPF) is a key task for market participants in short-term electricity markets. The increasing availability of high-frequency data and the need for real-time decision-making in energy markets require online estimation methods for efficient model updating. We present an online, multivariate, regularized distributional regression model, allowing for the modeling of all distribution parameters conditional on explanatory variables. Our approach is based on the combination of the multivariate distributional regression and an efficient online learning algorithm based on online coordinate descent for LASSO-type regularization. Additionally, we propose to regularize the estimation along a path of increasingly complex dependence structures of the multivariate distribution, allowing for parsimonious estimation and early stopping. We validate our approach through one of the first forecasting studies focusing on multivariate probabilistic forecasting in the German day-ahead electricity market while using only online estimation methods. We compare our approach to online LASSO-ARX-models with adaptive marginal distribution and to online univariate distributional models combined with an adaptive Copula. We show that the multivariate distributional regression, which allows modeling all distribution parameters - including the mean and the dependence structure - conditional on explanatory variables such as renewable in-feed or past prices provide superior forecasting performance compared to modeling of the marginals only and keeping a static/unconditional dependence structure. Additionally, online estimation yields a speed-up by a factor of 80 to over 400 times compared to batch fitting. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.02518 |
| By: | Anindya Sarkar; G. Vadivu |
| Abstract: | This research proposes a cutting-edge ensemble deep learning framework for stock price prediction by combining three advanced neural network architectures: The particular areas of interest for the research include but are not limited to: Variational Autoencoder (VAE), Transformer, and Long Short-Term Memory (LSTM) networks. The presented framework is aimed to substantially utilize the advantages of each model which would allow for achieving the identification of both linear and non-linear relations in stock price movements. To improve the accuracy of its predictions it uses rich set of technical indicators and it scales its predictors based on the current market situation. By trying out the framework on several stock data sets, and benchmarking the results against single models and conventional forecasting, the ensemble method exhibits consistently high accuracy and reliability. The VAE is able to learn linear representation on high-dimensional data while the Transformer outstandingly perform in recognizing long-term patterns on the stock price data. LSTM, based on its characteristics of being a model that can deal with sequences, brings additional improvements to the given framework, especially regarding temporal dynamics and fluctuations. Combined, these components provide exceptional directional performance and a very small disparity in the predicted results. The present solution has given a probable concept that can handle the inherent problem of stock price prediction with high reliability and scalability. Compared to the performance of individual proposals based on the neural network, as well as classical methods, the proposed ensemble framework demonstrates the advantages of combining different architectures. It has a very important application in algorithmic trading, risk analysis, and control and decision-making for finance professions and scholars. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.22192 |
| By: | Shovon Sengupta; Bhanu Pratap; Amit Pawar |
| Abstract: | The conventional linear Phillips curve model, while widely used in policymaking, often struggles to deliver accurate forecasts in the presence of structural breaks and inherent nonlinearities. This paper addresses these limitations by leveraging machine learning methods within a New Keynesian Phillips Curve framework to forecast and explain headline inflation in India, a major emerging economy. Our analysis demonstrates that machine learning-based approaches significantly outperform standard linear models in forecasting accuracy. Moreover, by employing explainable machine learning techniques, we reveal that the Phillips curve relationship in India is highly nonlinear, characterized by thresholds and interaction effects among key variables. Headline inflation is primarily driven by inflation expectations, followed by past inflation and the output gap, while supply shocks, except rainfall, exert only a marginal influence. These findings highlight the ability of machine learning models to improve forecast accuracy and uncover complex, nonlinear dynamics in inflation data, offering valuable insights for policymakers. |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2504.05350 |
| By: | Pangallo, Marco; Lafond, François; Farmer, J. Doyne; Wiese, Samuel; Muellbauer, John; Moran, José; Dyer, Joel; Kaszowska-Mojsa, Jagoda (Institute for New Economic Thinking, University of Oxford); Calinescu, Anisoara (Institute for New Economic Thinking, University of Oxford) |
| Abstract: | In the last few years, economic agent-based models have made the transition from qualitative models calibrated to match stylised facts to quantitative models for time series forecasting, and in some cases, their predictions have performed as well or better than those of standard models (see, e.g. Poledna et al. (2023a); Hommes et al. (2022); Pichler et al. (2022)). Here, we build on the model of Poledna et al., adding several new features such as housing markets, realistic synthetic populations of individuals with income, wealth and consumption heterogeneity, enhanced behavioural rules and market mechanisms, and an enhanced credit market. We calibrate our model for all 38 OECD member countries using state-of-the-art approximate Bayesian inference methods and test it by making out-of-sample forecasts. It outperforms both the Poledna and AR(1) time series models by a highly statistically significant margin. Our model is built within a platform we have developed, making it easy to build, run, and evaluate alternative models, which we hope will encourage future work in this area. |
| Keywords: | Agent-based models, Bayesian estimation, Economic forecasting |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:amz:wpaper:2024-06 |
| By: | Tom L. Dudda; Lars Hornuf |
| Abstract: | We examine predictive machine learning studies from 50 top business and economic journals published between 2010 and 2023. We investigate their transparency regarding the predictive performance of machine learning models compared to less complex traditional statistical models that require fewer resources in terms of time and energy. We find that the adoption of machine learning varies by discipline, and is most frequently used in information systems, marketing, and operations research journals. Our analysis also reveals that 28% of studies do not benchmark the predictive performance of machine learning models against traditional statistical models. These studies receive fewer citations, arguably due to a less rigorous analysis. Studies including traditional statistical models as benchmarks typically report high outperformance for the best machine learning model. However, the performance improvement is substantially lower for the average reported machine learning model. We contend that, due to opaque reporting practices, it often remains unclear whether the predictive gains justify the increased costs of more complex models. We advocate for standardized, transparent model reporting that relates predictive gains to the efficiency of machine learning models compared to less-costly traditional statistical models. |
| Keywords: | machine learning, predictive modelling, transparent model reporting |
| JEL: | C18 C40 C52 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11721 |
| By: | Adam Daigneault; Jonathan Gendron |
| Abstract: | Several key states in various regions have experienced recent sawtimber as well as pulp and paper mill closures, which have resulted in harmful effects to rural, natural-resource dependent communities. This raises an important research question, how will key macroeconomic and related variables for the U.S. forest sector change in the future for highly forest-dependent states? To address this, we employ a vector error correction (VEC) model to forecast economic trends in three major industries - forestry and logging, wood manufacturing, and paper manufacturing - across six of the most forest-dependent states in the U.S.: Alabama, Arkansas, Maine, Mississippi, Oregon, and Wisconsin. The forecasting results imply that the forestry and logging industry will largely experience decreases in employment and the number of firms. Wood manufacturing has similar findings, but employment is forecasted to increase in general. Paper manufacturing is forecasted to decrease employment, output, and the number of firms, while wages will remain constant. The analysis highlights how timber-based manufacturing communities may be more resilient than other forestry-based industries in the face of economic disruptions. This type of regional forecasting provides valuable insights for regional policy makers and industry stakeholders, helping them anticipate economic shifts and implement strategies to support affected communities. In addition, the methodology applied in this study can be extended to other non-forestry industries that serve as economic pillars for specific regions such as mining, agriculture, and energy production, offering a framework for assessing economic resilience in resource-dependent communities. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.23569 |
| By: | Nicola Borri; Denis Chetverikov; Yukun Liu; Aleh Tsyvinski |
| Abstract: | We show that the higher-orders and their interactions of the common sparse linear factors can effectively subsume the factor zoo. To this extend, we propose a forward selection Fama-MacBeth procedure as a method to estimate a high-dimensional stochastic discount factor model, isolating the most relevant higher-order factors. Applying this approach to terms derived from six widely used factors (the Fama-French five-factor model and the momentum factor), we show that the resulting higher-order model with only a small number of selected higher-order terms significantly outperforms traditional benchmarks both in-sample and out-of-sample. Moreover, it effectively subsumes a majority of the factors from the extensive factor zoo, suggesting that the pricing power of most zoo factors is attributable to their exposure to higher-order terms of common linear factors. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.23501 |
| By: | Marie-Pierre Dargnies (University of Paris Dauphine); Rustamdjan Hakimov (University of Lausanne); Dorothea Kübler (WZB Berlin, Technische Universität Berlin, CES Ifo) |
| Abstract: | The adoption of Artificial Intelligence (AI) for hiring processes is often impeded by a scarcity of comprehensive employee data. We hypothesize that the inclusion of behavioral measures elicited from applicants can enhance the predictive accuracy of AI in hiring. We study this hypothesis in the context of microfinance loan officers. Our findings suggest that survey-based behavioral measures markedly improve the predictions of a random-forest algorithm trained to predict productivity within sample relative to demographic information alone. We then validate the algorithm’s robustness to the selectivity of the training sample and potential strategic responses by applicants by running two out-of-sample tests: one forecasting the future performance of novice employees, and another with a field experiment on hiring. Both tests corroborate the effectiveness of incorporating behavioral data to predict performance. The comparison of workers hired by the algorithm with those hired by human managers in the field experiment reveals that algorithmic hiring is marginally more efficient than managerial hiring. |
| Keywords: | hiring; ai; economic and behavioral measures; selective labels; |
| Date: | 2025–04–29 |
| URL: | https://d.repec.org/n?u=RePEc:rco:dpaper:532 |
| By: | Juan Tenorio; Heidi Alpiste; Jakelin Rem\'on; Arian Segil |
| Abstract: | In recent years, the use of databases that analyze trends, sentiments or news to make economic projections or create indicators has gained significant popularity, particularly with the Google Trends platform. This article explores the potential of Google search data to develop a new index that improves economic forecasts, with a particular focus on one of the key components of economic activity: private consumption (64\% of GDP in Peru). By selecting and estimating categorized variables, machine learning techniques are applied, demonstrating that Google data can identify patterns to generate a leading indicator in real time and improve the accuracy of forecasts. Finally, the results show that Google's "Food" and "Tourism" categories significantly reduce projection errors, highlighting the importance of using this information in a segmented manner to improve macroeconomic forecasts. |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2503.21981 |
| By: | Abajian, Alexander; Carleton, Tamma; Meng, Kyle; Deschênes, Olivier |
| Abstract: | Many behavioral responses to climate change are carbon-intensive, raising concerns that adaptation may cause additional warming. The sign and magnitude of this feedback depend on how increased emissions from cooling balance against reduced emissions from heating across space and time. We present an empirical approach that forecasts the effect of future adaptive energy use on global average temperature over the 21st century. We estimate that energy-based adaptation will lower global mean surface temperature in 2099 by 0.07 to 0.12 °C relative to baseline projections under Representative Concentration Pathways 4.5 and 8.5. This cooling avoids 0.6 to 1.8 trillion U.S. Dollars ($2019) in damages, depending on the baseline emissions scenario. Energy-based adaptation lowers business-as-usual emissions for 85% of countries, reducing the mitigation required to meet their unilateral Nationally Determined Contributions by 20% on average. These findings indicate that while business-as-usual adaptive energy use is unlikely to accelerate warming, it raises important implications for countries existing mitigation commitments. |
| Date: | 2025–04–01 |
| URL: | https://d.repec.org/n?u=RePEc:cdl:agrebk:qt9642j569 |
| By: | DiGiuseppe, Matthew; Garriga, Ana Carolina; Kern, Andreas |
| Abstract: | How does partisanship affect inflation expectations? While most research focuses on how inflation impacts political approval and voter behavior, we analyze the political roots of inflation expectations. We argue that elections serve as key moments when citizens update their economic outlook based on anticipated policy changes, and that partisanship influences these re-evaluations. Using a two-wave panel survey conducted before and after the 2024 U.S. Presidential Election, we show that partisan alignment strongly shapes inflation expectations. Democrats reported heightened inflation expectations, anticipating inflationary policies under a Trump administration, while Republicans expected inflation to fall. These shifts reflect partisan interpretations of economic policy rather than objective forecasts. We also analyze the characteristics of those who are more likely to update inflation expectations and in what direction. Importantly, we verify that individuals with strong partisan attitudes exhibit less anchored inflation expectations. Our findings have implications beyond the case under analysis. From a policy perspective, our results underscore the challenges central banks face in anchoring inflation expectations in an era of political polarization, where economic perceptions differ sharply across partisanship lines. |
| Keywords: | Inflation expectations, Survey data, Partisanship, United States, Polarization |
| JEL: | D83 E03 E31 E58 Y80 |
| Date: | 2025–04–09 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:124391 |