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on Forecasting |
| By: | Sérgio Cruz |
| Abstract: | This paper analyzes electricity consumption dynamics in Portugal between 2012 8 and 2024, a period marked by unprecedented energy transition, the COVID-19 pan- 9 demic, and the 20212022 energy crisis. Using three complementary Vector Autore- 10 gressive approaches, the analysis compares forecast performance and identies the 11 main drivers of electricity consumption amid the rapid expansion of renewable capa- 12 city. All models achieve good forecasting accuracy, and impulse response functions 13 indicate that GDP shocks have signicant and persistent eects on electricity con- 14 sumption, with long-run cumulative eects ranging from 0.27 to 0.49. At the same 15 time, renewable capacity penetration shows ambiguous short-run eects, and con- 16 sumption remains highly price-inelastic. The results suggest that economic forecasts 17 will remain central to electricity system planning. However, declining elasticities 18 suggest a gradual decoupling between consumption and economic growth, driven by 19 energy transition factors such as energy eciency and self-consumption. |
| Keywords: | electricity consumption, income, renewable capacity, forecasting, VAR. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:ise:remwps:wp04062026 |
| By: | Kim Christensen; Mathias Siggaard; Bezirgen Veliyev |
| Abstract: | We inspect how accurate machine learning (ML) is at forecasting realized variance of the Dow Jones Industrial Average index constituents. We compare several ML algorithms, including regularization, regression trees, and neural networks, to multiple Heterogeneous AutoRegressive (HAR) models. ML is implemented with minimal hyperparameter tuning. In spite of this, ML is competitive and beats the HAR lineage, even when the only predictors are the daily, weekly, and monthly lags of realized variance. The forecast gains are more pronounced at longer horizons. We attribute this to higher persistence in the ML models, which helps to approximate the long-memory of realized variance. ML also excels at locating incremental information about future volatility from additional predictors. Lastly, we propose a ML measure of variable importance based on accumulated local effects. This shows that while there is agreement about the most important predictors, there is disagreement on their ranking, helping to reconcile our results. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.13014 |
| By: | Li, Junkan; Tsiboe, Francis |
| Abstract: | Accurate county-level crop yield prediction is essential for agricultural outlooks and risk management, yet the predictive value of complex models remains uncertain. This study conducts a horse race comparison of alternative yield prediction methods for corn, soybeans, and cotton using USDA NASS yield data and PRISM weather data. Models range from simple historical averages to specifications incorporating spatial dependence, time dynamics, and weather variables. Evaluated using out-of-sample forecasts from 2015 to 2024, results show that simple models based on recent county-level yield averages consistently outperform more complex approaches. The findings highlight the robustness and practical value of parsimonious benchmarks for operational yield forecasting. |
| Keywords: | Research Research Methods/Statistical Methods, Risk and Uncertainty |
| Date: | 2025–11–20 |
| URL: | https://d.repec.org/n?u=RePEc:ags:arpcbr:391348 |
| By: | Daniil Bargman; Francesca Medda; Akash Sedai Sharma |
| Abstract: | This paper re-examines the empirical Phillips curve (PC) model and its usefulness in the context of medium-term inflation forecasting. A latent variable Phillips curve hypothesis is formulated and tested using 3, 968 randomly generated factor combinations. Evidence from US core PCE inflation between Q1 1983 and Q1 2025 suggests that latent variable PC models reliably outperform traditional PC models six to eight quarters ahead and stand a greater chance of outperforming a univariate benchmark. Incorporating an MA(1) residual process improves the accuracy of empirical PC models across the board, although the gains relative to univariate models remain small. The findings presented in this paper have two important implications: First, they corroborate a new conceptual view on the Phillips curve theory; second, they offer a novel path towards improving the competitiveness of Phillips curve forecasts in future empirical work. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.11601 |
| By: | George Awiakye-Marfo; Elijah Agbosu; Victoria Mawuena Barns; Samuel Asante Gyamerah |
| Abstract: | Deep learning models are effective for sequential data modeling, yet commonly used activation functions such as ReLU, LeakyReLU, and PReLU often exhibit gradient instability when applied to noisy, non-stationary financial time series. This study introduces BrownianReLU, a stochastic activation function induced by Brownian motion that enhances gradient propagation and learning stability in Long Short-Term Memory (LSTM) networks. Using Monte Carlo simulation, BrownianReLU provides a smooth, adaptive response for negative inputs, mitigating the dying ReLU problem. The proposed activation is evaluated on financial time series from Apple, GCB, and the S&P 500, as well as LendingClub loan data for classification. Results show consistently lower Mean Squared Error and higher $R^2$ values, indicating improved predictive accuracy and generalization. Although ROC-AUC metric is limited in classification tasks, activation choice significantly affects the trade-off between accuracy and sensitivity, with Brownian ReLU and the selected activation functions yielding practically meaningful performance. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.16446 |
| By: | Paker, Meredith; Stephenson, Judy; Wallis, Patrick |
| Abstract: | Understanding long-run economic growth requires reliable historical data, yet the vast majority of long-run economic time series are drawn from incomplete records with significant temporal and geographic gaps. Conventional solutions to these gaps rely on linear regressions that risk bias or overfitting when data are scarce. We introduce “past predictive modeling, ” a framework that leverages machine learning and out-of-sample predictive modeling techniques to reconstruct representative historical time series from scarce data. Validating our approach using nominal wage data from England, 1300-1900, we show that this new method leads to more accurate and generalizable estimates, with bootstrapped standard errors 72% lower than benchmark linear regressions. Beyond just bettering accuracy, these improved wage estimates for England yield new insights into the impact of the Black Death on inequality, the economic geography of pre-industrial growth, and productivity over the long-run. |
| Keywords: | machine learning; predictive modeling; wages; black death; industrial revolution |
| JEL: | J31 C53 N33 N13 N63 |
| Date: | 2025–06–13 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:wpaper:128852 |
| By: | Demetrio Lacava; Edoardo Otranto |
| Abstract: | This paper investigates the impact of Trade Policy Uncertainty (TPU) on stock-bond correlation dynamics in the United States. Using daily data on major U.S. stock indices and the 10-year Treasury bond from 2015 to 2025, we estimate correlation within a two-step GARCH-based framework, relying on multivariate specifications, including Constant Conditional Correlation (CCC), Smooth Transition Conditional Correlation (STCC), and Dynamic Conditional Correlation (DCC) models. We extend these frameworks by incorporating TPU index and a presidential dummy to capture effects of trade uncertainty and government cycles. The findings show that constant correlation models are strongly rejected in favor of time-varying specifications. Both STCC and DCC models confirm TPU's central role in driving correlation dynamics, with significant differences across political regimes. DCC models augmented with TPU and political effects deliver the best in-sample fit and strongest forecasting performance, as measured by statistical and economic loss functions. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.21447 |
| By: | Shengwu Du; Flora Haberkorn; Isabel Kitschelt; Seung Jung Lee; Anderson Monken; Dylan Saez; Kelsey Shipman; Sandeep Thakur |
| Abstract: | We apply various natural language processing tools to see if the Beige Book is helpful in understanding economic activity. The Beige Book is a gathering of anecdotal compilations of current economic conditions from each Federal Reserve Bank, which is released to the public prior to FOMC meetings. We find that even controlling for lagged GDP growth and other metrics, the Beige Book sentiment provides meaningful explanatory power in nowcasting GDP growth and forecasting recessions, even more so than the yield spread or other news sentiment measures. The results on economic activity even hold in regional panel analysis. The Beige Book offers many more insights on the economy that can be gathered from even simple keyword tabulations. Topic modeling can also inform us about the different factors driving the narrative across particular periods of interest. |
| Keywords: | Now-casting; Business fluctuations and cycles; Recessions; Sentiment |
| JEL: | E32 E37 |
| Date: | 2026–01–15 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:102374 |
| By: | Eirik E. Brandsaas; Daniel I. García; Robert J. Kurtzman; Joseph B. Nichols; Adelia Zytek |
| Abstract: | Data center investment in the U.S. has increased rapidly in the post-pandemic era, and plans for future investment have surged further. Forecasting investment at such a turning point is an important but potentially fraught exercise, especially given lags in aggregate data availability. We develop a straightforward method to forecast aggregate investment using project-level microdata and a small number of parameters: specifically, abandonment rates, time from planning to start, and time from start to completion. As a key validation of our approach, we generate estimates that match the recent history of aggregate data center investment in the NIPAs. We then use our method to generate nowcasts of aggregate data center investment in the short run, with the mean forecast indicating that investment will increase to $370 billion annualized by 2026:Q2. We can extend our methodology further out, but our forecasts then become conditional on the assumed flow of new data center plans. Assuming future plans range from one-fourth to twice the average pace of plans from 2024-2025 implies a range of investment forecasts of $360 billion to $930 billion in 2027, demonstrating the substantial upside and downside risks to future levels of investment. |
| JEL: | E22 E32 L74 R33 |
| Date: | 2025–12–22 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:102368 |