| Title | Authors | Year | JEL Codes |
|---|
| CCE Estimation of High-Dimensional Panel Data Models with Interactive Fixed Effects | Vogt, M., Walsh, C., Linton, O. | [2022] | C13 C23 C55 |
| A Structural Dynamic Factor Model for Daily Global Stock Market Returns | Linton, O. B., Tang, H., Wu, J. | [2022] | C55 C58 G15 |
| Using Past Violence and Current News to Predict Changes in Violence | Mueller, H., Rauh, C.
| [2022] | F21 C53 C55 |
| A Bias-Corrected CD Test for Error Cross-Sectional Dependence in Panel Data Models with Latent Factors | Pesaran, M. H. and Xie, Y. | [2021] | C18 C23 C55 |
| Identifying residential consumption patterns using data-mining techniques: A large-scale study of smart meter data in Chengdu, China | Kang, J. and Reiner, D. | [2021] | C55 D12 R22 Q41 |
| Machine Learning on residential electricity consumption: Which households are more responsive to weather? | Kang, J. and Reiner, D. | [2021] | C55 D12 R22 Q41 |
| What is the effect of weather on household electricity consumption? Empirical evidence from Ireland | Kang, J. and Reiner, D. | [2021] | C55 D12 R22 Q41 |
| The Hard Problem of Prediction for Conflict Prevention | Mueller, H. and Rauh, C.
| [2021] | F21 C53 C55 |
| Regional Heterogeneity and U.S. Presidential Elections | Ahmed, R. and Pesaran, M. H. | [2020] | C53 C55 D72 |
| The Hard Problem of Prediction for Conflict Prevention (see cwpe2103) | Mueller, H. and Rauh, C.
| [2021] | F21 C53 C55 |
| Can Alternative Data Improve the Accuracy of Dynamic Factor Model Nowcasts? | Cristea, R. G. | [2020] | C38 C53 C55 E27 E66 Y40 |
| Dependent Microstructure Noise and Integrated Volatility: Estimation from High-Frequency Data | Li, Z. M., Laeven, R. J. A. and Vellekoop, M. H. | [2019] | C13 C14 C55 C58 |
| Fixed-Effect Regressions on Network Data | Jochmans, K. and Weidner, M. | [2019] | C23 C55 |
| Estimation of a Multiplicative Correlation Structure in the Large Dimensional Case | Hafner, C., Linton, O., Tang, H. | [2018] | C55 C58 G11 |
| Causal Tree Estimation of Heterogeneous Household Response to Time-Of-Use Electricity Pricing Schemes | O'Neill, E., Weeks, M. | [2018] | Q41 C55 |
| A novel machine learning approach for identifying the drivers of domestic electricity users’ price responsiveness | Guo, P., Lam, J., Li, V. | [2018] | Q41 C55 |
| A One-Covariate at a Time, Multiple Testing Approach to Variable Selection in High-Dimensional Linear Regression Models | Chudik, A., and Kapetanios, G. and Pesaran, Hashem | [2016] | C52 C55 |
| Big Data Analytics: A New Perspective | Chudik, A., Kapetanios, G. and Pesaran, M. H. | [2016] | C52 C55 |
