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Faculty of Economics

C55

Large Data Sets: Modeling and Analysis


Title AuthorsYearJEL Codes
Identifying residential consumption patterns using data-mining techniques: A large-scale study of smart meter data in Chengdu, ChinaKang, 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 IrelandKang, J. and Reiner, D.[2021]C55 D12 R22 Q41
Regional Heterogeneity and U.S. Presidential ElectionsAhmed, R. and Pesaran, M. H.[2020]C53 C55 D72
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 DataLi, Z. M., Laeven, R. J. A. and Vellekoop, M. H.[2019]C13 C14 C55 C58
Fixed-Effect Regressions on Network DataJochmans, 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 SchemesO'Neill, E., Weeks, M.[2018]Q41 C55
A novel machine learning approach for identifying the drivers of domestic electricity users’ price responsivenessGuo, 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 ModelsChudik, A., and Kapetanios, G. and Pesaran, Hashem[2016]C52 C55
Big Data Analytics: A New PerspectiveChudik, A., Kapetanios, G. and Pesaran, M. H.[2016]C52 C55

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