Big Data in Economics and Management
portes grátis
Big Data in Economics and Management
Zhang, Kun; Zhang, Yuqian; Yan, Xing; Yang, Songshan; Zhang, Zheng
Springer Verlag, Singapore
04/2026
218
Dura
Inglês
9789819531240
Pré-lançamento - envio 15 a 20 dias após a sua edição
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Part I Causal Inference in Economics.- 1 Causal Inference for A Discrete Treatment.- 1.1 Basic Framework.- 1.2 Causal Inference based on Covariate Balancing Calibration.- 1.3 Causal Inference based on Semi-supervised Data.- 1.4 Causal Inference based on Neural Networks.- 2 Causal Inference for A Continuous Treatment.- 2.1 Basic Framework.- 2.2 Semiparametric Efficiency Bound.- 2.3 Maximum Entropy Weighting.- 2.4 Efficient Estimation Results.- 2.5 Model Specification Tests.- 2.6 Nonparametric Estimation of ATE.- 2.7 Nonparametric Estimation of Distributional and Quantile Treatment Effects.- 2.8 Testing Distributional Effects.- 2.9 Empirical Study: Presidential Campaign Data.- 3 Causal Inference with Measurement Errors.- 3.1 Basic Framework.- 3.2 Estimation Method.- 3.3 Large Sample Properties.- 3.4 Select the Smoothing Parameters.- 3.5 Real Data Example.- Part II Financial Model Computing and Decisions.- 4 Efficient Computing for High-Dimensional Econometric Models.- 4.1 Introduction.- 4.2 Asset-splitting algorithm for portfolio selection.- 4.3 Feature-splitting algorithm for PQR.- 4.4 Numerical study.- 4.5 Conclusion and discussion.- 4.6 Appendix.- Part III Financial Risk Management.- 5 Bootstrap-based Budget Allocation for Nested Simulation.- 5.1 Introduction.- 5.2 Backgrounds.- 5.3 A Sample-Driven Budget Allocation Method.- 5.4 Appendix.- 6 Constructing Confidence Intervals for Nested Simulation.- 6.1 Introduction.- 6.2 Formulations.- 6.3 Confidence Intervals.- 7 Deep Probabilistic Forecasting for Market Risks.- 7.1 Background of Market Risk Forecasting.- 7.2 Background of Uncertainty Quantification in Machine Learning.- 7.3 Deep Sequential Learning of Conditional Heavy-Tailed Distributions.- 7.4 Ensemble Multi-Quantile Regression with Deep Learning.- Appendix.- References.
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Causal Inference;Machine Learning;Quantile Regression;Portfolio Optimization;Financial Risk Measurement;Efficient Model Computing;Big Data;Open Access
Part I Causal Inference in Economics.- 1 Causal Inference for A Discrete Treatment.- 1.1 Basic Framework.- 1.2 Causal Inference based on Covariate Balancing Calibration.- 1.3 Causal Inference based on Semi-supervised Data.- 1.4 Causal Inference based on Neural Networks.- 2 Causal Inference for A Continuous Treatment.- 2.1 Basic Framework.- 2.2 Semiparametric Efficiency Bound.- 2.3 Maximum Entropy Weighting.- 2.4 Efficient Estimation Results.- 2.5 Model Specification Tests.- 2.6 Nonparametric Estimation of ATE.- 2.7 Nonparametric Estimation of Distributional and Quantile Treatment Effects.- 2.8 Testing Distributional Effects.- 2.9 Empirical Study: Presidential Campaign Data.- 3 Causal Inference with Measurement Errors.- 3.1 Basic Framework.- 3.2 Estimation Method.- 3.3 Large Sample Properties.- 3.4 Select the Smoothing Parameters.- 3.5 Real Data Example.- Part II Financial Model Computing and Decisions.- 4 Efficient Computing for High-Dimensional Econometric Models.- 4.1 Introduction.- 4.2 Asset-splitting algorithm for portfolio selection.- 4.3 Feature-splitting algorithm for PQR.- 4.4 Numerical study.- 4.5 Conclusion and discussion.- 4.6 Appendix.- Part III Financial Risk Management.- 5 Bootstrap-based Budget Allocation for Nested Simulation.- 5.1 Introduction.- 5.2 Backgrounds.- 5.3 A Sample-Driven Budget Allocation Method.- 5.4 Appendix.- 6 Constructing Confidence Intervals for Nested Simulation.- 6.1 Introduction.- 6.2 Formulations.- 6.3 Confidence Intervals.- 7 Deep Probabilistic Forecasting for Market Risks.- 7.1 Background of Market Risk Forecasting.- 7.2 Background of Uncertainty Quantification in Machine Learning.- 7.3 Deep Sequential Learning of Conditional Heavy-Tailed Distributions.- 7.4 Ensemble Multi-Quantile Regression with Deep Learning.- Appendix.- References.
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