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Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis

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Table of Contents

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    Book Overview
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    Chapter 1 Improving U.S. GDP Measurement: A Forecast Combination Perspective
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    Chapter 2 Identification Without Exogeneity Under Equiconfounding in Linear Recursive Structural Systems
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    Chapter 3 Optimizing Robust Conditional Moment Tests: An Estimating Function Approach
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    Chapter 4 Asymptotic Properties of Penalized M Estimators with Time Series Observations
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    Chapter 5 A Survey of Recent Advances in Forecast Accuracy Comparison Testing, with an Extension to Stochastic Dominance
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    Chapter 6 New Directions in Information Matrix Testing: Eigenspectrum Tests
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    Chapter 7 Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis
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    Chapter 8 Hal White: Time at MIT and Early Days of Research
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    Chapter 9 Open-Model Forecast-Error Taxonomies
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    Chapter 10 Heavy-Tail and Plug-In Robust Consistent Conditional Moment Tests of Functional Form
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    Chapter 11 Nonparametric Identification in Dynamic Nonseparable Panel Data Models
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    Chapter 12 Consistent Model Selection: Over Rolling Windows
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    Chapter 13 Estimating Misspecified Moment Inequality Models
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    Chapter 14 Model Adequacy Checks for Discrete Choice Dynamic Models
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    Chapter 15 On Long-Run Covariance Matrix Estimation with the Truncated Flat Kernel
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    Chapter 16 Predictability and Specification in Models of Exchange Rate Determination
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    Chapter 17 Thirty Years of Heteroskedasticity-Robust Inference
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    Chapter 18 Smooth Constrained Frontier Analysis
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    Chapter 19 NoVaS Transformations: Flexible Inference for Volatility Forecasting
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    Chapter 20 Regression Efficacy and the Curse of Dimensionality
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