一看目录，就明白，是个论文集

不过包含了很多新的idea

目测有：

动态因子模型

FVAR， Panel VAR和Global VAR

大型BVAR

大数据中的Volatility

神经网络

主成分分析和静态因子分析

……

密度预测

拐点和分类

总之，大数据时代，计量模型同样也在很快地发展着

总之，这是一本好书。

贴这本书的preface

The last three decades have seen a surge in data collection. During the same period, statisticians and econometricians have developed numerous techniques to digest the ever-growing amount of data and improve predictions. This volume surveys the adaptation of these methods to macroeconomic forecasting from both the theoretical and the applied perspective. The reader is presented with the current state of the literature and a broad collection of tools for analyzing large macroeconomic datasets. The intended audience includes researchers, professional forecasters, instructors, and students. The volume can be used as a reference and a teaching resource in advanced courses focusing on macroeconomic forecasting.

Each chapter of the book is self-contained with references. The topics are grouped into five main parts. Part I sets the stage by surveying big data types and sources. Part II reviews some of the main approaches for modeling relationships among macroeconomic time series, including dynamic factor models, vector autoregressions, volatility models, and neural networks. Part III showcases dimension reduction techniques yielding parsimonious model specifications. Part IV surveys methods that deal with model and forecast uncertainty. Several techniques described in Parts III and IV originated in the statistical learning literature and have been successfully adopted in econometrics. They are frequently combined to avoid overfitting and to improve forecast accuracy. Part V examines important extensions of the topics covered in the previous three sections.

This volume assumes prior training in econometrics and time series analysis. By filling a niche in forecasting in data-rich environments, it complements more comprehensive texts such as Economic Forecasting by Elliott and Timmermann (2016) and Applied Economic Forecasting Using Time Series Methods by Ghysels and Marcellino (2018). The chapters in this book attempt to balance the depth and breadth of the covered material, and are more survey-like than the papers published in the 2019 Journal of Econometrics special issue titled Big Data in Dynamic Predictive Econometric Modeling. Given the rapid evolution of big data analysis, the topics included in this volume have to be somewhat selective, focusing on the time series aspects of macroeconomic forecasting—as implied by the title—rather than on spatial, network, structural, and causal modeling.

I began working on this volume during my sabbatical at the Central European University, where I received lots of valuable advice and encouragement to proceed from László Mátyás. Working on this book has been a great learning experience. I would like to thank the wonderful team of contributors for excellent chapters produced in a timely manner. I appreciate their responsiveness and patience in dealing with my requests. I would also like to thank my colleagues at the University of Hawaii for letting me focus on this project during a busy academic year.

                                                                          Peter Fuleky

Honolulu, HI, USA May 2019