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ref.bib
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ref.bib
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@article{2020-zhongwanling,
title={国际油价、宏观经济变量与中国股市的尾部风险溢出效应研究},
author={钟婉玲 and 李海奇 and 杨胜刚},
authoraddress={湖南大学金融与统计学院;},
journal={中国管理科学},
pages={1-13},
langid = {3},
year={2020},
isbn/issn={1003-207x},
notes={11-2835/g3},
databaseprovider={cnki},
doi = {10.16381/j.cnki.issn1003-207x.2020.0359},
url = {https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CAPJ&dbname=CAPJLAST&filename=ZGGK20200820008&uniplatform=NZKPT&v=l3ufLZsXuPkhvQtAmoVDo_rsgZSUtZzspwMV4v5bZWWua6r_yju52t9wmhd_oejJ},
}
@article{2014-chen-zhang-yao,
author={陈国进 and 张润泽 and 姚莲莲},
title={政策不确定性与股票市场波动溢出效应},
journal={金融经济学研究},
year={2014},
volume={},
number={5},
pages={},
month={},
url={https://www.cnki.com.cn/Article/CJFDTotal-JIRO201405007.htm}
}
@article{2007-Barber-Odean,
author = {Barber, Brad M. and Odean, Terrance},
title = "{All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors}",
journal = {The Review of Financial Studies},
volume = {21},
number = {2},
pages = {785-818},
year = {2007},
month = {12},
issn = {0893-9454},
doi = {10.1093/rfs/hhm079},
url = {https://doi.org/10.1093/rfs/hhm079},
eprint = {https://academic.oup.com/rfs/article-pdf/21/2/785/24429230/hhm079.pdf},
}
@article{2012-Pastor-Veronesi,
ISSN = {00221082, 15406261},
URL = {http://www.jstor.org/stable/23261358},
author = {Lubo\v{s} P\'{a}stor and Pietro Veronesi},
journal = {The Journal of Finance},
number = {4},
pages = {1219--1264},
publisher = {[American Finance Association, Wiley]},
title = {Uncertainty about Government Policy and Stock Prices},
volume = {67},
year = {2012},
}
@article{2018-Li-Gong,
title={经济政策不确定性冲击与股市波动率——来自宏观与微观两个层面的经验证据},
author={李力 and 宫蕾 and 王博},
journal={金融学季刊},
volume={12},
number={4},
pages={33},
year={2018},
}
@article{2014-jin-zhong-wang,
title={政策不确定性的宏观经济后果},
author={金雪军 and 钟意 and 王义中},
journal={经济理论与经济管理},
number={2},
pages={10},
year={2014},
}
@article{2016-RUAN,
title = {Investor attention and market microstructure},
journal = {Economics Letters},
volume = {149},
pages = {125-130},
year = {2016},
issn = {0165-1765},
doi = {https://doi.org/10.1016/j.econlet.2016.10.032},
url = {https://www.sciencedirect.com/science/article/pii/S0165176516304414},
author = {Xinfeng Ruan and Jin E. Zhang},
keywords = {Investor attention, Market microstructure},
}
@article{1945-hayekuse,
title={The use of knowledge in society},
author={Hayek, Friedrich August},
journal={The American economic review},
volume={35},
number={4},
pages={519--530},
year={1945},
publisher={JSTOR},
url = {https://www.jstor.org/stable/1809376},
}
@article{1985-debont-thaler,
author = {De Bont, Werner F. M. and Thaler, Richard},
title = {Does the Stock Market Overreact?},
journal = {The Journal of Finance},
volume = {40},
number = {3},
pages = {793-805},
doi = {https://doi.org/10.1111/j.1540-6261.1985.tb05004.x},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1540-6261.1985.tb05004.x},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1540-6261.1985.tb05004.x},
abstract = {ABSTRACT Research in experimental psychology suggests that, in violation of Bayes' rule, most people tend to “overreact” to unexpected and dramatic news events. This study of market efficiency investigates whether such behavior affects stock prices. The empirical evidence, based on CRSP monthly return data, is consistent with the overreaction hypothesis. Substantial weak form market inefficiencies are discovered. The results also shed new light on the January returns earned by prior “winners” and “losers.” Portfolios of losers experience exceptionally large January returns as late as five years after portfolio formation.},
year = {1985}
}
@article{2020gong,
author = {宫晓莉 and 熊熊},
title = {波动溢出网络视角的金融风险传染研究},
year = {2020},
langid = {3},
language = {c},
journal = {金融研究},
volume = {479},
number = {5},
pages = {19-39},
url = {http://www.jryj.org.cn/CN/abstract/article_740.shtml},
doi = {}
}
@article{CK1994,
author = {Carter, C. K. and Kohn, R.},
title = "{On Gibbs Sampling for State Space Models}",
journal = {Biometrika},
volume = {81},
number = {3},
pages = {541-553},
year = {1994},
month = {09},
abstract = "{We show how to use the Gibbs sampler to carry out Bayesian inference on a linear state space model with errors that are a mixture of normals and coefficients that can switch over time. Our approach simultaneously generates the whole of the state vector given the mixture and coefficient indicator variables and simultaneously generates all the indicator variables conditional on the state vectors. The states are generated efficiently using the Kalman filter. We illustrate our approach by several examples and empirically compare its performance to another Gibbs sampler where the states are generated one at a time. The empirical results suggest that our approach is both practical to implement and dominates the Gibbs sampler that generates the states one at a time.}",
issn = {0006-3444},
doi = {10.1093/biomet/81.3.541},
url = {https://doi.org/10.1093/biomet/81.3.541},
eprint = {https://academic.oup.com/biomet/article-pdf/81/3/541/714321/81-3-541.pdf},
}
@ARTICLE{1996-ERS,
title = "{Efficient Tests for an Autoregressive Unit Root}",
author = {Elliott, Graham and Rothenberg, Thomas J and Stock, James},
year = {1996},
journal = {Econometrica},
volume = {64},
number = {4},
pages = {813-36},
url = {https://EconPapers.repec.org/RePEc:ecm:emetrp:v:64:y:1996:i:4:p:813-36}
}
@article{1994-Newton,
author = {Newton, Michael A. and Raftery, Adrian E.},
title = "{Approximate Bayesian Inference with the Weighted Likelihood Bootstrap}",
journal = {Journal of the Royal Statistical Society: Series B (Methodological)},
volume = {56},
number = {1},
pages = {3-26},
doi = {https://doi.org/10.1111/j.2517-6161.1994.tb01956.x},
url = {https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/j.2517-6161.1994.tb01956.x},
eprint = {https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.2517-6161.1994.tb01956.x},
abstract = {SUMMARY We introduce the weighted likelihood bootstrap (WLB) as a way to simulate approximately from a posterior distribution. This method is often easy to implement, requiring only an algorithm for calculating the maximum likelihood estimator, such as iteratively reweighted least squares. In the generic weighting scheme, the WLB is first order correct under quite general conditions. Inaccuracies can be removed by using the WLB as a source of samples in the sampling-importance resampling (SIR) algorithm, which also allows incorporation of particular prior information. The SIR-adjusted WLB can be a competitive alternative to other integration methods in certain models. Asymptotic expansions elucidate the second-order properties of the WLB, which is a generalization of Rubin's Bayesian bootstrap. The calculation of approximate Bayes factors for model comparison is also considered. We note that, given a sample simulated from the posterior distribution, the required marginal likelihood may be simulation consistently estimated by the harmonic mean of the associated likelihood values; a modification of this estimator that avoids instability is also noted. These methods provide simple ways of calculating approximate Bayes factors and posterior model probabilities for a very wide class of models.},
year = {1994}
}
@article{2008-fw-marginal,
title = "{Marginal Likelihoods for Non-Gaussian Models Using Auxiliary Mixture Sampling}",
journal = "{Computational Statistics \& Data Analysis}",
volume = {52},
number = {10},
pages = {4608-4624},
year = {2008},
issn = {0167-9473},
doi = {https://doi.org/10.1016/j.csda.2008.03.028},
url = {https://www.sciencedirect.com/science/article/pii/S016794730800176X},
author = {Sylvia Frühwirth-Schnatter and Helga Wagner},
abstract = {Several new estimators of the marginal likelihood for complex non-Gaussian models are developed. These estimators make use of the output of auxiliary mixture sampling for count data and for binary and multinomial data. One of these estimators is based on combining Chib’s estimator with data augmentation as in auxiliary mixture sampling, while the other estimators are importance sampling and bridge sampling based on constructing an unsupervised importance density from the output of auxiliary mixture sampling. These estimators are applied to a logit regression model, to a Poisson regression model, to a binomial model with random intercept, as well as to state space modeling of count data.}
}
@article{2015-CHAN-EL,
title = "{Pitfalls of Estimating the Marginal Likelihood Using the Modified Harmonic Mean}",
journal = "{Economics Letters}",
volume = {131},
pages = {29-33},
year = {2015},
issn = {0165-1765},
doi = {https://doi.org/10.1016/j.econlet.2015.03.036},
url = {https://www.sciencedirect.com/science/article/pii/S0165176515001378},
author = {Joshua C.C. Chan and Angelia L. Grant},
keywords = {Bayesian model comparison, State space, Unobserved components, Inflation},
abstract = {The modified harmonic mean is widely used for estimating the marginal likelihood. We investigate the empirical performance of two versions of this estimator: one based on the observed-data likelihood and the other on the complete-data likelihood. Through an empirical example using US and UK inflation, we show that the version based on the complete-data likelihood has a substantial bias and tends to select the wrong model, whereas the version based on the observed-data likelihood works well.}
}
@article{Lilliefors,
author = { Hubert W. Lilliefors },
title = "{On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown}",
journal = "{Journal of the American Statistical Association}",
volume = {62},
number = {318},
pages = {399-402},
year = {1967},
publisher = {Taylor & Francis},
doi = {10.1080/01621459.1967.10482916},
URL = {
https://www.tandfonline.com/doi/abs/10.1080/01621459.1967.10482916
}
}
@article{ChanJeliazkov2009,
title="{Efficient Simulation and Integrated Likelihood
Estimation in State Space Models}",
author={Chan, Joshua and Jeliazkov, Ivan},
journal={Journal of Computational and Graphical Statistics},
volume={18},
pages={457-480},
year={2009},
url = {https://espace.library.uq.edu.au/view/UQ:205502/statespace.pdf},
}
@article{2005Primiceri,
ISSN = {00346527, 1467937X},
URL = {http://www.jstor.org/stable/3700675},
author = {Giorgio E. Primiceri},
journal = {The Review of Economic Studies},
number = {3},
pages = {821--852},
publisher = {[Oxford University Press, Review of Economic Studies, Ltd.]},
title = "{Time Varying Structural Vector Autoregressions and Monetary Policy}",
volume = {72},
year = {2005}
}
@article{2008-Giordani-Kohn,
author = {Paolo Giordani and Robert Kohn},
title = "{Efficient Bayesian Inference for Multiple Change-Point and Mixture Innovation Models}",
journal = {Journal of Business \& Economic Statistics},
volume = {26},
number = {1},
pages = {66-77},
year = {2008},
publisher = {Taylor & Francis},
doi = {
https://doi.org/10.1198/073500107000000241
},
url = {
https://www.tandfonline.com/doi/abs/10.1198/073500107000000241
}
}
@article{GCK2000,
author = { Richard Gerlach and Chris Carter and Robert Kohn },
title = "{Efficient Bayesian Inference for Dynamic Mixture Models}",
journal = {Journal of the American Statistical Association},
volume = {95},
number = {451},
pages = {819-828},
year = {2000},
publisher = {Taylor & Francis},
doi = {
https://www.tandfonline.com/doi/abs/10.1080/01621459.2000.10474273
},
url = {
https://www.tandfonline.com/doi/pdf/10.1080/01621459.2000.10474273
}
}
@article{2008_Barunik_jfe,
author = {Baruník, Jozef and Křehlík, Tomáš},
title = "{Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk}",
journal = {Journal of Financial Econometrics},
volume = {16},
number = {2},
pages = {271-296},
year = {2018},
month = {02},
abstract = "{We propose a new framework for measuring connectedness among financial variables that arise due to heterogeneous frequency responses to shocks. To estimate connectedness in short-, medium-, and long-term financial cycles, we introduce a framework based on the spectral representation of variance decompositions. In an empirical application, we document the rich time-frequency dynamics of volatility connectedness in U.S. financial institutions. Economically, periods in which connectedness is created at high frequencies are periods when stock markets seem to process information rapidly and calmly, and a shock to one asset in the system will have an impact mainly in the short term. When the connectedness is created at lower frequencies, it suggests that shocks are persistent and are being transmitted for longer periods.}",
issn = {1479-8409},
doi = {https://doi.org/10.1093/jjfinec/nby001},
url = {https://academic.oup.com/jfec/article-pdf/16/2/271/24595177/nby001.pdf},
}
@article{2015-Li-chisq,
title = "{A Bayesian Chi-Squared Test for Hypothesis Testing}",
journal = {Journal of Econometrics},
volume = {189},
number = {1},
pages = {54-69},
year = {2015},
issn = {0304-4076},
doi = {https://doi.org/10.1016/j.jeconom.2015.06.021},
url = {https://www.sciencedirect.com/science/article/pii/S030440761500192X},
author = {Yong Li and Xiao-Bin Liu and Jun Yu},
keywords = {Bayes factor, Decision theory, EM algorithm, Lagrange multiplier, Markov chain Monte Carlo, Latent variable models},
}
@TechReport{2020-Liu-posterior,
title="{Posterior-Based Wald-Type Statistics for Hypothesis Testing}",
author={Liu, Xiaobin and Li, Yong and Yu, Jun and Zeng, Tao},
year={2020},
address = {Singapore Management University},
institution={School of Economics},
url = {http://www.mysmu.edu/faculty/yujun/Research/ABT33.pdf},
}
@TechReport{koc:wpaper,
author={Dimitris Korobilis and Kamil Yilmaz},
title={{Measuring Dynamic Connectedness with Large Bayesian VAR Models}},
year=2018,
address = {Sariyer, Istanbul 34450, Turkey},
month=Jan,
institution={Koc University-TUSIAD Economic Research Forum},
type={Koç University-TUSIAD Economic Research Forum Working Papers},
url={https://ideas.repec.org/p/koc/wpaper/1802.html},
number={1802},
keywords={Connectedness; Vector autoregression; Time-varying parameter model; Rolling window estimation; Syste},
doi={},
}
@article{2016_BARUNIK_jfm,
title = "{Asymmetric Connectedness on the U.S. Stock Market: Bad and Good Volatility Spillovers}",
journal = {Journal of Financial Markets},
volume = {27},
pages = {55-78},
year = {2016},
issn = {1386-4181},
doi = {https://doi.org/10.1016/j.finmar.2015.09.003},
url = {https://www.sciencedirect.com/science/article/pii/S1386418115000622},
author = {Jozef Baruník and Evžen Kočenda and Lukáš Vácha},
keywords = {Volatility, Spillovers, Semivariance, Asymmetric effects, Financial markets},
abstract = {In this paper, we examine how to quantify asymmetries in volatility spillovers that emerge due to bad and good volatility. Using data covering most liquid U.S. stocks in seven sectors, we provide ample evidence of the asymmetric connectedness of stocks at the disaggregate level. Moreover, the spillovers of bad and good volatility are transmitted at different magnitudes that sizably change over time in different sectors. While negative spillovers are often of substantial magnitudes, they do not strictly dominate positive spillovers. We find that the overall intra-market connectedness of U.S. stocks increased substantially during the recent financial crisis.}
}
@article{2012_DIEBOLD_IJF,
title = "{Better to Give Than to Receive: Predictive Directional Measurement of Volatility Spillovers}",
journal = {International Journal of Forecasting},
volume = {28},
number = {1},
pages = {57-66},
year = {2012},
note = {Special Section 1: The Predictability of Financial Markets Special Section 2: Credit Risk Modelling and Forecasting},
issn = {0169-2070},
doi = {https://doi.org/10.1016/j.ijforecast.2011.02.006},
url = {https://www.sciencedirect.com/science/article/pii/S016920701100032X},
author = {Francis X. Diebold and Kamil Yilmaz},
keywords = {Asset market, Asset return, Stock market, Market linkage, Financial crisis, Contagion, Vector autoregression, Variance decomposition},
abstract = {Using a generalized vector autoregressive framework in which forecast-error variance decompositions are invariant to the variable ordering, we propose measures of both the total and directional volatility spillovers. We use our methods to characterize daily volatility spillovers across US stock, bond, foreign exchange and commodities markets, from January 1999 to January 2010. We show that despite significant volatility fluctuations in all four markets during the sample, cross-market volatility spillovers were quite limited until the global financial crisis, which began in 2007. As the crisis intensified, so too did the volatility spillovers, with particularly important spillovers from the stock market to other markets taking place after the collapse of the Lehman Brothers in September 2008.}
}
@article{2009KoopLeon-GonzalezStrachan,
title = "{On the Evolution of the Monetary Policy Transmission Mechanism}",
journal = {Journal of Economic Dynamics and Control},
volume = {33},
number = {4},
pages = {997-1017},
year = {2009},
issn = {0165-1889},
doi = {https://doi.org/10.1016/j.jedc.2008.11.003},
url = {https://www.sciencedirect.com/science/article/pii/S016518890800211X},
author = {Gary Koop and Roberto Leon-Gonzalez and Rodney W. Strachan},
}
@incollection{1996GHYSELS,
title = {5 Stochastic volatility},
series = {Handbook of Statistics},
publisher = {Elsevier},
volume = {14},
pages = {119-191},
year = {1996},
booktitle = {Statistical Methods in Finance},
issn = {0169-7161},
doi = {https://doi.org/10.1016/S0169-7161(96)14007-4},
url = {https://www.sciencedirect.com/science/article/pii/S0169716196140074},
author = {Eric Ghysels and Andrew C. Harvey and Eric Renault}
}
@book{2005Shephard,
editor={Shephard, Neil},
title={{Stochastic Volatility: Selected Readings}},
publisher={Oxford University Press},
year=2005,
address={Oxford University},
series={OUP Catalogue}
}
@article{2005COGLEY,
title = "{Drifts and Volatilities: Monetary Policies and Outcomes in the Post WWII US}",
journal = {Review of Economic Dynamics},
volume = {8},
number = {2},
pages = {262-302},
year = {2005},
note = {Monetary Policy and Learning},
issn = {1094-2025},
doi = {https://doi.org/10.1016/j.red.2004.10.009},
url = {https://www.sciencedirect.com/science/article/pii/S1094202505000049},
author = {Timothy Cogley and Thomas J. Sargent},
abstract = {For a VAR with drifting coefficients and stochastic volatilities, we present posterior densities for several objects that are pertinent for designing and evaluating monetary policy. These include measures of inflation persistence, the natural rate of unemployment, a core rate of inflation, and ‘activism coefficients’ for monetary policy rules. Our posteriors imply substantial variation of all of these objects for post WWII US data. After adjusting for changes in volatility, persistence of inflation increases during the 1970s, then falls in the 1980s and 1990s. Innovation variances change systematically, being substantially larger in the late 1970s than during other times. Measures of uncertainty about core inflation and the degree of persistence covary positively. We use our posterior distributions to evaluate the power of several tests that have been used to test the null hypothesis of time-invariance of autoregressive coefficients of VARs against the alternative of time-varying coefficients. Except for one, we find that those tests have low power against the form of time variation captured by our model.}
}
@article{2017shi,
author={石勇 and 唐静 and 郭琨},
langid={chinese},
title={社交媒体投资者关注、投资者情绪对中国股票市场的影响},
journal={中央财经大学学报},
year={2017},
volume={},
number={7},
pages={45-53},
month={1},
}
@mastersthesis{2017pan,
author={潘玥行},
title={投资者关注度对股票表现的实证研究},
address={哈尔滨工业大学},
publisher={哈尔滨工业大学硕士论文},
year={2017},
type={硕士论文},
month={6},
doi={10.7666/d.D01335957},
url={https://d.wanfangdata.com.cn/thesis/ChJUaGVzaXNOZXdTMjAyMTA1MTkSCUQwMTMzNTk1NxoIaHlpdWsxOXE%3D}
}
@article{2021-li,
author={李正辉 and 钟俊豪 and 董浩},
title={经济政策不确定性宏观金融效应的统计测度研究},
journal={系统工程理论与实践},
year={2021},
volume={41},
number={8},
pages={1897-1910},
month={1},
doi = {10.12011/SETP2019-2006},
url = {https://d.wanfangdata.com.cn/periodical/ChlQZXJpb2RpY2FsQ0hJTmV3UzIwMjExMDI2EhJ4dGdjbGx5c2oyMDIxMDgwMDEaCHJlYndjbXN0},
}
@article{2020lichengwei,
author={李成威 and 傅志华},
title={应对疫情对经济影响的关键是构建确定性},
journal={财政研究},
year={2020},
volume={},
number={3},
pages={3-9,118},
month={3},
}
@article{2020HUANGLUK,
title = "{Measuring Economic Policy Uncertainty in China}",
journal = {China Economic Review},
volume = {59},
pages = {101367},
year = {2020},
issn = {1043-951X},
doi = {https://doi.org/10.1016/j.chieco.2019.101367},
url = {https://www.sciencedirect.com/science/article/pii/S1043951X19301282},
author = {Yun Huang and Paul Luk},
keywords = {Economic policy uncertainty, Chinese economy, Media bias},
abstract = {We construct a new monthly index of Economic Policy Uncertainty for China in 2000–2018 based on Chinese newspapers. Different from the existing index, ours uses information from multiple local newspapers, and foreshadows declines in equity price, employment and output. Media censorship does not seem to have qualitative impact to our index. Moreover, we develop a daily uncertainty index and several policy-specific uncertainty indices for public use.}
}
@article{2018-Ichev,
title = {Stock Prices and Geographic Proximity of Information: Evidence from the Ebola Outbreak},
journal = {International Review of Financial Analysis},
volume = {56},
pages = {153-166},
year = {2018},
issn = {1057-5219},
doi = {https://doi.org/10.1016/j.irfa.2017.12.004},
url = {https://www.sciencedirect.com/science/article/pii/S1057521917301862},
author = {Riste Ichev and Matej Marinč},
keywords = {Ebola outbreak, Investor sentiment},
abstract = {Behavioral finance studies reveal that investor sentiment affects investment decisions and may therefore affect stock pricing. This paper examines whether the geographic proximity of information disseminated by the 2014–2016 Ebola outbreak events combined with intense media coverage affected stock prices in the U.S. We find that the Ebola outbreak event effect is the strongest for the stocks of companies with exposure of their operations to the West African countries (WAC) and the U.S. and for the events located in the WAC and the U.S. This result suggests that the information about Ebola outbreak events is more relevant for companies that are geographically closer to both the birthplace of the Ebola outbreak events and the financial markets. The results also show that the effect is more pronounced for small and more volatile stocks, stocks of specific industry, and for the stocks exposed to the intense media coverage. The event effect is also followed by the elevated perceived risk; that is, the implied volatility increases after the Ebola outbreak events.}
}
@incollection{2013-fischhoff-risk,
title="{Risk Perception and Communication}",
author={Fischhoff, Baruch},
booktitle="{Risk Analysis and Human Behavior}",
pages={17--46},
year={2013},
address = {Carnegie Mellon University},
publisher={Routledge}
}
@TechReport{2008BKS,
author={Ole E. Barndorff-Nielsen and Silja Kinnebrock and Neil Shephard},
title={{Measuring Downside Risk — Realised Semivariance}},
year=2008,
month=Sep,
institution={Department of Economics and Business Economics, Aarhus University},
type={CREATES Research Papers},
address = {Ny Munkegade, DK-8000 Aarhus C, Denmark},
url={https://ideas.repec.org/p/aah/create/2008-42.html},
number={2008-42},
abstract={We propose a new measure of risk, based entirely on downwards moves measured using high frequency data. Realised semivariances are shown to have important predictive qualities for future market volatility. The theory of these new measures is spelt out, drawing on some new results from probability theory.},
keywords={Market frictions; Quadratic variation; Realised variance; Semimartingale; Semivariance},
doi={},
}
@article{2016Chan_Eisenstat_Strachan,
author = {Eric Eisenstat and Joshua C. C. Chan and Rodney W. Strachan},
title = "{Stochastic Model Specification Search for Time-Varying Parameter VARs}",
journal = {Econometric Reviews},
volume = {35},
number = {8-10},
pages = {1638-1665},
year = {2016},
publisher = {Taylor & Francis},
doi = {
https://doi.org/10.1080/07474938.2015.1092808
},
eprint = {
https://doi.org/10.1080/07474938.2015.1092808
}
}
@article{2018behrendt_wikipedia,
title="{Wikipedia Search Momentum and Stock Returns}",
author={Behrendt, Simon and Zimmermann, David},
journal={Available at SSRN 3220053},
year={2018},
url={https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3220053}
}
@article{liu2015does,
title="{Does Anything Beat 5-minute RV? A Comparison of Realized Measures Across Multiple Asset Classes}",
author={Liu, Lily Y and Patton, Andrew J and Sheppard, Kevin},
journal={Journal of Econometrics},
volume={187},
number={1},
pages={293--311},
year={2015},
publisher={Elsevier},
url = {https://www.sciencedirect.com/science/article/pii/S0304407615000329},
}
@article{2011-da-search,
title="{In Search of Attention}",
author={Da, Zhi and Engelberg, Joseph and Gao, Pengjie},
journal={The Journal of Finance},
volume={66},
number={5},
pages={1461--1499},
year={2011},
publisher={Wiley Online Library},
url = {https://onlinelibrary.wiley.com/doi/full/10.1111/j.1540-6261.2011.01679.x},
}
@article{giordani2007unified,
title="{A Unified Approach to Nonlinearity, Structural Change, and Outliers}",
author={Giordani, Paolo and Kohn, Robert and van Dijk, Dick},
journal={Journal of Econometrics},
volume={137},
number={1},
pages={112--133},
year={2007},
publisher={Elsevier},
doi={https://doi.org/10.1016/j.jeconom.2006.03.013}
}
@article{DIKS20061647,
title = "{A New Statistic and Practical Guidelines for Nonparametric Granger Causality Testing}",
journal = {Journal of Economic Dynamics and Control},
volume = {30},
number = {9},
pages = {1647-1669},
year = {2006},
note = {Computing in economics and finance},
issn = {0165-1889},
doi = {https://doi.org/10.1016/j.jedc.2005.08.008},
url = {https://www.sciencedirect.com/science/article/pii/S016518890600056X},
author = {Cees Diks and Valentyn Panchenko},
keywords = {Financial time series, Granger causality, Nonparametric, Hypothesis testing, Size distortion, U-statistics},
abstract = {In this paper we introduce a new nonparametric test for Granger non-causality which avoids the over-rejection observed in the frequently used test proposed by Hiemstra and Jones [1994. Testing for linear and nonlinear Granger causality in the stock price-volume relation. Journal of Finance 49, 1639–1664]. After illustrating the problem by showing that rejection probabilities under the null hypothesis may tend to one as the sample size increases, we study the reason behind this phenomenon analytically. It turns out that the Hiemstra–Jones test for the null of Granger non-causality, which can be rephrased in terms of conditional independence of two vectors X and Z given a third vector Y, is sensitive to variations in the conditional distributions of X and Z that may be present under the null. To overcome this problem we replace the global test statistic by an average of local conditional dependence measures. By letting the bandwidth tend to zero at appropriate rates, the variations in the conditional distributions are accounted for automatically. Based on asymptotic theory we formulate practical guidelines for choosing the bandwidth depending on the sample size. We conclude with an application to historical returns and trading volumes of the Standard and Poor's index which indicates that the evidence for volume Granger-causing returns is weaker than suggested by the Hiemstra–Jones test.}
}
@article{2007KoopPotter,
author = {Koop, Gary and Potter, Simon M.},
title = "{Estimation and Forecasting in Models with Multiple Breaks}",
journal = {The Review of Economic Studies},
volume = {74},
number = {3},
pages = {763-789},
year = {2007},
month = {07},
abstract = "{This paper develops a new approach to change-point modelling that allows the number of change-points in the observed sample to be unknown. The model we develop assumes that regime durations have a Poisson distribution. It approximately nests the two most common approaches: the time-varying parameter (TVP) model with a change-point every period and the change-point model with a small number of regimes. We focus considerable attention on the construction of reasonable hierarchical priors both for regime durations and for the parameters that characterize each regime. A Markov chain Monte Carlo posterior sampler is constructed to estimate a version of our model, which allows for change in conditional means and variances. We show how real-time forecasting can be done in an efficient manner using sequential importance sampling. Our techniques are found to work well in an empirical exercise involving U.S. GDP growth and inflation. Empirical results suggest that the number of change-points is larger than previously estimated in these series and the implied model is similar to a TVP (with stochastic volatility) model.}",
issn = {0034-6527},
doi = {https://doi.org/10.1111/j.1467-937X.2007.00436.x},
url = {https://academic.oup.com/restud/article-pdf/74/3/763/18399665/74-3-763.pdf},
}
@article{uhlig1997bayesian,
title="{Bayesian Vector Autoregressions with Stochastic Volatility}",
author={Uhlig, Harald},
journal={Econometrica: Journal of the Econometric Society},
pages={59--73},
year={1997},
publisher={JSTOR},
url = {http://www.jstor.org/stable/2171813},
}
@article{2009-Diebold,
title={Measuring financial asset return and volatility spillovers, with application to global equity markets},
author={Diebold, Francis X and Yilmaz, Kamil},
journal={The Economic Journal},
volume={119},
number={534},
pages={158--171},
year={2009},
publisher={Oxford University Press Oxford, UK}
}
@article{2014-Diebold,
title = "{On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms}",
journal = {Journal of Econometrics},
volume = {182},
number = {1},
pages = {119-134},
year = {2014},
note = {Causality, Prediction, and Specification Analysis: Recent Advances and Future Directions},
issn = {0304-4076},
doi = {https://doi.org/10.1016/j.jeconom.2014.04.012},
url = {https://www.sciencedirect.com/science/article/pii/S0304407614000712},
author = {Francis X. Diebold and Kamil Yilmaz},
keywords = {Risk measurement, Risk management, Portfolio allocation, Market risk, Credit risk, Systemic risk, Asset markets, Degree distribution},
abstract = {We propose several connectedness measures built from pieces of variance decompositions, and we argue that they provide natural and insightful measures of connectedness. We also show that variance decompositions define weighted, directed networks, so that our connectedness measures are intimately related to key measures of connectedness used in the network literature. Building on these insights, we track daily time-varying connectedness of major US financial institutions’ stock return volatilities in recent years, with emphasis on the financial crisis of 2007–2008.}
}
@article{1965rts,
author = {Rauch, H. E. and Tung, F. and Striebel, C. T.},
title = "{Maximum Likelihood Estimates of Linear Dynamic Systems}",
journal = {AIAA Journal},
volume = {3},
number = {8},
pages = {1445-1450},
year = {1965},
doi = {10.2514/3.3166},
URL = {
https://doi.org/10.2514/3.3166
},
eprint = {
https://doi.org/10.2514/3.3166
}
}
@article{baker2016measuring,
title="{Measuring Economic Policy Uncertainty}",
author={Baker, Scott R and Bloom, Nicholas and Davis, Steven J},
journal={The quarterly journal of economics},
volume={131},
number={4},
pages={1593--1636},
year={2016},
publisher={Oxford University Press},
url = {https://academic.oup.com/qje/article/131/4/1593/2468873},
}
@article{2015-RFS-FinancialAttention,
author = {Sicherman, Nachum and Loewenstein, George and Seppi, Duane J. and Utkus, Stephen P.},
title = "{Financial Attention}",
journal = {The Review of Financial Studies},
volume = {29},
number = {4},
pages = {863-897},
year = {2015},
month = {11},
issn = {0893-9454},
doi = {10.1093/rfs/hhv073},
url = {https://doi.org/10.1093/rfs/hhv073},
eprint = {https://academic.oup.com/rfs/article-pdf/29/4/863/24451268/hhv073.pdf},
}
@article{2014-meerkat_effect,
title = {The Meerkat Effect: Personality and Market Returns Affect Investors’ Portfolio Monitoring Behaviour},
journal = {Journal of Economic Behavior $\&$ Organization},
volume = {107},
pages = {512-526},
year = {2014},
note = {Empirical Behavioral Finance},
issn = {0167-2681},
doi = {https://doi.org/10.1016/j.jebo.2014.07.013},
url = {https://www.sciencedirect.com/science/article/pii/S0167268114002145},
author = {Svetlana Gherzi and Daniel Egan and Neil Stewart and Emily Haisley and Peter Ayton},
}
@article{1998Kim,
author = {Kim, Sangjoon and Shephard, Neil and Chib, Siddhartha},
title = "{Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models}",
journal = {The Review of Economic Studies},
volume = {65},
number = {3},
pages = {361-393},
year = {1998},
month = {07},
abstract = "{In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models. A highly effective method is developed that samples all the unobserved volatilities at once using an approximating offset mixture model, followed by an importance reweighting procedure. This approach is compared with several alternative methods using real data. The paper also develops simulation-based methods for filtering, likelihood evaluation and model failure diagnostics. The issue of model choice using non-nested likelihood ratios and Bayes factors is also investigated. These methods are used to compare the fit of stochastic volatility and GARCH models. All the procedures are illustrated in detail.}",
issn = {0034-6527},
doi = {10.1111/1467-937X.00050},
url = {https://doi.org/10.1111/1467-937X.00050},
eprint = {https://academic.oup.com/restud/article-pdf/65/3/361/4459409/65-3-361.pdf},
}
@TechReport{2006gk_wpaper,
author={Giordani, Paolo and Kohn, Robert},
title={{Efficient Bayesian Inference for Multiple Change-Point and Mixture Innovation Models}},
year=2006,
month=May,
address={Central Bank of Sweden, Stockholm},
institution={Sveriges Riksbank Working Paper Series},
type={Sveriges Riksbank Working Paper Series},
url={https://ideas.repec.org/p/hhs/rbnkwp/0196.html},
number={No.196},
doi={},
}
@TechReport{2014-barunik-wpaper,
title="{Asymmetric Connectedness of Stocks: How Does Bad and Good Volatility Spill Over the U.S. Stock Market?}",
author={Jozef Baruník and Evžen Kočenda and Lukáš Vácha},
year={2014},
eprint={1308.1221},
type = {Working Paper Series},
url={https://arxiv.org/abs/1308.1221v2},
institution={Institute of Economic Studies, Charles University},
address = {Prague, Czech Republic},
}
@article{2010GKadaptive,
author = {Paolo Giordani and Robert Kohn},
title = "{Adaptive Independent Metropolis–Hastings by Fast Estimation of Mixtures of Normals}",
journal = {Journal of Computational and Graphical Statistics},
volume = {19},
number = {2},
pages = {243-259},
year = {2010},
publisher = {Taylor & Francis},
url = {https://www.tandfonline.com/doi/abs/10.1198/jcgs.2009.07174},
doi = {
https://doi.org/10.1198/jcgs.2009.07174
}
}
@article{2009Koop-prior-elicitation,
author = {Koop, Gary and Potter, Simon M.},
title = "{Prior Elicitation in Multiple Change-point Models}",
journal = {International Economic Review},
volume = {50},
number = {3},
pages = {751-772},
doi = {https://doi.org/10.1111/j.1468-2354.2009.00547.x},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1468-2354.2009.00547.x},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1468-2354.2009.00547.x},
abstract = {This article discusses Bayesian inference in change-point models. The main existing approaches treat all change-points equally, a priori, using either a Uniform prior or an informative hierarchical prior. Both approaches assume a known number of change-points. Some undesirable properties of these approaches are discussed. We develop a new Uniform prior that allows some of the change-points to occur out of sample. This prior has desirable properties, can be interpreted as “noninformative,” and treats the number of change-points as unknown. Artificial and real data exercises show how these different priors can have a substantial impact on estimation and prediction.},
year = {2009}
}
@article{2002Durbin_smoother,
author = {Durbin, J. and Koopman, S. J.},
title = "{A Simple and Efficient Simulation Smoother for State Space Time Series Analysis}",
journal = {Biometrika},
volume = {89},
number = {3},
pages = {603-616},
year = {2002},
month = {08},
abstract = "{A simulation smoother in state space time series analysis is a procedure for drawing samples from the conditional distribution of state or disturbance vectors given the observations. We present a new technique for this which is both simple and computationally efficient. The treatment includes models with diffuse initial conditions and regression effects. Computational comparisons are made with the previous standard method. Two applications are provided to illustrate the use of the simulation smoother for Gibbs sampling for Bayesian inference and importance sampling for classical inference.}",
issn = {0006-3444},
doi = {10.1093/biomet/89.3.603},
url = {https://doi.org/10.1093/biomet/89.3.603},
eprint = {https://academic.oup.com/biomet/article-pdf/89/3/603/699345/890603.pdf},
}
@article{2009-Bloom,
author = {Bloom, Nicholas},
title = "{The Impact of Uncertainty Shocks}",
journal = {Econometrica},
volume = {77},
number = {3},
pages = {623-685},
keywords = {Adjustment costs, uncertainty, real options, labor and investment},
doi = {https://doi.org/10.3982/ECTA6248},
url = {https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA6248},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.3982/ECTA6248},
abstract = {Uncertainty appears to jump up after major shocks like the Cuban Missile crisis, the assassination of JFK, the OPEC I oil-price shock, and the 9/11 terrorist attacks. This paper offers a structural framework to analyze the impact of these uncertainty shocks. I build a model with a time-varying second moment, which is numerically solved and estimated using firm-level data. The parameterized model is then used to simulate a macro uncertainty shock, which produces a rapid drop and rebound in aggregate output and employment. This occurs because higher uncertainty causes firms to temporarily pause their investment and hiring. Productivity growth also falls because this pause in activity freezes reallocation across units. In the medium term the increased volatility from the shock induces an overshoot in output, employment, and productivity. Thus, uncertainty shocks generate short sharp recessions and recoveries. This simulated impact of an uncertainty shock is compared to vector autoregression estimations on actual data, showing a good match in both magnitude and timing. The paper also jointly estimates labor and capital adjustment costs (both convex and nonconvex). Ignoring capital adjustment costs is shown to lead to substantial bias, while ignoring labor adjustment costs does not.},
year = {2009}
}
@article{2017chenguojin,
author={陈国进 and 张润泽 and 赵向琴},
language = {c},
title={政策不确定性、消费行为与股票资产定价},
journal={世界经济},
year={2017},
volume={40},
number={1},
pages={116-141},
month={1},
}
@article{2020yang,
author={杨子晖 and 陈里璇 and 陈雨恬},
title={经济政策不确定性与系统性金融风险的跨市场传染——基于非线性网络关联的研究},
journal={经济研究},
year={2020},
volume={55},
number={1},
pages={65-81},
month={1},
url = {https://d.wanfangdata.com.cn/periodical/ChlQZXJpb2RpY2FsQ0hJTmV3UzIwMjExMDI2Eg1qanlqMjAyMDAxMDEwGghoY3V3cHg2dw%3D%3D},
}
@article{2009-KarlssonTheOE,
title={The ostrich effect: Selective attention to information},
author={Niklas Karlsson and George Loewenstein and Duane J. Seppi},
journal={Journal of Risk and Uncertainty},
year={2009},
volume={38},
pages={95-115},
doi = {https://doi.org/10.1007/s11166-009-9060-6},
url = {https://link.springer.com/article/10.1007/s11166-009-9060-6},
}
@article{2020WANGasy,
title = "{Asymmetric Volatility Spillovers Between Economic Policy Uncertainty and Stock Markets: Evidence from China}",
journal = {Research in International Business and Finance},
volume = {53},
pages = {101233},
year = {2020},
issn = {0275-5319},
doi = {https://doi.org/10.1016/j.ribaf.2020.101233},
url = {https://www.sciencedirect.com/science/article/pii/S0275531919309419},
author = {Ziwei Wang and Youwei Li and Feng He},
keywords = {Economic policy uncertainty, Realized volatility, Asymmetry spillover, Good and bad volatility},
abstract = {This study explores the spillovers between economic policy uncertainty (EPU) and stock market realized volatility (RV). The monthly index of Chinese and US EPU and RV are used to analyze the pairwise directional spillovers. We find that RV is a net receiver that is more vulnerable to shocks from U.S. EPU than to shocks from Chinese EPU. We further decompose the RV into good and bad volatility to test the asymmetric spillover effect between the stock market and EPU. The results suggest that EPU has a bigger effect on bad volatility in the stock market throughout most of the sample period. However, we find that good volatility spillovers become larger during periods of stimulated reform, whereas bad volatility spillovers become larger during periods of international disputes. We show that Chinese stock market volatility is sensitive to both U.S. and Chinese EPU and that the spillover is asymmetric in different periods.}
}
@article{1972-Fox,
author = {Fox, A. J.},
title = "{Outliers in Time Series}",
journal = {Journal of the Royal Statistical Society: Series B (Methodological)},
volume = {34},
number = {3},
pages = {350-363},
keywords = {time series, outliers, maximum likelihood ratio tests, simulation of power curves},
doi = {https://doi.org/10.1111/j.2517-6161.1972.tb00912.x},
url = {https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/j.2517-6161.1972.tb00912.x},
eprint = {https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.2517-6161.1972.tb00912.x},
abstract = {Summary Two models are considered for outliers and their effects in time series. Likelihood ratio and approximate likelihood ratio criteria are derived for these models and the power functions are compared with that of the approach generally applied in the past.},
year = {1972}
}
@article{2015Patton-GoodVB,
author = {Patton, Andrew J. and Sheppard, Kevin},
title = "{Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility}",
journal = {The Review of Economics and Statistics},
volume = {97},
number = {3},
pages = {683-697},
year = {2015},
month = {07},
issn = {0034-6535},
doi = {10.1162/REST_a_00503},
url = {https://doi.org/10.1162/REST\_a\_00503},
eprint = {https://direct.mit.edu/rest/article-pdf/97/3/683/1917950/rest\_a\_00503.pdf},
}
@article{2015SEGAL,
title = {Good and bad uncertainty: Macroeconomic and financial market implications},
journal = {Journal of Financial Economics},
volume = {117},
number = {2},
pages = {369-397},
year = {2015},
issn = {0304-405X},
doi = {https://doi.org/10.1016/j.jfineco.2015.05.004},
url = {https://www.sciencedirect.com/science/article/pii/S0304405X15000756},
author = {Gill Segal and Ivan Shaliastovich and Amir Yaron},
}
@book{1994-tsa-Hamilton,
author = {Hamilton, James D.},
biburl = {https://www.bibsonomy.org/bibtex/25717b936b0de29cfdb58a83db18c2c3b/alexv},
day = 11,
edition = 1,
chapter = {22},
howpublished = {Hardcover},
isbn = {0691042896},
keywords = {kalman-filter statistics time-series},
month = jan,
posted-at = {2007-10-16 20:29:48},
priority = {2},
address={Princeton University},
publisher = {Princeton University Press},
timestamp = {2019-08-24T00:21:00.000+0200},
title = {{Time Series Analysis}},
url = {http://www.amazon.com/exec/obidos/redirect?tag=citeulike07-20\&path=ASIN/0691042896},
year = 1994
}
@article{2000SmithKohn,
title = {Nonparametric Seemingly Unrelated Regression},
journal = {Journal of Econometrics},
volume = {98},
number = {2},
pages = {257-281},
year = {2000},
issn = {0304-4076},
doi = {https://doi.org/10.1016/S0304-4076(00)00018-X},
url = {https://www.sciencedirect.com/science/article/pii/S030440760000018X},
author = {Michael Smith and Robert Kohn},
keywords = {Nonparametric multivariate regression, Bayesian hierarchical SUR model, Multivariate subset selection, Markov Chain Monte Carlo},
abstract = {A method is presented for simultaneously estimating a system of nonparametric regressions which may seem unrelated, but where the errors are potentially correlated between equations. We show that the advantage of estimating such a ‘seemingly unrelated’ system of nonparametric regressions is that less observations can be required to obtain reliable function estimates than if each of the regression equations is estimated separately and the correlation ignored. This increase in efficiency is investigated empirically using both simulated and real data. The method uses a Bayesian hierarchical framework where each regression function is represented as a linear combination of a large number of basis terms. All the regression coefficients, and the variance matrix of the errors, are estimated simultaneously by their posterior means. The computation is carried out using a Markov chain Monte Carlo sampling scheme that employs a ‘focused sampling’ step to combat the high-dimensional representation of the unknown regression functions. The methodology extends easily to other nonparametric multivariate regression models.}
}
@article{2002-Barndorff-jrssb,
author = {Barndorff-Nielsen, Ole E. and Shephard, Neil},
title = {Econometric analysis of realized volatility and its use in estimating stochastic volatility models},
journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
volume = {64},
number = {2},
pages = {253-280},
doi = {https://doi.org/10.1111/1467-9868.00336},
url = {https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/1467-9868.00336},
eprint = {https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/1467-9868.00336},
year = {2002}
}
@article{1998PESARAN,
title = {Generalized impulse response analysis in linear multivariate models},
journal = {Economics Letters},
volume = {58},
number = {1},
pages = {17-29},
year = {1998},
issn = {0165-1765},
doi = {https://doi.org/10.1016/S0165-1765(97)00214-0},
url = {https://www.sciencedirect.com/science/article/pii/S0165176597002140},
author = {H.Hashem Pesaran and Yongcheol Shin},
keywords = {Generalized impulse responses, Forecast error variance decompositions, VAR, Cointegration},
abstract = {Building on Koop, [Koop et al. (1996) Impulse response analysis in nonlinear multivariate models. Journal of Econometrics 74, 119–147] we propose the `generalized' impulse response analysis for unrestricted vector autoregressive (VAR) and cointegrated VAR models. Unlike the traditional impulse response analysis, our approach does not require orthogonalization of shocks and is invariant to the ordering of the variables in the VAR. The approach is also used in the construction of order-invariant forecast error variance decompositions.}
}
@article{2002-BayesianFit,
author = {Spiegelhalter, David J. and Best, Nicola G. and Carlin, Bradley P. and Van Der Linde, Angelika},
title = "{Bayesian Measures of Model Complexity and Fit}",
journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
volume = {64},
number = {4},
pages = {583-639},
keywords = {Bayesian model comparison, Decision theory, Deviance information criterion, Effective number of parameters, Hierarchical models, Information theory, Leverage, Markov chain Monte Carlo methods, Model dimension},
doi = {https://doi.org/10.1111/1467-9868.00353},
url = {https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/1467-9868.00353},
eprint = {https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/1467-9868.00353},
year = {2002}
}
@article{2015-Primiceri,
author = {Del Negro, Marco and Primiceri, Giorgio E.},
title = "{Time Varying Structural Vector Autoregressions and Monetary Policy: A Corrigendum}",
journal = {The Review of Economic Studies},
volume = {82},
number = {4},
pages = {1342-1345},
year = {2015},
month = {06},
abstract = "{ This note shows how to apply the procedure of Kim et al . (1998) to the estimation of VAR, DSGE, factor, and unobserved components models with stochastic volatility. In particular, it revisits the estimation algorithm of the time-varying VAR model of Primiceri (2005) . The main difference of the new algorithm is the ordering of the various MCMC steps, with each individual step remaining the same. }",
issn = {0034-6527},
doi = {10.1093/restud/rdv024},
url = {https://doi.org/10.1093/restud/rdv024},
eprint = {https://academic.oup.com/restud/article-pdf/82/4/1342/17416860/rdv024.pdf},
}
@article{2018zhengtg,
author={郑挺国 and 刘堂勇},
title={股市波动溢出效应及其影响因素分析},
journal={经济学（季刊）},
year={2018},
langid = {3},
volume={17},
number={2},
pages={669-692},
month={1},
doi = {10.13821/j.cnki.ceq.2018.01.10},
url = {https://d.wanfangdata.com.cn/periodical/ChlQZXJpb2RpY2FsQ0hJTmV3UzIwMjExMDI2EgxqangyMDE4MDIwMTAaCHU2ejg3M3pv}
}
@article{2020-Barunik,
author = {{Baruník}, Jozef and {Ellington}, Michael},
title = "{Persistence in Economic Networks}",
journal = {arXiv e-prints},
keywords = {Economics - Econometrics},
year = 2020,
month = jul,
eid = {arXiv:2007.07842},