Journal of Business & Economic Statistics 2023年第1期

?

?

——更多動態(tài)，請持續(xù)關注gzh：理想主義的百年孤獨

?

?

1.Reconciling Trends in U.S. Male Earnings Volatility: Results from Survey and Administrative Data

調和美國男性收入波動的趨勢:來自調查和行政數(shù)據(jù)的結果

Robert Moffitt, John Abowd, Christopher Bollinger, Michael Carr, Charles Hokayem, Kevin McKinney, Emily Wiemers, Sisi Zhang & James Ziliak

There is a large literature on earnings and income volatility in labor economics, household finance, and macroeconomics. One strand of that literature has studied whether individual earnings volatility has risen or fallen in the United States over the last several decades. There are strong disagreements in the empirical literature on this important question, with some studies showing upward trends, some showing downward trends, and some showing no trends. Some studies have suggested that the differences are the result of using flawed survey data instead of more accurate administrative data. This article summarizes the results of a project attempting to reconcile these findings with four different datasets and six different data series—three survey and three administrative data series, including two which match survey respondent data to their administrative data. Using common specifications, measures of volatility, and other treatments of the data, four of the six data series show a lack of any significant long-term trend in male earnings volatility over the last 20-to-30+ years when differences across the datasets are properly accounted for. A fifth data series (the PSID) shows a positive net trend but small in magnitude. A sixth, administrative, dataset, available only since 1998, shows no net trend 1998–2011 and only a small decline thereafter. Many of the remaining differences across data series can be explained by differences in their cross-sectional distribution of earnings, particularly differences in the size of the lower tail. We conclude that the datasets we have analyzed, which include many of the most important available, show little evidence of any significant trend in male earnings volatility since the mid-1980s.

在勞動經濟學、家庭金融和宏觀經濟學中，有大量關于收入和收入波動的文獻。其中一組文獻研究了美國過去幾十年個人收入波動性是上升還是下降。關于這一重要問題的實證文獻存在著強烈的分歧，有的研究呈現(xiàn)上升趨勢，有的研究呈現(xiàn)下降趨勢，有的研究沒有趨勢。一些研究表明，造成這種差異的原因是使用了有缺陷的調查數(shù)據(jù)，而不是更準確的管理數(shù)據(jù)。本文總結了一個項目的結果，該項目試圖用四個不同的數(shù)據(jù)集和六個不同的數(shù)據(jù)系列來協(xié)調這些發(fā)現(xiàn)-三個調查和三個行政數(shù)據(jù)系列，其中兩個將調查對象的數(shù)據(jù)與其行政數(shù)據(jù)進行匹配。使用共同的規(guī)格、波動性的衡量和對數(shù)據(jù)的其他處理，六個數(shù)據(jù)系列中的四個顯示，在適當考慮到數(shù)據(jù)集之間的差異時，過去20至30多年來，男性收入波動性缺乏任何顯著的長期趨勢。第五個數(shù)據(jù)系列(PSID)顯示了積極的凈趨勢，但規(guī)模較小。第6個數(shù)據(jù)集(行政數(shù)據(jù)集)僅在1998年之后提供，顯示1998 - 2011年沒有凈趨勢，此后只有小幅下降。數(shù)據(jù)序列之間的許多剩余差異可以用它們的橫截面收入分布的差異來解釋，特別是下尾大小的差異。我們得出的結論是，我們分析的數(shù)據(jù)集(包括許多最重要的可用數(shù)據(jù))幾乎沒有證據(jù)表明，自20世紀80年代中期以來，男性收入波動出現(xiàn)了任何顯著趨勢。

?

?

?

2.Trends in Earnings Volatility Using Linked Administrative and Survey Data

使用關聯(lián)管理和調查數(shù)據(jù)的盈利波動趨勢

James P. Ziliak, Charles Hokayem & Christopher R. Bollinger

We document trends in earnings volatility separately by gender using unique linked survey data from the CPS ASEC and Social Security earnings records for the tax years spanning 1995–2015. The exact data link permits us to focus on differences in measured volatility from earnings nonresponse, survey attrition, and measurement between survey and administrative earnings data reports, while holding constant the sampling frame. Our results for both men and women suggest that the level and trend in volatility is similar in the survey and administrative data, showing substantial business-cycle sensitivity among men but no overall trend among continuous workers, while women demonstrate no change in earnings volatility over the business cycle but a declining trend. A substantive difference emerges with the inclusion of imputed earnings among survey nonrespondents, suggesting that users of the ASEC drop earnings nonrespondents.

我們使用CPS ASEC和1995-2015納稅年度社會保障收入記錄的獨特關聯(lián)調查數(shù)據(jù)，按性別分別記錄了收入波動趨勢。準確的數(shù)據(jù)鏈接使我們能夠在保持抽樣框架不變的情況下，專注于從盈余不回應、調查減員以及調查和行政盈余數(shù)據(jù)報告之間的測量波動的差異。我們對男性和女性的研究結果表明，調查和管理數(shù)據(jù)中的波動水平和趨勢相似，男性的收入波動在商業(yè)周期中具有很大的敏感性，但在連續(xù)工作者中沒有總體趨勢，而女性的收入波動在商業(yè)周期中沒有變化，而是呈下降趨勢。在納入未被調查對象的估算收入時，出現(xiàn)了實質性的差異，這表明ASEC的用戶減少了未被調查對象的收入。

?

?

?

3.Estimating Trends in Male Earnings Volatility with the Panel Study of Income Dynamics

用收入動態(tài)的小組研究估計男性收入波動的趨勢

Robert Moffitt & Sisi Zhang

The Panel Study of Income Dynamics (PSID) has been the workhorse dataset used to estimate trends in U.S. earnings volatility at the individual level. We provide updated estimates for male earnings volatility using additional years of data. The analysis confirms prior work showing upward trends in the 1970s and 1980s, with a near doubling of the level of volatility over that period. The results also confirm prior work showing a resumption of an upward trend starting in the 2000s, but the new years of data available show volatility to be falling in recent years. By 2018, volatility had grown by a modest amount relative to the 1990s, with a growth rate only one-fifth the magnitude of that in the 1970s and 1980s. We show that neither attrition or item nonresponse bias, nor other issues with the PSID, affect these conclusions.

收入動態(tài)面板研究(PSID)一直是用于估計美國個人層面盈利波動趨勢的主力數(shù)據(jù)集。我們使用額外年份的數(shù)據(jù)提供男性收入波動的最新估計。該分析證實了之前的研究顯示，20世紀70年代和80年代出現(xiàn)了上升趨勢，在此期間的波動水平幾乎翻了一番。研究結果還證實了之前的研究結果，即從2000年代開始，美國經濟恢復了上升趨勢，但可獲得的新一年數(shù)據(jù)顯示，近年來波動性正在下降。到2018年，相對于上世紀90年代，波動性略有增長，增速僅為上世紀70年代和80年代的五分之一。我們表明，無論是磨損或項目無反應偏差，或PSID的其他問題，都不會影響這些結論。

?

?

4.Reconciling Trends in Male Earnings Volatility: Evidence from the SIPP Survey and Administrative Data

調和男性收入波動的趨勢:來自SIPP調查和行政數(shù)據(jù)的證據(jù)

Michael D. Carr, Robert A. Moffitt & Emily E. Wiemers

As part of a set of papers using the same methods and sample selection criteria to estimate trends in male earnings volatility across survey and administrative datasets, we conduct a new investigation of male earnings volatility using data from the Survey of Income and Program Participation (SIPP) survey and SIPP-linked administrative earnings data (SIPP GSF). We find that the level of volatility is higher in the administrative earnings histories in the SIPP GSF than in the SIPP survey but that the trends are similar. Between 1984 and 2012, volatility in the SIPP survey declines slightly while volatility in the SIPP GSF increases slightly. Including imputations due to unit nonresponse in the SIPP survey data increases both the level and upward trend in volatility and poses a challenge for estimating a consistent series in the SIPP survey data. Because the density of low earnings differs considerably across datasets, and volatility may vary across the earnings distribution, we also estimate trends in volatility where we hold the earnings distribution fixed across the two data sources. Differences in the underlying earnings distribution explain much of the difference in the level of and trends in volatility between the SIPP survey and SIPP GSF.

作為一套使用相同方法和樣本選擇標準來估計調查和管理數(shù)據(jù)集男性收入波動趨勢的論文的一部分，我們使用收入和項目參與調查(SIPP)和SIPP相關管理收入數(shù)據(jù)(SIPP GSF)的數(shù)據(jù)對男性收入波動進行了新的調查。我們發(fā)現(xiàn)，與SIPP調查相比，SIPP GSF的行政盈余歷史波動水平更高，但趨勢相似。1984—2012年期間，SIPP調查的波動性略有下降，而SIPP GSF的波動性略有上升。在SIPP調查數(shù)據(jù)中加入單位無響應的估算會增加波動水平和上升趨勢，并對SIPP調查數(shù)據(jù)中一致性序列的估計提出了挑戰(zhàn)。由于低收益的密度在不同數(shù)據(jù)集之間存在很大差異，而波動性可能在不同的盈利分布之間存在差異，我們還在兩個數(shù)據(jù)源之間保持盈利分布固定的情況下估計波動性的趨勢?；A收益分配的差異在很大程度上解釋了SIPP調查和SIPP GSF之間波動水平和趨勢的差異。

?

?

5.Male Earnings Volatility in LEHD Before, During, and After the Great Recession

在大衰退之前、期間和之后，LEHD的男性收入波動

Kevin L. McKinney & John M. Abowd

This article is part of a coordinated collection of papers on prime-age male earnings volatility. Each paper produces a similar set of statistics for the same reference population using a different primary data source. Our primary data source is the Census Bureau’s Longitudinal Employer-Household Dynamics (LEHD) infrastructure files. Using LEHD data from 1998 to 2016, we create a well-defined population frame to facilitate accurate estimation of temporal changes comparable to designed longitudinal samples of people. We show that earnings volatility, excluding increases during recessions, has declined over the analysis period, a finding robust to various sensitivity analyses.

本文是關于壯年男性收入波動的一系列論文的一部分。每篇論文都使用不同的主要數(shù)據(jù)源對相同的參考人口產生了一組類似的統(tǒng)計數(shù)據(jù)。我們的主要數(shù)據(jù)來源是人口普查局的縱向雇主-家庭動態(tài)(LEHD)基礎設施文件。利用1998年至2016年的LEHD數(shù)據(jù)，我們創(chuàng)建了一個定義良好的人口框架，以促進與設計的縱向人群樣本相比較的時間變化的準確估計。我們發(fā)現(xiàn)，除去經濟衰退期間的增長，盈利波動性在分析期間有所下降，這一發(fā)現(xiàn)對各種敏感性分析都是穩(wěn)健的。

?

?

6.Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: A General Dynamic Factor Approach

高維時間序列中條件協(xié)方差矩陣的預測:一般動態(tài)因子方法

Carlos Trucíos, Jo?o H. G. Mazzeu, Marc Hallin, Luiz K. Hotta, Pedro L. Valls Pereira & Mauricio Zevallos

Based on a General Dynamic Factor Model with infinite-dimensional factor space and MGARCH volatility models, we develop new estimation and forecasting procedures for conditional covariance matrices in high-dimensional time series. The finite-sample performance of our approach is evaluated via Monte Carlo experiments and outperforms the most alternative methods. This new approach is also used to construct minimum one-step-ahead variance portfolios for a high-dimensional panel of assets. The results are shown to match the results of recent proposals by Engle, Ledoit, and Wolf and achieve better out-of-sample portfolio performance than alternative procedures proposed in the literature.

基于具有無限維因子空間的一般動態(tài)因子模型和MGARCH波動率模型，我們開發(fā)了高維時間序列條件協(xié)方差矩陣的估計和預測方法。我們的方法的有限樣本性能通過蒙特卡羅實驗進行評估，并優(yōu)于大多數(shù)替代方法。這種新方法也被用于構造一個高維資產面板的最小一步前方差投資組合。結果顯示，與Engle、Ledoit和Wolf最近的建議相匹配，并取得了比文獻中提出的替代程序更好的樣本外投資組合業(yè)績。

?

?

?

7.Volatility Estimation When the Zero-Process is Nonstationary

零過程非平穩(wěn)時的波動率估計

Christian Francq & Genaro Sucarrat

Financial returns are frequently nonstationary due to the nonstationary distribution of zeros. In daily stock returns, for example, the nonstationarity can be due to an upwards trend in liquidity over time, which may lead to a downwards trend in the zero-probability. In intraday returns, the zero-probability may be periodic: It is lower in periods where the opening hours of the main financial centers overlap, and higher otherwise. A nonstationary zero-process invalidates standard estimators of volatility models, since they rely on the assumption that returns are strictly stationary. We propose a GARCH model that accommodates a nonstationary zero-process, derive a zero-adjusted QMLE for the parameters of the model, and prove its consistency and asymptotic normality under mild assumptions. The volatility specification in our model can contain higher order ARCH and GARCH terms, and past zero-indicators as covariates. Simulations verify the asymptotic properties in finite samples, and show that the standard estimator is biased. An empirical study of daily and intradaily returns illustrate our results. They show how a nonstationary zero-process induces time-varying parameters in the conditional variance representation, and that the distribution of zero returns can have a strong impact on volatility predictions.

由于零的非平穩(wěn)分布，財務回報往往是非平穩(wěn)的。例如，在每日股票收益中，非平穩(wěn)性可能是由于隨著時間的推移，流動性呈上升趨勢，這可能導致零概率呈下降趨勢。在日內回報率中，零概率可能是周期性的:在主要金融中心開放時間重疊的時期，零概率較低，反之則較高。非平穩(wěn)零過程使波動率模型的標準估計失效，因為它們依賴于收益是嚴格平穩(wěn)的假設。本文提出了一個適應非平穩(wěn)零過程的GARCH模型，給出了模型參數(shù)的零調整QMLE，并證明了該模型在溫和假設下的一致性和漸近正態(tài)性。在我們的模型中，波動率規(guī)范可以包含更高階的ARCH和GARCH項，以及過去的零指標作為協(xié)變量。仿真驗證了有限樣本下的漸近性質，并證明了標準估計是有偏的。對每日和每日內部回報的實證研究說明了我們的結果。它們展示了非平穩(wěn)零過程如何在條件方差表示中誘導時變參數(shù)，以及零回報的分布可能對波動率預測產生強烈影響。

?

?

8.Composite Index Construction with Expert Opinion

結合專家意見構建綜合指數(shù)

Rong Chen, Yuanyuan Ji, Guolin Jiang, Han Xiao, Ruoqing Xie & Pingfang Zhu

Composite index is a powerful and popularly used tool in providing an overall measure of a subject by summarizing a group of measurements (component indices) of different aspects of the subject. It is widely used in economics, finance, policy evaluation, performance ranking, and many other fields. Effective construction of a composite index has been studied extensively. The most widely used approach is to use a linear combination of the component indices, where the combination weights are determined by optimizing an objective function. To maximize the overall variation of the resulting composite index, the combination weights can be obtained through principal component analysis. In this article, we propose to incorporate expert opinions into the construction of the composite index. It is noted that expert opinion often provides useful information in assessing which of the component indices are more important for the overall measure of the subject. We consider the case that a group of experts have been consulted, each providing a set of importance scores for the component indices, along with a set of confidence scores which reflects the expert’s own confidence in his/her assessment. In addition, the constructor of the composite index can also provide an assessment of the expertise level of each expert. We use linear combinations to construct the composite index, where the combination weights are determined by maximizing the sum of resulting composite index variation and the negative weighted sum of squares of deviation between the combination weights used and the experts’ scores. A data-driven approach is used to find the optimal balance between the two sources of information. Theoretical properties of the procedure are investigated. Simulation examples and an economic application on constructing science and technology development index is carried out to illustrate the proposed method.

綜合指數(shù)是一種功能強大且被廣泛使用的工具，它通過匯總一組主體不同方面的度量(組成指數(shù))來提供對主體的總體度量。它被廣泛應用于經濟、金融、政策評價、績效排名等諸多領域。綜合指數(shù)的有效構建已被廣泛研究。最廣泛使用的方法是使用組成指標的線性組合，其中組合權重通過優(yōu)化目標函數(shù)來確定。為了使得到的綜合指標的整體變化最大化，可以通過主成分分析得到組合權重。在本文中，我們建議將專家意見納入到綜合指數(shù)的構建中。有人指出，專家意見往往提供有用的資料，以評估哪些組成指數(shù)對該問題的全面衡量更為重要。我們認為，已經咨詢了一組專家，每個專家都為組成指數(shù)提供了一套重要分數(shù)，以及一套信心分數(shù)，這反映了專家自己對其評估的信心。此外，綜合指數(shù)的構造者還可以對每個專家的專業(yè)水平進行評估。我們使用線性組合來構建綜合指標，其中組合權重是通過最大化所得到的綜合指標變異和所使用的組合權重與專家得分的負加權偏差平方和來確定的。數(shù)據(jù)驅動方法用于在兩個信息源之間找到最佳平衡。研究了該過程的理論性質。通過仿真實例和構建科技發(fā)展指數(shù)的經濟應用來說明所提出的方法。

?

?

9.Panel Stochastic Frontier Model With Endogenous Inputs and Correlated Random Components

具有內生輸入和相關隨機分量的面板隨機前沿模型

Lai Hung-pin & Subal C. Kumbhakar

In this article, we consider a panel stochastic frontier model in which the composite error term εit has four components, that is, εit=τi?ηi+vit?uit, where ηi and uit are persistent and transient inefficiency components, τi consists of the random firm effects and vit is the random noise. Two distinguishing features of the proposed model are (i) the inputs are allowed to be correlated with one or more of the error components in the production function; (ii) time-invariant and time-varying components, that is, (τi?ηi) and (vit?uit), are allowed to be correlated. To keep the formulation general, we do not specify whether this correlation comes from the correlations between (i) ηi and uit, (ii) τi and uit, (iii) τi and vit, (iv) ηi and vit, or some other combination of them. Further, we also consider the case when the correlation in the composite error arises from the time dependence of εit. To estimate the model parameters and predict (in)efficiency, we propose a two-step procedure. In the first step, either the within or the first difference transformation that eliminates the time-invariant components is proposed. We then use either the 2SLS or the GMM approach to obtain unbiased and consistent estimators of the parameters in the frontier function, except for the intercept. In the second step, the maximum simulated likelihood method is used to estimate the parameters associated with the distributions of τi and vit, ηi and uit as well as the intercept. The copula approach is used in this step to model the dependence between the time-varying and time-invariant components. Formulas to predict transient and persistent (in)efficiency are also derived. Finally, results from both simulated and real data are provided.

在本文中，我們考慮一個面板隨機前沿模型，其中復合誤差項εit有四個分量，即εit=τi - ηi+vit - uit，其中ηi和uit是持續(xù)的和瞬態(tài)的無效率分量，τi由隨機企業(yè)效應組成，vit是隨機噪聲。該模型的兩個顯著特征是(i)輸入允許與生產函數(shù)中的一個或多個誤差分量相關;(ii)允許時不變和時變分量，即(τi - ηi)和(vit - uit)相關。為了使公式一般化，我們沒有具體說明這種相關性是否來自(i) ηi和uit， (ii) τi和uit， (iii) τi和vit， (iv) ηi和vit，或它們的其他組合。此外，我們還考慮了復合誤差中的相關性是由εit的時間依賴性引起的情況。為了估計模型參數(shù)和預測(in)效率，我們提出了一個兩步程序。第一步，提出了消除時不變分量的內差分變換和一階差分變換。然后，我們使用2SLS或GMM方法來獲得除截距外的邊界函數(shù)參數(shù)的無偏和一致估計。第二步，利用最大似然法估計τi和vit、ηi和uit分布的相關參數(shù)以及截距。在這一步中使用copula方法來建模時變和時不變分量之間的依賴關系。推導了瞬態(tài)和持續(xù)(in)效率的計算公式。最后給出了模擬和實際數(shù)據(jù)的結果。

?

?

10.Optimal Covariate Balancing Conditions in Propensity Score Estimation

傾向得分估計中的最優(yōu)協(xié)變量平衡條件

Jianqing Fan, Kosuke Imai, Inbeom Lee, Han Liu, Yang Ning & Xiaolin Yang

Inverse probability of treatment weighting (IPTW) is a popular method for estimating the average treatment effect (ATE). However, empirical studies show that the IPTW estimators can be sensitive to the misspecification of the propensity score model. To address this problem, researchers have proposed to estimate propensity score by directly optimizing the balance of pretreatment covariates. While these methods appear to empirically perform well, little is known about how the choice of balancing conditions affects their theoretical properties. To fill this gap, we first characterize the asymptotic bias and efficiency of the IPTW estimator based on the covariate balancing propensity score (CBPS) methodology under local model misspecification. Based on this analysis, we show how to optimally choose the covariate balancing functions and propose an optimal CBPS-based IPTW estimator. This estimator is doubly robust; it is consistent for the ATE if either the propensity score model or the outcome model is correct. In addition, the proposed estimator is locally semiparametric efficient when both models are correctly specified. To further relax the parametric assumptions, we extend our method by using a sieve estimation approach. We show that the resulting estimator is globally efficient under a set of much weaker assumptions and has a smaller asymptotic bias than the existing estimators. Finally, we evaluate the finite sample performance of the proposed estimators via simulation and empirical studies. An open-source software package is available for implementing the proposed methods.

處理加權反概率法(IPTW)是估計平均處理效應(ATE)的一種常用方法。然而，實證研究表明，IPTW估計量對傾向評分模型的錯誤描述很敏感。針對這一問題，研究者提出通過直接優(yōu)化預處理協(xié)變量的平衡來估計傾向得分。雖然這些方法似乎在經驗上表現(xiàn)良好，但很少知道平衡條件的選擇如何影響它們的理論性質。為了填補這一空白，我們首先描述了在局部模型錯誤描述下，基于協(xié)變量平衡傾向得分(CBPS)方法的IPTW估計器的漸近偏差和效率。在此基礎上，我們展示了如何最優(yōu)地選擇協(xié)變量平衡函數(shù)，并提出了一個最優(yōu)的基于cbps的IPTW估計器。該估計量具有雙重魯棒性;如果傾向得分模型或結果模型都是正確的，則對ATE來說是一致的。此外，當兩個模型都被正確指定時，所提出的估計是局部半?yún)?shù)有效的。為了進一步放寬參數(shù)假設，我們使用了一個篩估計方法來擴展我們的方法。我們證明了所得到的估計量在一組更弱的假設下是全局有效的，并且有一個比現(xiàn)有估計量更小的漸近偏差。最后，我們通過仿真和實證研究評估了所提出的估計器的有限樣本性能。一個開源軟件包可用于實現(xiàn)所提出的方法。

?

?

?

11.Testing Error Distribution by Kernelized Stein Discrepancy in Multivariate Time Series Models

多元時間序列模型中誤差分布的核化Stein方差檢驗

Donghang Luo, Ke Zhu, Huan Gong & Dong Li

Knowing the error distribution is important in many multivariate time series applications. To alleviate the risk of error distribution mis-specification, testing methodologies are needed to detect whether the chosen error distribution is correct. However, the majority of existing tests only deal with the multivariate normal distribution for some special multivariate time series models, and thus cannot be used for testing the often observed heavy-tailed and skewed error distributions in applications. In this article, we construct a new consistent test for general multivariate time series models, based on the kernelized Stein discrepancy. To account for the estimation uncertainty and unobserved initial values, a bootstrap method is provided to calculate the critical values. Our new test is easy-to-implement for a large scope of multivariate error distributions, and its importance is illustrated by simulated and real data. As an extension, we also show how to test for the error distribution in copula time series models.

在許多多元時間序列的應用中，了解誤差分布是很重要的。為了減輕錯誤分布不規(guī)范的風險，需要測試方法來檢測所選擇的錯誤分布是否正確。然而，現(xiàn)有的檢驗方法大多只處理一些特殊的多元時間序列模型的多元正態(tài)分布，無法對應用中經常觀測到的重尾和偏態(tài)誤差分布進行檢驗。本文基于核化斯坦差異構造了一種適用于一般多元時間序列模型的一致性檢驗。為了考慮到估計的不確定性和不可觀測的初始值，提供了一種bootstrap方法來計算臨界值。我們的新測試方法易于在大范圍的多元誤差分布中實現(xiàn)，并通過模擬和真實數(shù)據(jù)說明了它的重要性。作為一個擴展，我們還展示了如何在copula時間序列模型中測試誤差分布。

?

?

?

12.Inference in Sparsity-Induced Weak Factor Models

稀疏性誘導的弱因子模型中的推理

Yoshimasa Uematsu & Takashi Yamagata

In this article, we consider statistical inference for high-dimensional approximate factor models. We posit a weak factor structure, in which the factor loading matrix can be sparse and the signal eigenvalues may diverge more slowly than the cross-sectional dimension, N. We propose a novel inferential procedure to decide whether each component of the factor loadings is zero or not, and prove that this controls the false discovery rate (FDR) below a preassigned level, while the power tends to unity. This “factor selection” procedure is primarily based on a debiased version of the sparse orthogonal factor regression (SOFAR) estimator; but is also applicable to the principal component (PC) estimator. After the factor selection, the resparsified SOFAR and sparsified PC estimators are proposed and their consistency is established. Finite sample evidence supports the theoretical results. We apply our method to the FRED-MD dataset of macroeconomic variables and the monthly firm-level excess returns which constitute the S&P 500 index. The results give very strong statistical evidence of sparse factor loadings under the identification restrictions and exhibit clear associations of factors and categories of the variables. Furthermore, our method uncovers a very weak but statistically significant factor in the residuals of Fama-French five factor regression.

在本文中，我們考慮高維近似因子模型的統(tǒng)計推斷。我們假設了一個弱因子結構，其中因子載荷矩陣可以是稀疏的，信號特征值可能比橫截面維n發(fā)散得更慢。我們提出了一個新的推理程序來決定因子載荷的每個組成部分是否為零，并證明了這將控制錯誤發(fā)現(xiàn)率(FDR)低于預先指定的水平，而功率趨于統(tǒng)一。這個“因子選擇”過程主要基于稀疏正交因子回歸(SOFAR)估計的去偏版本;但也適用于主成分(PC)估計。在因子選擇之后，提出了重分類SOFAR和稀疏PC估計量，并建立了它們的一致性。有限樣本證據(jù)支持理論結果。我們將我們的方法應用于FRED-MD宏觀經濟變量數(shù)據(jù)集和構成標準普爾500指數(shù)的公司層面月度超額收益。結果給出了非常強大的統(tǒng)計證據(jù)，稀疏的因素載荷下識別限制，并顯示出明確的關聯(lián)因素和類別的變量。此外，我們的方法在Fama-French五因子回歸的殘差中發(fā)現(xiàn)了一個非常弱但在統(tǒng)計上顯著的因素。

?

?

?

13.Optimal Shrinkage-Based Portfolio Selection in High Dimensions

高維下基于收縮的最優(yōu)投資組合選擇

Taras Bodnar, Yarema Okhrin & Nestor Parolya

In this article, we estimate the mean-variance portfolio in the high-dimensional case using the recent results from the theory of random matrices. We construct a linear shrinkage estimator which is distribution-free and is optimal in the sense of maximizing with probability 1 the asymptotic out-of-sample expected utility, that is, mean-variance objective function for different values of risk aversion coefficient which in particular leads to the maximization of the out-of-sample expected utility and to the minimization of the out-of-sample variance. One of the main features of our estimator is the inclusion of the estimation risk related to the sample mean vector into the high-dimensional portfolio optimization. The asymptotic properties of the new estimator are investigated when the number of assets p and the sample size n tend simultaneously to infinity such that p/n→c∈(0,+∞). The results are obtained under weak assumptions imposed on the distribution of the asset returns, namely the existence of the 4+ε moments is only required. Thereafter we perform numerical and empirical studies where the small- and large-sample behavior of the derived estimator is investigated. The suggested estimator shows significant improvements over the existent approaches including the nonlinear shrinkage estimator and the three-fund portfolio rule, especially when the portfolio dimension is larger than the sample size. Moreover, it is robust to deviations from normality.

在本文中，我們利用隨機矩陣理論的最新結果來估計高維情況下的平均方差投資組合。我們構造了一個線性收縮估計，它是無分布的，并且在以概率1最大化漸近樣本外期望效用的意義上是最優(yōu)的，即不同風險規(guī)避系數(shù)值的均值-方差目標函數(shù)，它特別導致了樣本外期望效用的最大化和樣本外方差的最小化。我們的估計器的一個主要特征是將與樣本均值向量相關的估計風險納入高維投資組合優(yōu)化中。研究了當資產數(shù)量p和樣本容量n同時趨于無窮，使得p/n→c∈(0，+∞)時，新估計量的漸近性質。這一結果是在對資產收益率分布的弱假設下得到的，即只需要存在4+ε矩。隨后，我們進行了數(shù)值和實證研究，其中研究了導出的估計量的小樣本和大樣本行為。與已有的非線性收縮估計法和三基金組合規(guī)則估計法相比，所提出的估計方法有了明顯的改進，特別是當投資組合維數(shù)大于樣本量時。此外，該模型對偏離常態(tài)的情況具有較強的穩(wěn)健性。

?

?

?

14.Kernel Averaging Estimators

核平均估計量

Rong Zhu, Xinyu Zhang, Alan T. K. Wan & Guohua Zou

The issue of bandwidth selection is a fundamental model selection problem stemming from the uncertainty about the smoothness of the regression. In this article, we advocate a model averaging approach to circumvent the problem caused by this uncertainty. Our new approach involves averaging across a series of Nadaraya-Watson kernel estimators each under a different bandwidth, with weights for these different estimators chosen such that a least-squares cross-validation criterion is minimized. We prove that the resultant combined-kernel estimator achieves the smallest possible asymptotic aggregate squared error. The superiority of the new estimator over estimators based on widely accepted conventional bandwidth choices in finite samples is demonstrated in a simulation study and a real data example.

帶寬選擇問題是一個基本的模型選擇問題，它源于回歸的平滑性的不確定性。在本文中，我們提出了一種模型平均方法來規(guī)避這種不確定性帶來的問題。我們的新方法涉及在不同帶寬下對一系列Nadaraya-Watson核估計進行平均，為這些不同的估計選擇權重，以便最小化最小二乘交叉驗證準則。我們證明了所得到的組合核估計達到了最小的可能漸近聚集平方誤差。在一個仿真研究和一個真實的數(shù)據(jù)例子中，我們證明了新的估計器相對于在有限樣本中廣泛接受的傳統(tǒng)帶寬選擇的估計器的優(yōu)越性。

?

?

15.Time Series Approach to the Evolution of Networks: Prediction and Estimation

網絡演化的時間序列方法:預測和估計

Anna Bykhovskaya

The article analyzes nonnegative multivariate time series which we interpret as weighted networks. We introduce a model where each coordinate of the time series represents a given edge across time. The number of time periods is treated as large compared to the size of the network. The model specifies the temporal evolution of a weighted network that combines classical autoregression with nonnegativity, a positive probability of vanishing, and peer effect interactions between weights assigned to edges in the process. The main results provide criteria for stationarity versus explosiveness of the network evolution process and techniques for estimation of the parameters of the model and for prediction of its future values. Natural applications arise in networks of fixed number of agents, such as countries, large corporations, or small social communities. The article provides an empirical implementation of the approach to monthly trade data in European Union. Overall, the results confirm that incorporating nonnegativity of dependent variables into the model matters and incorporating peer effects leads to the improved prediction power.

本文分析了非負的多元時間序列，并將其解釋為加權網絡。我們引入了一個模型，其中時間序列的每個坐標表示跨時間的給定邊。與網絡的規(guī)模相比，時間周期的數(shù)量被視為較大的。該模型指定了一個加權網絡的時間演變，該網絡結合了經典自回歸與非負性、消失的正概率以及在此過程中分配給邊緣的權值之間的同伴效應相互作用。研究結果為網絡演化過程的平穩(wěn)性和爆發(fā)性提供了標準，為模型參數(shù)的估計和未來數(shù)值的預測提供了技術依據(jù)。自然應用出現(xiàn)在固定數(shù)量的代理網絡中，如國家、大公司或小型社會社區(qū)。本文對歐盟的月度貿易數(shù)據(jù)進行了實證分析。總體而言，結果證實了將因變量的非負性納入模型具有重要意義，并將同群效應納入模型能夠提高預測能力。

?

?

16.Test for Market Timing Using Daily Fund Returns

利用每日基金收益測試市場擇時

Lei Jiang, Weimin Liu & Liang Peng

Using daily mutual fund returns to estimate market timing, some econometric issues, including heteroscedasticity, correlated errors, and heavy tails, make the traditional least-squares estimate in Treynor–Mazuy and Henriksson–Merton models biased and severely distort the t-test size. Using ARMA-GARCH models, weighted least-squares estimate to ensure a normal limit, and random weighted bootstrap method to quantify uncertainty, we find more funds with positive timing ability than the Newey–West t-test. Empirical evidence indicates that funds with perverse timing ability have high fund turnovers and funds tradeoff between timing and stock picking skills.

摘要利用共同基金日收益來估計市場時機，由于存在異方差、相關誤差和重尾等計量經濟學問題，使得傳統(tǒng)的Treynor-Mazuy模型和Henriksson-Merton模型的最小二乘估計存在偏差，嚴重扭曲了t檢驗的大小。本文利用ARMA-GARCH模型、加權最小二乘估計和隨機加權bootstrap方法來量化基金的不確定性，結果表明，相對于Newey-West t檢驗，基金更具有正擇時能力。實證結果表明，擇時能力較差的基金具有較高的基金周轉率和擇時與選股能力之間的權衡。

?

?

17.Survey Response Behavior as a Proxy for Unobserved Ability: Theory and Evidence

作為不可觀察能力的代表的調查反應行為:理論和證據(jù)

Sonja C. de New & Stefanie Schurer

An emerging literature is experimenting with using survey response behavior as a proxy for hard-to-measure abilities. We contribute to this literature by formalizing this idea and evaluating its benefits and risks. Using a standard and nationally representative survey from Australia, we demonstrate that the survey item-response rate (SIRR), a straightforward summary measure of response behavior, varies more with cognitive than with noncognitive ability. We evaluate whether SIRR is a useful proxy to reduce ability-related biases in a standard economic application. We show empirically that SIRR, although a weak and imperfect proxy, leads to omitted-variable bias reductions of up to 20%, and performs better than other proxy variables derived from paradata. Deriving the necessary and sufficient conditions for a valid proxy, we show that a strong proxy is neither a necessary nor a sufficient condition to reduce estimation biases. A critical consideration is to which degree the proxy introduces a multicollinearity problem, a finding of general interest. We illustrate the theoretical derivations with an empirical application.

一種新興的文獻正在嘗試使用調查反應行為作為難以衡量的能力的代理。我們通過將這一想法正式化并評估其收益和風險來貢獻這一文獻。使用來自澳大利亞的一項標準的、具有全國代表性的調查，我們證明了調查項目反應率(SIRR)，一種直接的反應行為的總結測量，在認知能力方面的差異大于非認知能力。我們評估SIRR是否在標準經濟應用中是減少能力相關偏差的有用代理。我們的經驗表明，SIRR雖然是一個弱的和不完美的代理變量，但導致遺漏變量偏差降低高達20%，并優(yōu)于其他從paradata派生的代理變量。通過推導有效代理的充要條件，我們證明了強代理既不是減少估計偏差的必要條件，也不是充分條件。一個關鍵的考慮是，代理在多大程度上引入了多重共線性問題，一個普遍感興趣的發(fā)現(xiàn)。我們舉例說明了理論推導與經驗應用。

?

?

18.Estimation of Sparsity-Induced Weak Factor Models

稀疏性誘發(fā)的弱因子模型的估計

Yoshimasa Uematsu & Takashi Yamagata

This article investigates estimation of sparsity-induced weak factor (sWF) models, with large cross-sectional and time-series dimensions (N and T, respectively). It assumes that the kth largest eigenvalue of a data covariance matrix grows proportionally to Nαk with unknown exponents 0<αk≤1 for k=1,…,r. Employing the same rotation of the principal components (PC) estimator, the growth rate αk is linked to the degree of sparsity of kth factor loadings. This is much weaker than the typical assumption on the recent factor models, in which all the r largest eigenvalues diverge proportionally to N. We apply the method of sparse orthogonal factor regression (SOFAR) by Uematsu et?al. (2019) to estimate the sWF models and derive the estimation error bound. Importantly, our method also yields consistent estimation of αk. A finite sample experiment shows that the performance of the new estimator uniformly dominates that of the PC estimator. We apply our method to forecasting bond yields and the results demonstrate that our method outperforms that based on the PC. We also analyze S&P500 firm security returns and find that the first factor is consistently near strong while the others are weak.

本文研究了具有大截面和時間序列維數(shù)(分別為N和T)的稀疏誘導弱因子(sWF)模型的估計。假設數(shù)據(jù)協(xié)方差矩陣的第k個最大特征值與Nαk成比例增長，未知指數(shù)為0&lt;當k=1，…，r時，αk≤1。利用主成分(PC)估計器的相同旋轉，增長率αk與第k個因子載荷的稀疏程度相聯(lián)系。這比最近的因子模型中所有r最大特征值與n成比例發(fā)散的典型假設要弱得多。重要的是，我們的方法也得到了αk的一致估計。有限樣本實驗表明，新估計器的性能一致優(yōu)于原估計器。我們將我們的方法應用于債券收益率的預測，結果表明，我們的方法優(yōu)于基于PC的方法。我們還分析了標準普爾500指數(shù)(S&P500)公司的證券回報，發(fā)現(xiàn)第一個因素一直接近強勢，而其他因素則較弱。

?

?

?

19.Testing for Structural Change of Predictive Regression Model to Threshold Predictive Regression Model

預測回歸模型向閾值預測回歸模型的結構變化檢驗

Fukang Zhu, Mengya Liu, Shiqing Ling & Zongwu Cai

This article investigates two test statistics for testing structural changes and thresholds in predictive regression models. The generalized likelihood ratio (GLR) test is proposed for the stationary predictor and the generalized F test is suggested for the persistent predictor. Under the null hypothesis of no structural change and threshold, it is shown that the GLR test statistic converges to a function of a centered Gaussian process, and the generalized F test statistic converges to a function of Brownian motions. A Bootstrap method is proposed to obtain the critical values of test statistics. Simulation studies and a real example are given to assess the performances of the proposed tests.

本文研究了預測回歸模型中檢驗結構變化和閾值的兩個檢驗統(tǒng)計量。對平穩(wěn)預測量采用廣義似然比(GLR)檢驗，對持續(xù)預測量采用廣義F檢驗。在無結構變化和閾值的零假設下，GLR檢驗統(tǒng)計量收斂于中心高斯過程的函數(shù)，廣義F檢驗統(tǒng)計量收斂于布朗運動的函數(shù)。提出了一種Bootstrap方法來獲取檢驗統(tǒng)計量的臨界值。通過仿真研究和實例驗證了所提出的測試方法的性能。

?

?

?

20.Bootstrap Tests for High-Dimensional White-Noise

高維白噪聲的Bootstrap檢驗

Lengyang Wang, Efang Kong & Yingcun Xia

The testing of white-noise (WN) is an essential step in time series analysis. In a high dimensional set-up, most existing methods either are computationally infeasible, or suffer from highly distorted Type-I errors, or both. We propose an easy-to-implement bootstrap method for high-dimensional WN test and prove its consistency for a variety of test statistics. Its power properties as well as extensions to WN tests based on fitted residuals are also considered. Simulation results show that compared to the existing methods, the new approach possesses much better power, while maintaining a proper control over the Type-I error. They also provide proofs that even in cases where our method is expected to suffer from lack of theoretical justification, it continues to outperform its competitors. The proposed method is applied to the analysis of the daily stock returns of the top 50 companies by market capitalization listed on the NYSE, and we find strong evidence that the common market factor is the main cause of cross-correlation between stocks.

白噪聲檢驗是時間序列分析中必不可少的步驟。在高維設置中，大多數(shù)現(xiàn)有的方法要么在計算上不可行的，要么遭受高度扭曲的第一類錯誤，或者兩者兼有。提出了一種易于實現(xiàn)的高維WN檢驗bootstrap方法，并證明了該方法對多種檢驗統(tǒng)計量的一致性。它的功率特性以及基于擬合殘差的WN測試的擴展也被考慮。仿真結果表明，與現(xiàn)有方法相比，新方法在保持對第一類錯誤的適當控制的同時，具有更好的功率。他們還提供了證據(jù)，證明即使在我們的方法預計會缺乏理論理由的情況下，它仍然優(yōu)于其競爭對手。將本文提出的方法應用于紐約證券交易所上市公司按市值計算的前50家公司的每日股票收益率分析，我們發(fā)現(xiàn)，共同市場因素是股票之間相互關聯(lián)的主要原因。

?

?

?

21.Extreme Value Estimation for Heterogeneous Data

異質性數(shù)據(jù)的極值估計

John H. J. Einmahl & Yi He

We develop a universal econometric formulation of empirical power laws possibly driven by parameter heterogeneity. Our approach extends classical extreme value theory to specifying the tail behavior of the empirical distribution of a general dataset with possibly heterogeneous marginal distributions. We discuss several model examples that satisfy our conditions and demonstrate in simulations how heterogeneity may generate empirical power laws. We observe a cross-sectional power law for the U.S. stock losses and show that this tail behavior is largely driven by the heterogeneous volatilities of the individual assets.

我們開發(fā)了一個普遍的計量經濟學公式的經驗權力法律可能驅動的參數(shù)異質性。我們的方法擴展了經典的極值理論，以指定尾部行為的經驗分布的一般數(shù)據(jù)集可能具有異質性的邊際分布。我們討論了幾個滿足我們條件的模型例子，并在模擬中演示了異質性如何產生經驗冪律。我們觀察到美國股票損失的橫截面冪律，并表明這種尾部行為在很大程度上是由單個資產的異質性波動驅動的。

?

?

?

22.Factor and Factor Loading Augmented Estimators for Panel Regression With Possibly Nonstrong Factors

可能存在非強因素的面板回歸的因子和因子負荷增強估計

Jad Beyhum & Eric Gautier

This article considers linear panel data models where the dependence of the regressors and the unobservables is modeled through a factor structure. The number of time periods and the sample size both go to infinity. Unlike in most existing methods for the estimation of this type of models, nonstrong factors are allowed and the number of factors can grow to infinity with the sample size. We study a class of two-step estimators of the regression coefficients. In the first step, factors and factor loadings are estimated. Then, the second step corresponds to the panel regression of the outcome on the regressors and the estimates of the factors and the factor loadings from the first step. The estimators enjoy double robustness. Different methods can be used in the first step while the second step is unique. We derive sufficient conditions on the first-step estimator and the data generating process under which the two-step estimator is asymptotically normal. Assumptions under which using an approach based on principal components analysis in the first step yields an asymptotically normal estimator are also given. The two-step procedure exhibits good finite sample properties in simulations. The approach is illustrated by an empirical application on fiscal policy.

本文考慮線性面板數(shù)據(jù)模型，其中回歸變量和不可觀察變量的依賴關系通過因子結構建模。時間周期的數(shù)量和樣本容量都趨于無窮大。與大多數(shù)現(xiàn)有的模型估計方法不同，非強因子是允許的，并且因子的數(shù)量可以隨著樣本量的增加而無限增長。研究了回歸系數(shù)的一類兩步估計。第一步，估計因子和因子載荷。然后，第二步對應第一步對回歸變量的結果和因子估計及因子載荷進行面板回歸。估計量具有雙重穩(wěn)健性。第一步可以使用不同的方法，而第二步是唯一的。給出了第一步估計量漸近正態(tài)的充分條件和兩步估計量漸近正態(tài)的數(shù)據(jù)生成過程。給出了第一步基于主成分分析的方法得到漸近正態(tài)估計的假設條件。兩步方法在模擬中顯示出良好的有限樣本特性。財政政策的實證應用說明了這種方法。