Journal of business & economic statistics.

Reconciling Trends in U.S. Male Earnings Volatility: Results from Survey and Administrative Data.-- Trends in Earnings Volatility Using Linked Administrative and Survey Data.--Estimating Trends in Male Earnings Volatility with the Panel Study of Income Dynamics.-- Reconciling Trends in Male Earnings Volatility: Evidence from the SIPP Survey and Administrative Data.-- Male Earnings Volatility in LEHD Before, During, and After the Great Recession.-- Forecasting Conditional Covariance Matrices in High-Dimensional Time Series: A General Dynamic Factor Approach.-- Volatility Estimation When the Zero-Process is Nonstationary.-- Composite Index Construction with Expert Opinion.-- Panel Stochastic Frontier Model With Endogenous Inputs and Correlated Random Components.-- Optimal Covariate Balancing Conditions in Propensity Score Estimation.-- Testing Error Distribution by Kernelized Stein Discrepancy in Multivariate Time Series Models.-- Inference in Sparsity-Induced Weak Factor Models.-- Optimal Shrinkage-Based Portfolio Selection in High Dimensions.-- Kernel Averaging Estimators.-- Time Series Approach to the Evolution of Networks: Prediction and Estimation.-- Test for Market Timing Using Daily Fund Returns.-- Survey Response Behavior as a Proxy for Unobserved Ability: Theory and Evidence.-- Estimation of Sparsity-Induced Weak Factor Models.-- Testing for Structural Change of Predictive Regression Model to Threshold Predictive Regression Model.-- Bootstrap Tests for High-Dimensional White-Noise.-- Extreme Value Estimation for Heterogeneous Data.-- Factor and Factor Loading Augmented Estimators for Panel Regression With Possibly Nonstrong Factors.

Article : Estimating Trends in Male Earnings Volatility with the Panel Study of Income Dynamics.Abstract
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.

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

Article: Male Earnings Volatility in LEHD Before, During, and After the Great Recession. Abstract
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.

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

Article : Volatility Estimation When the Zero-Process is Nonstationary. Abstract
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.

Article :Composite Index Construction with Expert Opinion. Abstract
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.

Article : Panel Stochastic Frontier Model With Endogenous Inputs and Correlated Random Components. Abstract
In this article, we consider a panel stochastic frontier model in which the composite error term
ε
i
t
has four components, that is,
ε
i
t
=
τ
i
−
η
i
+
v
i
t
−
u
i
t
, 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, (
τ
i
−
η
i
) and (
v
i
t
−
u
i
t
), 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
ε
i
t
. 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.

Article: Optimal Covariate Balancing Conditions in Propensity Score Estimation. Abstract
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.

Article : Testing Error Distribution by Kernelized Stein Discrepancy in Multivariate Time Series Models. Abstract
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.

Article : Inference in Sparsity-Induced Weak Factor Models. Abstract
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.

Article : Optimal Shrinkage-Based Portfolio Selection in High Dimensions.ABSTRACT
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.

Article : Kernel Averaging Estimators. Abstract
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.

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

Article : Test for Market Timing Using Daily Fund Returns. Abstract
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.

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

Article : Estimation of Sparsity-Induced Weak Factor Models. Abstract
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
N
α
k
with unknown exponents
0
<
α
k
≤
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. (Citation2019) 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.

Article : Testing for Structural Change of Predictive Regression Model to Threshold Predictive Regression Model.Abstract
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.

Article : Bootstrap Tests for High-Dimensional White-Noise. Abstract
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.

Article : Extreme Value Estimation for Heterogeneous Data. Abstract
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.

Article : Factor and Factor Loading Augmented Estimators for Panel Regression With Possibly Nonstrong Factors. Abstract
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.