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35810nam a22005537a 4500 |
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NUBALIWAG |
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20240808065605.0 |
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240808b ph ||||| |||| 00| 0 eng d |
| 022 ## - INTERNATIONAL STANDARD SERIAL NUMBER |
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0735-0015 |
| 040 ## - CATALOGING SOURCE |
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NUBLRC |
| 245 ## - TITLE STATEMENT |
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| Title |
Journal of business & economic statistics. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
Alexandria, VA : |
| Name of publisher, distributor, etc. |
American Statistical Association, |
| Date of publication, distribution, etc. |
c2023. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
652 pages ; |
| Dimensions |
28 cm. |
| 490 ## - SERIES STATEMENT |
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| Volume/sequential designation |
Journal of business & economic statistics. Volume 41, No. 2, April 2023 |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
-Penalized Pairwise Difference Estimation for a High-Dimensional Censored Regression Model.-- Dynamic Score-Driven Independent Component Analysis.-- No-Crossing Single-Index Quantile Regression Curve Estimation.-- Identification-Robust Inference With Simulation-Based Pseudo-Matching.-- Diagnostic Testing of Finite Moment Conditions for the Consistency and Root-N Asymptotic Normality of the GMM and M Estimators.-- Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective.-- On Testing Equal Conditional Predictive Ability Under Measurement Error.-- Multi-Threshold Structural Equation Model.-- Learning Human Activity Patterns Using Clustered Point Processes With Active and Inactive States.-- A Novel Estimation Method in Generalized Single Index Models.-- A Statistical Recurrent Stochastic Volatility Model for Stock Markets.-- Bayesian Dynamic Tensor Regression.-- Predicting the Global Minimum Variance Portfolio.-- Testing for Trend Specifications in Panel Data Models.-- Estimating Density Ratio of Marginals to Joint: Applications to Causal Inference.-- Proper Scoring Rules for Evaluating Density Forecasts with Asymmetric Loss Functions.-- Locally Stationary Multiplicative Volatility Modeling.-- Detecting Unobserved Heterogeneity in Efficient Prices via Classifier-Lasso.-- Quantifying Time-Varying Forecast Uncertainty and Risk for the Real Price of Oil.-- Inference for Nonparametric High-Frequency Estimators with an Application to Time Variation in Betas.-- QML and Efficient GMM Estimation of Spatial Autoregressive Models with Dominant (Popular) Units.-- Reconciled Estimates of Monthly GDP in the United States.-- Skilled Mutual Fund Selection: False Discovery Control Under Dependence.-- Composite Likelihood Estimation of an Autoregressive Panel Ordered Probit Model with Random Effects.-- Simultaneous Spatial Panel Data Models with Common Shocks.-- Post-selection Inference of High-dimensional Logistic Regression Under Case–Control Design.-- Circularly Projected Common Factors for Grouped Data.-- Corrigendum: Small Sample Methods for Cluster-Robust Variance Estimation and Hypothesis Testing in Fixed Effects Models.-- |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Article : -Penalized Pairwise Difference Estimation for a High-Dimensional Censored Regression Model. Abstract<br/>High-dimensional data are nowadays readily available and increasingly common in various fields of empirical economics. This article considers estimation and model selection for a high-dimensional censored linear regression model. We combine <br/>l<br/>1<br/>-penalization method with the ideas of pairwise difference and propose an <br/>l<br/>1<br/>-penalized pairwise difference least absolute deviations (LAD) estimator. Estimation consistency and model selection consistency of the estimator are established under regularity conditions. We also propose a post-penalized estimator that applies unpenalized pairwise difference LAD estimation to the model selected by the <br/>l<br/>1<br/>-penalized estimator, and find that the post-penalized estimator generally can perform better than the <br/>l<br/>1<br/>-penalized estimator in terms of the rate of convergence. Novel fast algorithms for computing the proposed estimators are provided based on the alternating direction method of multipliers. A simulation study is conducted to show the great improvements of our algorithms in terms of computation time and to illustrate the satisfactory statistical performance of our estimators. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Article : Dynamic Score-Driven Independent Component Analysis. Abstract<br/>A model for dynamic independent component analysis is introduced where the dynamics are driven by the score of the pseudo likelihood with respect to the rotation angle of model innovations. While conditional second moments are invariant with respect to rotations, higher conditional moments are not, which may have important implications for applications. The pseudo maximum likelihood estimator of the model is shown to be consistent and asymptotically normally distributed. A simulation study reports good finite sample properties of the estimator, including the case of a misspecification of the innovation density. In an application to a bivariate exchange rate series of the Euro and the British Pound against the U.S. Dollar, it is shown that the model-implied conditional portfolio kurtosis largely aligns with narratives on financial stress as a result of the global financial crisis in 2008, the European sovereign debt crisis (2010–2013) and early rumors signalling the United Kingdom to leave the European Union (2017). These insights are consistent with a recently proposed model that associates portfolio kurtosis with a geopolitical risk factor. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Article : No-Crossing Single-Index Quantile Regression Curve Estimation. Abstract<br/>Single-index quantile regression (QR) models can avoid the curse of dimensionality in nonparametric problems by assuming that the response is only related to a single linear combination of the covariates. Like the standard parametric or nonparametric QR whose estimated curves may cross, the single-index QR can also suffer quantile crossing, leading to an invalid distribution for the response. This issue has attracted considerable attention in the literature in the recent year. In this article, we consider single-index models, develop methods for QR that guarantee noncrossing quantile curves, and extend the methods and results to composite quantile regression. The asymptotic properties of the proposed estimators are derived and their advantages over existing methods are explained. Simulation studies and a real data application are conducted to illustrate the finite sample performance of the proposed methods. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Article : Identification-Robust Inference With Simulation-Based Pseudo-Matching. Abstract<br/>We develop a general simulation-based inference procedure for partially specified models. Our procedure is based on matching auxiliary statistics to simulated counterparts where nuisance parameters are calibrated neither assuming identification of parameters of interest nor a one-to-one binding function. The conditions underlying the asymptotic validity of our (pseudo-)simulators in conjunction with appropriate bootstraps are characterized beyond the strict and exact calibration of the parameters of the simulator. Our procedure is illustrated through impulse-response (IR) matching in a simulation study of a stylized dynamic stochastic equilibrium model, and two empirical applications on the New Keynesian Phillips curve and on the Industrial Production index. In addition to usual Wald-type statistics that combine structural or reduced form IRs, we analyze local projections IRs through a factor-analytic measure of distance which eschews the need to define a weighting matrix. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Article : Diagnostic Testing of Finite Moment Conditions for the Consistency and Root-N Asymptotic Normality of the GMM and M Estimators. Abstract<br/>Common econometric analyses based on point estimates, standard errors, and confidence intervals presume the consistency and the root-n asymptotic normality of the GMM or M estimators. However, their key assumptions that data entail finite moments may not be always satisfied in applications. This article proposes a method of diagnostic testing for these key assumptions with applications to both simulated and real datasets. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Article : Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective. Abstract<br/>This article constructs individual-specific density forecasts for a panel of firms or households using a dynamic linear model with common and heterogeneous coefficients as well as cross-sectional heteroscedasticity. The panel considered in this article features a large cross-sectional dimension N but short time series T. Due to the short T, traditional methods have difficulty in disentangling the heterogeneous parameters from the shocks, which contaminates the estimates of the heterogeneous parameters. To tackle this problem, I assume that there is an underlying distribution of heterogeneous parameters, model this distribution nonparametrically allowing for correlation between heterogeneous parameters and initial conditions as well as individual-specific regressors, and then estimate this distribution by combining information from the whole panel. Theoretically, I prove that in cross-sectional homoscedastic cases, both the estimated common parameters and the estimated distribution of the heterogeneous parameters achieve posterior consistency, and that the density forecasts asymptotically converge to the oracle forecast. Methodologically, I develop a simulation-based posterior sampling algorithm specifically addressing the nonparametric density estimation of unobserved heterogeneous parameters. Monte Carlo simulations and an empirical application to young firm dynamics demonstrate improvements in density forecasts relative to alternative approaches. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Article: On Testing Equal Conditional Predictive Ability Under Measurement Error. Abstract<br/>Loss functions are widely used to compare several competing forecasts. However, forecast comparisons are often based on mismeasured proxy variables for the true target. We introduce the concept of exact robustness to measurement error for loss functions and fully characterize this class of loss functions as the Bregman class. Hence, only conditional mean forecasts can be evaluated exactly robustly. For such exactly robust loss functions, forecast loss differences are on average unaffected by the use of proxy variables and, thus, inference on conditional predictive ability can be carried out as usual. Moreover, we show that more precise proxies give predictive ability tests higher power in discriminating between competing forecasts. Simulations illustrate the different behavior of exactly robust and nonrobust loss functions. An empirical application to U.S. GDP growth rates demonstrates the nonrobustness of quantile forecasts. It also shows that it is easier to discriminate between mean forecasts issued at different horizons if a better proxy for GDP growth is used. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Article : Multi-Threshold Structural Equation Model. Abstract<br/>In this article, we consider the instrumental variable estimation for causal regression parameters with multiple unknown structural changes across subpopulations. We propose a multiple change point detection method to determine the number of thresholds and estimate the threshold locations in the two-stage least square procedure. After identifying the estimated threshold locations, we use the Wald method to estimate the parameters of interest, that is, the regression coefficients of the endogenous variable. Based on some technical assumptions, we carefully establish the consistency of estimated parameters and the asymptotic normality of causal coefficients. Simulation studies are included to examine the performance of the proposed method. Finally, our method is illustrated via an application of the Philippine farm households data for which some new findings are discovered. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Article :Learning Human Activity Patterns Using Clustered Point Processes With Active and Inactive States. Abstract<br/>Modeling event patterns is a central task in a wide range of disciplines. In applications such as studying human activity patterns, events often arrive clustered with sporadic and long periods of inactivity. Such heterogeneity in event patterns poses challenges for existing point process models. In this article, we propose a new class of clustered point processes that alternate between active and inactive states. The proposed model is flexible, highly interpretable, and can provide useful insights into event patterns. A composite likelihood approach and a composite EM estimation procedure are developed for efficient and numerically stable parameter estimation. We study both the computational and statistical properties of the estimator including convergence, consistency, and asymptotic normality. The proposed method is applied to Donald Trump’s Twitter data to investigate if and how his behaviors evolved before, during, and after the presidential campaign. Additionally, we analyze large-scale social media data from Sina Weibo and identify interesting groups of users with distinct behaviors. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Article : A Novel Estimation Method in Generalized Single Index Models. Abstract<br/>The single index and generalized single index models have been demonstrated to be a powerful tool for studying nonlinear interaction effects of variables in the low-dimensional case. In this article, we propose a new estimation approach for generalized single index models <br/>E<br/>(<br/>Y<br/> <br/>|<br/> <br/>θ<br/>⊤<br/>X<br/>)<br/>=<br/>ψ<br/>(<br/>g<br/>(<br/>θ<br/>⊤<br/>X<br/>)<br/>)<br/> with <br/>ψ<br/>(<br/>·<br/>)<br/> known but <br/>g<br/>(<br/>·<br/>)<br/> unknown. Specifically, we first obtain a consistent estimator of the regression function by using a local linear smoother, and then estimate the parametric components by treating <br/>ψ<br/>(<br/>g<br/>̂<br/>(<br/>θ<br/>⊤<br/>X<br/>i<br/>)<br/>)<br/> as our continuous response. The resulting estimators of θ are asymptotically normal. The proposed procedure can substantially overcome convergence problems encountered in generalized linear models with discrete response variables when sparseness occurs and misspecification. We conduct simulation experiments to evaluate the numerical performance of the proposed methods and analyze a financial dataset from a peer-to-peer lending platform of China as an illustration. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Article : A Statistical Recurrent Stochastic Volatility Model for Stock Markets. Abstract<br/>The stochastic volatility (SV) model and its variants are widely used in the financial sector, while recurrent neural network (RNN) models are successfully used in many large-scale industrial applications of deep learning. We combine these two methods in a nontrivial way and propose a model, which we call the statistical recurrent stochastic volatility (SR-SV) model, to capture the dynamics of stochastic volatility. The proposed model is able to capture complex volatility effects, for example, nonlinearity and long-memory auto-dependence, overlooked by the conventional SV models, is statistically interpretable and has an impressive out-of-sample forecast performance. These properties are carefully discussed and illustrated through extensive simulation studies and applications to five international stock index datasets: the German stock index DAX30, the Hong Kong stock index HSI50, the France market index CAC40, the U.S. stock market index SP500 and the Canada market index TSX250. An user-friendly software package together with the examples reported in the article are available at https://github.com/vbayeslab. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Article : Bayesian Dynamic Tensor Regression. Abstract<br/>High- and multi-dimensional array data are becoming increasingly available. They admit a natural representation as tensors and call for appropriate statistical tools. We propose a new linear autoregressive tensor process (ART) for tensor-valued data, that encompasses some well-known time series models as special cases. We study its properties and derive the associated impulse response function. We exploit the PARAFAC low-rank decomposition for providing a parsimonious parameterization and develop a Bayesian inference allowing for shrinking effects. We apply the ART model to time series of multilayer networks and study the propagation of shocks across nodes, layers and time. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Article : Predicting the Global Minimum Variance Portfolio. Abstract<br/>We propose a novel dynamic approach to forecast the weights of the global minimum variance portfolio (GMVP) for the conditional covariance matrix of asset returns. The GMVP weights are the population coefficients of a linear regression of a benchmark return on a vector of return differences. This representation enables us to derive a consistent loss function from which we can infer the GMVP weights without imposing any distributional assumptions on the returns. In order to capture time variation in the returns’ conditional covariance structure, we model the portfolio weights through a recursive least squares (RLS) scheme as well as by generalized autoregressive score (GAS) type dynamics. Sparse parameterizations and targeting toward the weights of the equally weighted portfolio ensure scalability with respect to the number of assets. We apply these models to daily stock returns, and find that they perform well compared to existing static and dynamic approaches in terms of both the expected loss and unconditional portfolio variance. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Article : Testing for Trend Specifications in Panel Data Models. Abstract<br/>This article proposes a consistent nonparametric test for common trend specifications in panel data models with fixed effects. The test is general enough to allow for heteroscedasticity, cross-sectional and serial dependence in the error components, has an asymptotically normal distribution under the null hypothesis of correct trend specification, and is consistent against various alternatives that deviate from the null. In addition, the test has an asymptotic unit power against two classes of local alternatives approaching the null at different rates. We also propose a wild bootstrap procedure to better approximate the finite sample null distribution of the test statistic. Simulation results show that the proposed test implemented with bootstrap p-values performs reasonably well in finite samples. Finally, an empirical application to the analysis of the U.S. per capita personal income trend highlights the usefulness of our test in real datasets. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Article : Estimating Density Ratio of Marginals to Joint: Applications to Causal Inference. Abstract<br/>In various fields of data science, researchers often face problems of estimating the ratios of two probability densities. Particularly in the context of causal inference, the product of marginals for a treatment variable and covariates to their joint density ratio typically emerges in the process of constructing causal effect estimators. This article applies the general least square density ratio estimation methodology by Kanamori, Hido and Sugiyama to the product of marginals to joint density ratio, and demonstrates its usefulness particularly for causal inference on continuous treatment effects and dose-response curves. The proposed method is illustrated by a simulation study and an empirical example to investigate the treatment effect of political advertisements in the U.S. presidential campaign data. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Article : Proper Scoring Rules for Evaluating Density Forecasts with Asymmetric Loss Functions. Abstract<br/>This article proposes a novel asymmetric continuous probabilistic score (ACPS) for evaluating and comparing density forecasts. It generalizes the proposed score and defines a weighted version, which emphasizes regions of interest, such as the tails or the center of a variable’s range. The (weighted) ACPS extends the symmetric (weighted) CRPS by allowing for asymmetries in the preferences underlying the scoring rule. A test is used to statistically compare the predictive ability of different forecasts. The ACPS is of general use in any situation where the decision-maker has asymmetric preferences in the evaluation of the forecasts. In an artificial experiment, the implications of varying the level of asymmetry in the ACPS are illustrated. Then, the proposed score and test are applied to assess and compare density forecasts of macroeconomic relevant datasets (U.S. employment growth) and of commodity prices (oil and electricity prices) with particular focus on the recent COVID-19 crisis period. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Article : Locally Stationary Multiplicative Volatility Modeling. Abstract<br/>In this article, we study a semiparametric multiplicative volatility model, which splits up into a nonparametric part and a parametric GARCH component. The nonparametric part is modeled as a product of a deterministic time trend component and of further components that depend on stochastic regressors. We propose a two-step procedure to estimate the model. To estimate the nonparametric components, we transform the model and apply a backfitting procedure. The GARCH parameters are estimated in a second step via quasi maximum likelihood. We show consistency and asymptotic normality of our estimators. Our results are obtained using mixing properties and local stationarity. We illustrate our method using financial data. Finally, a small simulation study illustrates a substantial bias in the GARCH parameter estimates when omitting the stochastic regressors. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Article : Proper Scoring Rules for Evaluating Density Forecasts with Asymmetric Loss Functions. Abstract<br/>This article proposes a novel asymmetric continuous probabilistic score (ACPS) for evaluating and comparing density forecasts. It generalizes the proposed score and defines a weighted version, which emphasizes regions of interest, such as the tails or the center of a variable’s range. The (weighted) ACPS extends the symmetric (weighted) CRPS by allowing for asymmetries in the preferences underlying the scoring rule. A test is used to statistically compare the predictive ability of different forecasts. The ACPS is of general use in any situation where the decision-maker has asymmetric preferences in the evaluation of the forecasts. In an artificial experiment, the implications of varying the level of asymmetry in the ACPS are illustrated. Then, the proposed score and test are applied to assess and compare density forecasts of macroeconomic relevant datasets (U.S. employment growth) and of commodity prices (oil and electricity prices) with particular focus on the recent COVID-19 crisis period.. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Article : Detecting Unobserved Heterogeneity in Efficient Prices via Classifier-Lasso. Abstract<br/>This article proposes a new measure of efficient price as a weighted average of bid and ask prices, where the weights are constructed from the bid-ask long-run relationships in a panel error-correction model (ECM). To allow for heterogeneity in the long-run relationships, we consider a panel ECM with latent group structures so that all the stocks within a group share the same long-run relationship and do not otherwise. We extend the Classifier-Lasso method to the ECM to simultaneously identify the individual’s group membership and estimate the group-specific long-run relationship. We establish the uniform classification consistency and good asymptotic properties of the post-Lasso estimators under some regularity conditions. Empirically, we find that more than 30% of the Standard & Poor’s (S&P) 1500 stocks have estimated efficient prices significantly deviating from the midpoint—a conventional measure of efficient price. Such deviations explored from our data-driven method can provide dynamic information on the extent and direction of informed trading activities. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Article : Quantifying Time-Varying Forecast Uncertainty and Risk for the Real Price of Oil. Abstract<br/>We propose a novel and numerically efficient quantification approach to forecast uncertainty of the real price of oil using a combination of probabilistic individual model forecasts. Our combination method extends earlier approaches that have been applied to oil price forecasting, by allowing for sequentially updating of time-varying combination weights, estimation of time-varying forecast biases and facets of miscalibration of individual forecast densities and time-varying inter-dependencies among models. To illustrate the usefulness of the method, we present an extensive set of empirical results about time-varying forecast uncertainty and risk for the real price of oil over the period 1974–2018. We show that the combination approach systematically outperforms commonly used benchmark models and combination approaches, both in terms of point and density forecasts. The dynamic patterns of the estimated individual model weights are highly time-varying, reflecting a large time variation in the relative performance of the various individual models. The combination approach has built-in diagnostic information measures about forecast inaccuracy and/or model set incompleteness, which provide clear signals of model incompleteness during three crisis periods. To highlight that our approach also can be useful for policy analysis, we present a basic analysis of profit-loss and hedging against price risk. |
| 520 ## - SUMMARY, ETC. |
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Article : Inference for Nonparametric High-Frequency Estimators with an Application to Time Variation in Betas. Abstract<br/>We consider the problem of conducting inference on nonparametric high-frequency estimators without knowing their asymptotic variances. We prove that a multivariate subsampling method achieves this goal under general conditions that were not previously available in the literature. By construction, the subsampling method delivers estimates of the variance-covariance matrices that are always positive semidefinite. Our simulation study indicates that the subsampling method is more robust than the plug-in method based on the asymptotic expression for the variance. We use our subsampling method to study the dynamics of financial betas of six stocks on the NYSE. We document significant variation in betas, and find that tick data captures more variation in betas than the data sampled at moderate frequencies such as every 5 or 20 min. To capture this variation we estimate a simple dynamic model for betas. The variance estimation is also important for the correction of the errors-in-variables bias in such models. We find that the bias corrections are substantial, and that betas are more persistent than the naive estimators would lead one to believe. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
Article : QML and Efficient GMM Estimation of Spatial Autoregressive Models with Dominant (Popular) Units. Abstract<br/>This article investigates QML and GMM estimation of spatial autoregressive (SAR) models in which the column sums of the spatial weights matrix might not be uniformly bounded. We develop a central limit theorem in which the number of columns with unbounded sums can be finite or infinite and the magnitude of their column sums can be <br/>O<br/>(<br/>n<br/>δ<br/>)<br/> if <br/>δ<br/><<br/>1<br/>. Asymptotic distributions of QML and GMM estimators are derived under this setting, including the GMM estimators with the best linear and quadratic moments when the disturbances are not normally distributed. The Monte Carlo experiments show that these QML and GMM estimators have satisfactory finite sample performances, while cases with a column sums magnitude of O(n) might not have satisfactory performance. An empirical application with growth convergence in which the trade flow network has the feature of dominant units is provided. Supplementary materials for this article are available online. |
| 520 ## - SUMMARY, ETC. |
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Article : Reconciled Estimates of Monthly GDP in the United States. Abstract<br/>In the United States, income and expenditure-side estimates of gross domestic product (GDP) (GDP<br/>I<br/> and GDP<br/>E<br/>) measure “true” GDP with error and are available at a quarterly frequency. Methods exist for using these proxies to produce reconciled quarterly estimates of true GDP. In this paper, we extend these methods to provide reconciled historical true GDP estimates at a monthly frequency. We do this using a Bayesian mixed frequency vector autoregression (MF-VAR) involving GDP<br/>E<br/>, GDP<br/>I<br/>, unobserved true GDP, and monthly indicators of short-term economic activity. Our MF-VAR imposes restrictions that reflect a measurement-error perspective (i.e., the two GDP proxies are assumed to equal true GDP plus measurement error). Without further restrictions, our model is unidentified. We consider a range of restrictions that allow for point and set identification of true GDP and show that they lead to informative monthly GDP estimates. We illustrate how these new monthly data contribute to our historical understanding of business cycles and we provide a real-time application nowcasting monthly GDP over the pandemic recession. |
| 520 ## - SUMMARY, ETC. |
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Article : Skilled Mutual Fund Selection: False Discovery Control Under Dependence. Abstract<br/>Selecting skilled mutual funds through the multiple testing framework has received increasing attention from finance researchers and statisticians. The intercept α of Carhart four-factor model is commonly used to measure the true performance of mutual funds, and positive α’s are considered as skilled. We observe that the standardized ordinary least-square estimates of α’s across the funds possess strong dependence and nonnormality structures, indicating that the conventional multiple testing methods are inadequate for selecting the skilled funds. We start from a decision theoretical perspective, and propose an optimal multiple testing procedure to minimize a combination of false discovery rate and false nondiscovery rate. Our proposed testing procedure is constructed based on the probability of each fund not being skilled conditional on the information across all of the funds in our study. To model the distribution of the information used for the testing procedure, we consider a mixture model under dependence and propose a new method called “approximate empirical Bayes” to fit the parameters. Empirical studies show that our selected skilled funds have superior long-term and short-term performance, for example, our selection strongly outperforms the S&P 500 index during the same period. |
| 520 ## - SUMMARY, ETC. |
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Article : Composite Likelihood Estimation of an Autoregressive Panel Ordered Probit Model with Random Effects. Abstract<br/>Modeling and estimating autocorrelated discrete data can be challenging. In this article, we use an autoregressive panel ordered probit model where the serial correlation in the discrete variable is driven by the autocorrelation in the latent variable. In such a nonlinear model, the presence of a lagged latent variable results in an intractable likelihood containing high-dimensional integrals. To tackle this problem, we use composite likelihoods that involve a much lower order of integration. However, parameter identification might potentially become problematic since the information employed in lower dimensional distributions may not be rich enough for identification. Therefore, we characterize types of composite likelihoods that are valid for this model and study conditions under which the parameters can be identified. Moreover, we provide consistency and asymptotic normality results for two different composite likelihood estimators and conduct Monte Carlo studies to assess their finite-sample performances. Finally, we apply our method to analyze corporate bond ratings. Supplementary materials for this article are available online. |
| 520 ## - SUMMARY, ETC. |
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Article : Simultaneous Spatial Panel Data Models with Common Shocks. Abstract<br/>We consider a simultaneous spatial panel data model, jointly modeling three effects: simultaneous effects, spatial effects and common shock effects. This joint modeling and consideration of cross-sectional heteroscedasticity result in a large number of incidental parameters. We propose two estimation approaches, a quasi-maximum likelihood method and an iterative generalized principal components method. We develop full inferential theories for the estimation approaches and study the tradeoff between the model specifications and their respective asymptotic properties. We further investigate the finite sample performance of both methods using Monte Carlo simulations. We find that both methods perform well and that the simulation results corroborate the inferential theories. Some extensions of the model are considered. Finally, we apply the model to analyze the relationship between trade and gross domestic product using a panel data over time and across countries. |
| 520 ## - SUMMARY, ETC. |
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Article : Post-selection Inference of High-dimensional Logistic Regression Under Case–Control Design. Abstract<br/>Confidence sets are of key importance in high-dimensional statistical inference. Under case–control study, a popular response-selective sampling design in medical study or econometrics, we consider the confidence intervals and statistical tests for single or low-dimensional parameters in high-dimensional logistic regression model. The asymptotic properties of the resulting estimators are established under mild conditions. We also study statistical tests for testing more general and complex hypotheses of the high-dimensional parameters. The general testing procedures are proved to be asymptotically exact and have satisfactory power. Numerical studies including extensive simulations and a real data example confirm that the proposed method performs well in practical settings. |
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Article : Circularly Projected Common Factors for Grouped Data. Abstract<br/>To extract the common factors from grouped data, multilevel factor models have been put forward in the literature, and methods based on iterative principal component analysis (PCA) and canonical correlation analysis (CCA) have been proposed for estimation purpose. While iterative PCA requires iteration and is hence time-consuming, CCA can only deal with two groups of data. Herein, we develop two new methods to address these problems. We first extract the factors within groups and then project the estimated group factors into the space spanned by them in a circular manner. We propose two projection processes to estimate the common factors and determine the number of them. The new methods do not require iteration and are thus computationally efficient. They can estimate the common factors for multiple groups of data in a uniform way, regardless of whether the number of groups is large or small. They not only overcome the drawbacks of CCA but also nest the CCA method as a special case. Finally, we theoretically and numerically study the consistency properties of these new methods and apply them to studying international business cycles and the comovements of retail prices. |
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Article : Corrigendum: Small Sample Methods for Cluster-Robust Variance Estimation and Hypothesis Testing in Fixed Effects Models. Abstract<br/>Pustejovsky and Tipton considered how to implement cluster-robust variance estimators for fixed effects models estimated by weighted (or unweighted) least squares. Theorem 2 of the paper concerns a computational short cut for a certain cluster-robust variance estimator in models with cluster-specific fixed effects. It claimed that this short cut works for models estimated by generalized least squares, as long as the weights are taken to be inverse of the working model. However, the theorem is incorrect. In this corrigendum, we review the CR2 variance estimator, describe the assertion of the theorem as originally stated, and demonstrate the error with a counter-example. We then provide a revised version of the theorem, which holds for the more limited set of models estimated by ordinary least squares. |
| 690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN) |
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| Topical term or geographic name as entry element |
BUSINESS. |
| 690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN) |
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| Topical term or geographic name as entry element |
ECONOMIC. |
| 690 ## - LOCAL SUBJECT ADDED ENTRY--TOPICAL TERM (OCLC, RLIN) |
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| Topical term or geographic name as entry element |
STATISTICS. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Serials |
| Suppress in OPAC |
No |
