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Recreating the circle: A collective vision for radical African healing in community.
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| Pengarang | : | Grills, Cheryl; Aird, Enola; Auguste, Evan; Adibu, Florence; Bethea, Sharon; Endale, Tarik; Haggins, Kristee; Mendenhall, Ruby; Newland, Lisa Zakiya; Primm, Annelle; Spates, Kamesha. |
| Nama Majalah/Jurnal | : | American Psychologist |
| Volume / Edisi | : | 80 (No. 4) |
| Halaman | : | 589-602 |
| Abstrak | : | As attacks on Black people have become more visible, key Black organizations have been building a grassroots movement of culturally grounded healing circles. African-centered healing circles address historical and contemporary racial stress experienced by Black people. They privilege culture and function as community-driven medicine, promoting collective healing and protecting against the effects of ongoing racism. The growth of these circles signals a shift away from Western methods of healing and toward indigenous practices. This article describes their growth, underlying theories, common elements, and evidence, and sketches a vision for national and international expansion. |
Testing for trend specifications in panel data models
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 41 (No. 2) |
| Halaman | : | 453-466 |
| Abstrak | : | 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. |
Predicting the global minimum variance portfolio
| Pengarang | : | Reh, Laura,Krüger, Fabian,Liesenfeld, Roman |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 41 (No. 2) |
| Halaman | : | 440-452 |
| Abstrak | : | 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. |
Bayesian dynamic tensor regression
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 41 (No. 2) |
| Halaman | : | 429-439 |
| Abstrak | : | 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. |
A statistical recurrent stochastic volatility model for stock markets
| Pengarang | : | Nguyen, Trong-Nghia,Tran, Minh-Ngoc ,Gunawan, David,Kohn, Robert |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 41 (No. 2) |
| Halaman | : | 414-428 |
| Abstrak | : | 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. |
A novel estimation method in generalized single index models
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 41 (No. 2) |
| Halaman | : | 399-413 |
| Abstrak | : | 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 with known but 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 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. |
Learning human activity patterns using clustered point processes with active and inactive states
| Pengarang | : | Zhang, Jingfei ,Cai, Biao,Zhu, Xuening,Wang, Hansheng ,Xu, Ganggang,Guan, Yongtao |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 41 (No. 2) |
| Halaman | : | 388-398 |
| Abstrak | : | 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. |
Multi-threshold structural equation model
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 41 (No. 2) |
| Halaman | : | 377-387 |
| Abstrak | : | 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. |
Strategi Kooperatif China di ASEAN dalam Sengketa Laut China Selatan: Suatu Pendekatan Eklektis
| Pengarang | : | Max Ketterer |
| Nama Majalah/Jurnal | : | Analisis CSIS |
| Volume / Edisi | : | 47 (No. 4) |
| Halaman | : | 569-593 |
| Abstrak | : | Tulisan ini menganalisis perubahan kebijakan China di ASEAN dalam sengketa Laut China Selatan. Beberapa tahun belakangan kebijakan luar negeri China menunjukkan gestur positif terhadap ASEAN dengan menyepakati kerangka normatif COC dan menginisiasi kerja sama maritim bersama ASEAN. China juga menjalin bubungan baik dengan negara-negara anggota ASEAN yang dulunya sempat bermusuhan. Kecenderungan ini menimbulkan pertanyaan: akankah China benar- benar menginginkan perdamaian di kawasan? Menggunakan konsep keseimbangan ancaman dan kultur strategis, tulisan ini berargumen bahwa strategi kooperatif China di ASEAN hanyalah strategi untuk menjamin kepentingannya di Laut China Selatan. China menggunakan instrumen multilateralisme untuk mengurangi pengaruh Amerika Serikat di kawasan. China juga memandang bahwa penerapan strategi kooperatif tidak berarti mengesampingkan strategi koersif karena kedua strategi tersebut ibarat dua sisi mata uang. Hal ini perlu disadari oleh ASEAN supaya tidak menjadi alat kepentingan negara kuat. Indonesia sebagai primus inter pares di kawasan perlu mempertahankan sentralitas ASEAN dengan lebih aktif terlibat dalam multilateralisme, baik di internal ASEAN sendiri maupun melibatkan negara-negara lain. |
On testing equal conditional predictive ability under measurement error
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 41 (No. 2) |
| Halaman | : | 364-376 |
| Abstrak | : | 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. |