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Alternative Perspective on the Transfer of Learning: History, Issues, and Challenges for Future Research
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| Pengarang | : | - |
| Nama Majalah/Jurnal | : | The Journal of The Learning Sciences |
| Volume / Edisi | : | 15-4 (No. 4) |
| Halaman | : | 431-450 |
| Abstrak | : | - |
Dialogue in the Classroom
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | The Journal of The Learning Sciences |
| Volume / Edisi | : | 15-3 (No. 3) |
| Halaman | : | 379-428 |
| Abstrak | : | - |
The Nature and Development of Teacher Meta Strategic Knowledge in the Context of Teaching Higher Order Thinking
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | The Journal of The Learning Sciences |
| Volume / Edisi | : | 15-3 (No. 3) |
| Halaman | : | 331-378 |
| Abstrak | : | - |
Designing Social Infrastructure: Critical Issues in Creating Learning Environments With Technology
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | The Journal of The Learning Sciences |
| Volume / Edisi | : | 15-3 (No. 3) |
| Halaman | : | 301-330 |
| Abstrak | : | - |
Network-Based Clustering for Varying Coefficient Panel Data Models
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 40 (No. 2) |
| Halaman | : | 578-594 |
| Abstrak | : | In this article, we introduce a novel varying-coefficient panel-data model with locally stationary regressors and unknown group structure, in which the number of groups and the group membership are left unspecified. We develop a triple-localization approach to estimate the unknown subject-specific coefficient functions and then identify the latent group structure via community detection. To improve the efficiency of the first-stage estimator, we further propose a two-stage estimation method that enables the estimator to achieve optimal rates of convergence. In the theoretical part of the article, we derive the asymptotic theory of the resultant estimators. In the empirical part, we present several simulated examples together with an analysis of real data to illustrate the finite-sample performance of the proposed method. |
Dynamic Discrete Mixtures for High-Frequency Prices
| Pengarang | : | Wigati, Slamet Setio |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 40 (No. 2) |
| Halaman | : | 559-577 |
| Abstrak | : | The tick structure of the financial markets entails discreteness of stock price changes. Based on this empirical evidence, we develop a multivariate model for discrete price changes featuring a mechanism to account for the large share of zero returns at high frequency. We assume that the observed price changes are independent conditional on the realization of two hidden Markov chains determining the dynamics and the distribution of the multivariate time series at hand. We study the properties of the model, which is a dynamic mixture of zero-inflated Skellam distributions. We develop an expectation-maximization algorithm with closed-form M-step that allows us to estimate the model by maximum likelihood. In the empirical application, we study the joint distribution of the price changes of a number of assets traded on NYSE. Particular focus is dedicated to the assessment of the quality of univariate and multivariate density forecasts, and of the precision of the predictions of moments like volatility and correlations. Finally, we look at the predictability of price staleness and its determinants in relation to the trading activity on the financial markets. |
Bayesian Model Averaging for Spatial Autoregressive Models Based on Convex Combinations of Different Types of Connectivity Matrices
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 40 (No. 2) |
| Halaman | : | 547-558 |
| Abstrak | : | There is a great deal of literature regarding use of nongeographically based connectivity matrices or combinations of geographic and non-geographic structures in spatial econometric models. We focus on convex combinations of weight matrices that result in a single weight matrix reflecting multiple types of connectivity, where coefficients from the convex combination can be used for inference regarding the relative importance of each type of connectivity in the global cross-sectional dependence scheme. We tackle the question of model uncertainty regarding selection of the best convex combination by Bayesian model averaging. We use Metropolis–Hastings guided Monte Carlo integration during MCMC estimation of the models to produce log-marginal likelihoods and associated posterior model probabilities. We focus on MCMC estimation, computation of posterior model probabilities, model averaged estimates of the parameters, scalar summary measures of the non-linear partial derivative impacts, and their associated empirical measures of dispersion. |
Nonparametric Copula Estimation for Mixed Insurance Claim Data
| Pengarang | : | M. Lunn |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 40 (No. 2) |
| Halaman | : | 537-546 |
| Abstrak | : | Multivariate claim data are common in insurance applications, for example, claims of each policyholder from different types of insurance coverages. Understanding the dependencies among such multivariate risks is critical to the solvency and profitability of insurers. Effectively modeling insurance claim data is challenging due to their special complexities. At the policyholder level, claim outcomes usually follow a two-part mixed distribution: a probability mass at zero corresponding to no claim and an otherwise positive claim from a skewed and long-tailed distribution. To simultaneously accommodate the complex features of the marginal distributions while flexibly quantifying the dependencies among multivariate claims, copula models are commonly used. Although a substantial body of literature focusing on copulas with continuous outcomes has emerged, some key steps do not carry over to mixed data. In particular, existing nonparametric copula estimators are not consistent for mixed data, and thus copula specification and diagnostics for mixed outcomes have been a problem. However, insurance is a closely regulated industry in which model validation is particularly important, and it is essential to develop a baseline nonparametric copula estimator to identify the underlying dependence structure. In this article, we fill in this gap by developing a nonparametric copula estimator for mixed data. We show the uniform convergence of the proposed nonparametric copula estimator. Through simulation studies, we demonstrate that the proportion of zeros plays a key role in the finite sample performance of the proposed estimator. Using the claim data from the Wisconsin Local Government Property Insurance Fund, we illustrate that our nonparametric copula estimator can assist analysts in identifying important features of the underlying dependence structure, revealing how different claims or risks are related to one another. |
Prediction of Extremal Expectile Based on Regression Models With Heteroscedastic Extremes
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 40 (No. 2) |
| Halaman | : | 522-536 |
| Abstrak | : | Expectile recently receives much attention for its coherence as a tail risk measure. Estimation of conditional expectile at extremal tails is of great interest in quantitative risk management. Regression analysis is a convenient and useful way to quantify the conditional effect of some predictors or risk factors on an interesting response variable. However, when it comes to the estimation of extremal conditional expectile, the traditional inference methods may suffer from considerable variation due to a lack of sufficient samples on tail regions, which makes the prediction inaccurate. In this article, we study the estimation of extremal conditional expectile based on quantile regression and expectile regression models. We propose three methods to make extrapolation based on a second-order condition for a framework of the so-called conditionally heteroscedastic and unconditionally homoscedastic extremes. In addition, we establish the asymptotic properties of the proposed methods and show their empirical behaviors through simulation studies. Finally, data analysis is conducted to illustrate the applications of the proposed methods in real problems. |
A Simple Asymptotically F-Distributed Portmanteau Test for Diagnostic Checking of Time Series Models With Uncorrelated Innovations
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 40 (No. 2) |
| Halaman | : | 505-521 |
| Abstrak | : | We propose a simple asymptotically F-distributed portmanteau test for diagnostically checking whether the innovations in a parametric time series model are uncorrelated while allowing them to exhibit higher-order dependence of unknown forms. A transform of sample residual autocovariances removing the influence of parameter estimation uncertainty makes the test simple. Further, by employing the orthonormal series variance estimator, a special sample autocovariances estimator that is asymptotically invariant to parameter estimation uncertainty, we show that the proposed test statistic is asymptotically F-distributed under fixed-smoothing asymptotics. The asymptotic F-theory accounts for the estimation error of the variance estimator that the asymptotic chi-squared theory ignores. Moreover, an extensive Monte Carlo study demonstrates that the F-test has more accurate finite sample size than existing tests with virtually no power loss. An application to S&P 500 returns illustrates the merits of the proposed methodology. |