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Changes in the Curriculum Adaptation Skills of Teachers as a Result of Professional Development Support: A Turkish case study
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| Pengarang | : | Bumen, Nilay T.,Nalbantoglu, Umran Y. |
| Nama Majalah/Jurnal | : | Teaching and Teacher Education |
| Volume / Edisi | : | 137 (No. 137) |
| Halaman | : | 1-13 |
| Abstrak | : | Despite the growing popularity of research on teachers' curriculum adaptation, how to improve their ability to adapt curriculum productively remains unexplored. This paper presents a multi-case study to reveal changes in the productivity of curriculum adaptation patterns through long-term professional development (PD) support. The findings indicate that PD support enhanced the productive adaptation of teachers in all patterns and that adaptive decisions became systematic and deliberative at the 14-week follow-up. Moreover, extending and omitting demand high levels of pedagogical design capacity, which requires further support for teachers. Lastly, the study discusses the implications on the growth and adaptation of teachers. |
Locally Stationary Quantile Regression for Inflation and Interest Rates
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 40 (No. 2) |
| Halaman | : | 838-851 |
| Abstrak | : | Motivated by the potential time-varying and quantile-specific relation between inflation and interest rates, we propose a locally stationary quantile regression approach to model the inflation and interest rates relation. Large sample theory for estimation and inference of quantile-varying and time-varying coefficients are established. In empirical analysis of inflation and interest rates relation, it is found that the estimated functional coefficients vary with time in a complicated manner. Furthermore, the relation is quantile-specific: not only do the selected orders differ for different quantiles, but also the coefficients corresponding to different quantiles can display completely different patterns. |
Fixed-k Inference for Conditional Extremal Quantiles
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 40 (No. 2) |
| Halaman | : | 829-837 |
| Abstrak | : | We develop a new extreme value theory for repeated cross-sectional and longitudinal/panel data to construct asymptotically valid confidence intervals (CIs) for conditional extremal quantiles from a fixed number k of nearest-neighbor tail observations. As a by-product, we also construct CIs for extremal quantiles of coefficients in linear random coefficient models. For any fixed k, the CIs are uniformly valid without parametric assumptions over a set of nonparametric data generating processes associated with various tail indices. Simulation studies show that our CIs exhibit superior small-sample coverage and length properties than alternative nonparametric methods based on asymptotic normality. Applying the proposed method to Natality Vital Statistics, we study factors of extremely low birth weights. We find that signs of major effects are the same as those found in preceding studies based on parametric models, but with different magnitudes. |
Structural Equation Model Averaging: Methodology and Application
| Pengarang | : | Loraine Seng |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 40 (No. 2) |
| Halaman | : | 815-828 |
| Abstrak | : | The instrumental variable (IV) methods are attractive since they can lead to a consistent answer to the main question in causal modeling, that is, the estimation of average causal effect of an exposure on the outcome in the presence of unmeasured confounding. However, it is now acknowledged in the literature that using weak IVs might not suit the inference goal satisfactorily. We consider the problem of estimating causal effects in an observational study in this article, allowing some IVs to be weak. In many modern learning jobs, we may face a large number of instruments and their quality could range from poor to strong. To incorporate them in a 2-stage least squares estimation procedure, we consider a model averaging technique. The proposed methods only involve a few layers of least squares estimation with closed-form solutions and thus is easy to implement in practice. Theoretical properties are carefully established, including the consistency and asymptotic normality of the estimated causal parameter. Numerical studies are carried out to assess the performance in low- and high-dimensional settings and comparisons are made between our proposed method and a wide range of existing alternative methods. A real data example on home price is analyzed to illustrate our methodology. |
Multifrequency-Band Tests for White Noise Under Heteroscedasticity
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 40 (No. 2) |
| Halaman | : | 799-814 |
| Abstrak | : | This article proposes a new family of multifrequency-band tests for the white noise hypothesis by using the maximum overlap discrete wavelet packet transform. At each scale, the proposed multifrequency-band test has the chi-square asymptotic null distribution under mild conditions, which allow the data to be heteroscedastic. Moreover, an automatic multifrequency-band test is further proposed by using a data-driven method to select the scale, and its asymptotic null distribution is chi-square with one degree of freedom. Both multifrequency-band and automatic multifrequency-band tests are shown to have the desirable size and power performance by simulation studies, and their usefulness is further illustrated by two applications. As an extension, similar tests are given to check the adequacy of linear time series regression models, based on the unobserved model residuals. |
Model Averaging for Nonlinear Regression Models
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 40 (No. 2) |
| Halaman | : | 785-798 |
| Abstrak | : | This article considers the problem of model averaging for regression models that can be nonlinear in their parameters and variables. We consider a nonlinear model averaging (NMA) framework and propose a weight-choosing criterion, the nonlinear information criterion (NIC). We show that up to a constant, NIC is an asymptotically unbiased estimator of the risk function under nonlinear settings with some mild assumptions. We also prove the optimality of NIC and show the convergence of the model averaging weights. Monte Carlo experiments reveal that NMA leads to relatively lower risks compared with alternative model selection and model averaging methods in most situations. Finally, we apply the NMA method to predicting the individual wage, where our approach leads to the lowest prediction errors in most cases. |
A Factor-Based Estimation of Integrated Covariance Matrix With Noisy High-Frequency Data
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 40 (No. 2) |
| Halaman | : | 770-784 |
| Abstrak | : | This article studies a high-dimensional factor model with sparse idiosyncratic covariance matrix in continuous time, using asynchronous high-frequency financial data contaminated by microstructure noise. We focus on consistent estimations of the number of common factors, the integrated covariance matrix and its inverse, based on the flat-top realized kernels introduced by Varneskov. Simulation results illustrate the satisfactory performance of our estimators in finite samples. We apply our methodology to the high-frequency price data on a large number of stocks traded in Shanghai and Shenzhen stock exchanges, and demonstrate its value for capturing time-varying covariations and portfolio allocation. |
Long Memory Factor Model: On Estimation of Factor Memories
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 40 (No. 2) |
| Halaman | : | 756-769 |
| Abstrak | : | This article considers the estimation of the integration orders of the latent factors in an approximate factor model. Both the common factors and idiosyncratic error terms are potentially nonstationary fractionally integrated processes. We propose a two-stage approach to estimate the factor memories. We show the consistency and asymptotic normality of the proposed estimator. Applying the estimator to the log-squared returns of the U.S. financial institutions, we find evidence of long memory in the estimated factor. We also find that the factor becomes more persistent after 2007. |
Adaptive Testing for Cointegration With Nonstationary Volatility
| Pengarang | : | - |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 40 (No. 2) |
| Halaman | : | 744-755 |
| Abstrak | : | This article develops a class of adaptive cointegration tests for multivariate time series with nonstationary volatility. Persistent changes in the innovation variance matrix of a vector autoregressive model lead to size distortions in conventional cointegration tests, which may be resolved using the wild bootstrap, as shown in recent work by Cavaliere, Rahbek, and Taylor. We show that it also leads to the possibility of constructing tests with higher power, by taking the time-varying volatilities and correlations into account in the formulation of the likelihood function and the resulting likelihood ratio test statistic. We find that under suitable conditions, adaptation with respect to the volatility process is possible, in the sense that nonparametric volatility matrix estimation does not lead to a loss of asymptotic local power relative to the case where the volatilities are observed. The asymptotic null distribution of the test is nonstandard and depends on the volatility process; we show that various bootstrap implementations may be used to conduct asymptotically valid inference. Monte Carlo simulations show that the resulting test has good size properties, and higher power than existing tests. Empirical analyses of the U.S. term structure of interest rates and purchasing power parity illustrate the applicability of the tests. |
Analyzing Subjective Well-Being Data with Misclassification
| Pengarang | : | Ekaterina Oparina |
| Nama Majalah/Jurnal | : | Journal of Business and Economic Statistics |
| Volume / Edisi | : | 40 (No. 2) |
| Halaman | : | 730-743 |
| Abstrak | : | We use novel nonparametric techniques to test for the presence of nonclassical measurement error in reported life satisfaction (LS) and study the potential effects from ignoring it. Our dataset comes from Wave 3 of the UK Understanding Society that is surveyed from 35,000 British households. Our test finds evidence of measurement error in reported LS for the entire dataset as well as for 26 out of 32 socioeconomic subgroups in the sample. We estimate the joint distribution of reported and latent LS nonparametrically in order to understand the mis-reporting behavior. We show this distribution can then be used to estimate parametric models of latent LS. We find measurement error bias is not severe enough to distort the main drivers of LS. But there is an important difference that is policy relevant. We find women tend to over-report their latent LS relative to men. This may help explain the gender puzzle that questions why women are reportedly happier than men despite being worse off in objective outcomes such as income and employment. |