Stata Journal
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Ước Lượng Trong và Giữa Trong Mô Hình Ảnh Hưởng Ngẫu Nhiên: Lợi Ích và Hạn Chế của Mô Hình Ảnh Hưởng Ngẫu Nhiên Tương Quan và Mô Hình Lai Các mô hình ảnh hưởng ngẫu nhiên tương quan (Mundlak, 1978, Econometrica 46: 69–85; Wooldridge, 2010, Econometric Analysis of Cross Section and Panel Data [MIT Press]) và mô hình lai (Allison, 2009, Fixed Effects Regression Models [Sage]) được coi là những phương án thay thế hấp dẫn cho các mô hình ảnh hưởng ngẫu nhiên và ảnh hưởng cố định tiêu chuẩn vì chúng cung cấp các ước lượng trong biến cấp 1 và cho phép bao gồm cả các biến cấp 2. Tôi sẽ thảo luận về các mô hình này, đưa ra các ví dụ ước lượng và giải quyết một số phức tạp nảy sinh khi kết hợp các hiệu ứng tương tác.
Stata Journal - Tập 13 Số 1 - Trang 65-76 - 2013
#ảnh hưởng ngẫu nhiên #mô hình lai #ước lượng trong #mức độ 1 #mức độ 2 #tương tác hiệu ứng
Plotting Regression Coefficients and other Estimates Graphical display of regression results has become increasingly popular in presentations and in scientific literature because graphs are often much easier to read than tables. Such plots can be produced in Stata by the marginsplot command (see [R] marginsplot). However, while marginsplot is versatile and flexible, it has two major limitations: it can only process results left behind by margins (see [R] margins), and it can handle only one set of results at a time. In this article, I introduce a new command called coefplot that overcomes these limitations. It plots results from any estimation command and combines results from several models into one graph. The default behavior of coefplot is to plot markers for coefficients and horizontal spikes for confidence intervals. However, coefplot can also produce other types of graphs. I illustrate the capabilities of coefplot by using a series of examples.
Stata Journal - Tập 14 Số 4 - Trang 708-737 - 2014
Metandi: Meta-analysis of Diagnostic Accuracy Using Hierarchical Logistic Regression Meta-analysis of diagnostic test accuracy presents many challenges. Even in the simplest case, when the data are summarized by a 2 x 2 table from each study, a statistically rigorous analysis requires hierarchical (multilevel) models that respect the binomial data structure, such as hierarchical logistic regression. We present a Stata package, metandi, to facilitate the fitting of such models in Stata. The commands display the results in two alternative parameterizations and produce a customizable plot. metandi requires either Stata 10 or above (which has the new command xtmelogit), or Stata 8.2 or above with gllamm installed.
Stata Journal - Tập 9 Số 2 - Trang 211-229 - 2009
Visualizing Assumptions and Results in Network Meta-analysis: The Network Graphs Package Network meta-analysis has been established in recent years as a particularly useful evidence synthesis tool. However, it is still challenging to develop understandable and concise ways to present data, assumptions, and results from network meta-analysis to inform decision making and evaluate the credibility of the results. In this article, we provide a suite of commands with graphical tools to facilitate the understanding of data, the evaluation of assumptions, and the interpretation of findings from network meta-analysis.
Stata Journal - Tập 15 Số 4 - Trang 905-950 - 2015
Estimating Almost-ideal Demand Systems with Endogenous Regressors In this article, we present the new aidsills command for estimating almost-ideal demand systems and their quadratic extensions. In contrast with Poi's (2012, Stata Journal 12: 433–446) quaids command, which is based on the nonlinear nlsur command, aidsills uses the computationally attractive iterated linear least-squares estimator developed by Blundell and Robin (1999, Journal of Applied Econometrics 14: 209–232). The new command further allows one to account for endogenous prices and total expenditure by using instrumental-variable techniques. Elasticities and their standard errors can be obtained using the aidsills elas postestimation command.
Stata Journal - Tập 15 Số 2 - Trang 554-573 - 2015
Fitting Mixed Logit Models by Using Maximum Simulated Likelihood This article describes the mixlogit Stata command for fitting mixed logit models by using maximum simulated likelihood.
Stata Journal - Tập 7 Số 3 - Trang 388-401 - 2007
Error-Correction–Based Cointegration Tests for Panel Data This article describes a new Stata command called xtwest, which implements the four error-correction–based panel cointegration tests developed by Westerlund (2007). The tests are general enough to allow for a large degree of heterogeneity, both in the long-run cointegrating relationship and in the short-run dynamics, and dependence within as well as across the cross-sectional units.
Stata Journal - Tập 8 Số 2 - Trang 232-241 - 2008
Goodness-of-fit Test for a Logistic Regression Model Fitted using Survey Sample Data After a logistic regression model has been fitted, a global test of goodness of fit of the resulting model should be performed. A test that is commonly used to assess model fit is the Hosmer–Lemeshow test, which is available in Stata and most other statistical software programs. However, it is often of interest to fit a logistic regression model to sample survey data, such as data from the National Health Interview Survey or the National Health and Nutrition Examination Survey. Unfortunately, for such situations no goodness-of-fit testing procedures have been developed or implemented in available software. To address this problem, a Stata ado-command, svylogitgof, for estimating the F-adjusted mean residual test after svy: logit or svy: logistic estimation has been developed, and this paper describes its implementation.
Stata Journal - Tập 6 Số 1 - Trang 97-105 - 2006
Understanding the Multinomial-Poisson Transformation There is a known connection between the multinomial and the Poisson likelihoods. This, in turn, means that a Poisson regression may be transformed into a logit model and vice versa. In this paper, I show the data transformations required to implement this transformation. Several examples are used as illustrations.
Stata Journal - Tập 4 Số 3 - Trang 265-273 - 2004
Estimation of Nonstationary Heterogeneous Panels We introduce a new Stata command, xtpmg, for estimating nonstationary heterogeneous panels in which the number of groups and number of time-series observations are both large. Based on recent advances in the nonstationary panel literature, xtpmg provides three alternative estimators: a traditional fixed-effects estimator, the mean-group estimator of Pesaran and Smith (Estimating long-run relationships from dynamic heterogeneous panels, Journal of Econometrics 68: 79–113), and the pooled mean-group estimator of Pesaran, Shin, and Smith (Estimating long-run relationships in dynamic heterogeneous panels, DAE Working Papers Amalgamated Series 9721; Pooled mean group estimation of dynamic heterogeneous panels, Journal of the American Statistical Association 94: 621–634).
Stata Journal - Tập 7 Số 2 - Trang 197-208 - 2007
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