Random effects model

About: Random effects model is a research topic. Over the lifetime, 8388 publications have been published within this topic receiving 438823 citations. The topic is also known as: random effects & random effect.

splm: Spatial Panel Data Models in R

Abstract: splm is an R package for the estimation and testing of various spatial panel data specifications. We consider the implementation of both maximum likelihood and generalized moments estimators in the context of fixed as well as random effects spatial panel data models. This paper is a general description of splm and all functionalities are illustrated using a well-known example taken from Munnell (1990) with productivity data on 48 US states observed over 17 years. We perform comparisons with other available software; and, when this is not possible, Monte Carlo results support our original implementation.

Age-Period-Cohort Analysis: New Models, Methods, and Empirical Applications

Abstract: Introduction Why Cohort Analysis? Introduction The Conceptualization of Cohort Effects Distinguishing Age, Period, and Cohort Summary APC Analysis of Data from Three Common Research Designs Introduction Repeated Cross-Sectional Data Designs Research Design I: Age-by-Time Period Tabular Array of Rates/Proportions Research Design II: Repeated Cross-Sectional Sample Surveys Research Design III: Prospective Cohort Panels and the Accelerated Longitudinal Design Formalities of the Age-Period-Cohort Analysis Conundrum and a Generalized Linear Mixed Models (GLMM) Framework Introduction Descriptive APC Analysis Algebra of the APC Model Identification Problem Conventional Approaches to the APC Identification Problem Generalized Linear Mixed Models (GLMM) Framework APC Accounting/Multiple Classification Model, Part I: Model Identification and Estimation Using the Intrinsic Estimator Introduction Algebraic, Geometric, and Verbal Definitions of the Intrinsic Estimator Statistical Properties Model Validation: Empirical Example Model Validation: Monte Carlo Simulation Analyses Interpretation and Use of the Intrinsic Estimator APC Accounting/Multiple Classification Model, Part II: Empirical Applications Introduction Recent U.S. Cancer Incidence and Mortality Trends by Sex and Race: A Three-Step Procedure APC Model-Based Demographic Projection and Forecasting Mixed Effects Models: Hierarchical APC-Cross-Classified Random Effects Models (HAPC-CCREM), Part I: The Basics Introduction Beyond the Identification Problem Basic Model Specification Fixed versus Random Effects HAPC Specifications Interpretation of Model Estimates Assessing the Significance of Random Period and Cohort Effects Random Coefficients HAPC-CCREM Mixed Effects Models: Hierarchical APC-Cross-Classified Random Effects Models (HAPC-CCREM), Part II: Advanced Analyses Introduction Level 2 Covariates: Age and Temporal Changes in Social Inequalities in Happiness HAPC-CCREM Analysis of Aggregate Rate Data on Cancer Incidence and Mortality Full Bayesian Estimation HAPC-Variance Function Regression Mixed Effects Models: Hierarchical APC-Growth Curve Analysis of Prospective Cohort Data Introduction Intercohort Variations in Age Trajectories Intracohort Heterogeneity in Age Trajectories Intercohort Variations in Intracohort Heterogeneity Patterns Summary Directions for Future Research and Conclusion Introduction Additional Models Longitudinal Cohort Analysis of Balanced Cohort Designs of Age Trajectories Conclusion Index References appear at the end of each chapter.

Adding Spatially-Correlated Errors Can Mess Up the Fixed Effect You Love

Abstract: Many statisticians have had the experience of fitting a linear model with uncorrelated errors, then adding a spatially-correlated error term (random effect) and finding that the estimates of the fixed-effect coefficients have changed substantially. We show that adding a spatially-correlated error term to a linear model is equivalent to adding a saturated collection of canonical regressors, the coefficients of which are shrunk toward zero, where the spatial map determines both the canonical regressors and the relative extent of the coefficients’ shrinkage. Adding a spatially-correlated error term can also be seen as inflating the error variances associated with specific contrasts of the data, where the spatial map determines the contrasts and the extent of error-variance inflation. We show how to avoid this spatial confounding by restricting the spatial random effect to the orthogonal complement (residual space) of the fixed effects, which we call restricted spatial regression. We consider five proposed in...

A meta-analytic review of the effect of implementation intentions on physical activity

Abstract: Implementation intentions are a powerful strategy to promote health-related behaviours, but mixed results are observed regarding physical activity. The primary aim of this study was to systematically and quantitatively review the literature on the effectiveness of implementation intentions on physical activity. The second aim was to identify conditions under which effectiveness is optimal. A literature search was performed in several databases for published and non-published reports. The inverse variance method with random effect model was used for the meta-analysis of results. Effect sizes were reported as standard mean differences. Twenty-six independent studies were included in the systematic review. The overall effect size of implementation intentions was 0.31, 95% confidence intervals (CI) [0.11, 0.51] at post-intervention and 0.24, 95% CI [0.13, 0.35] at follow-up. The duration of follow-up had no significant effect on effect size (F(1, 18) = 0.21, p=0.66. This strategy was more effective a...