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Journal articles on the topic 'Fixed effects regression model'

Author: Grafiati

Published: 5 September 2021

Last updated: 18 February 2022

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1

Allison, Paul D., and Richard P. Waterman. "7. Fixed-Effects Negative Binomial Regression Models." Sociological Methodology 32, no. 1 (August 2002): 247–65. http://dx.doi.org/10.1111/1467-9531.00117.

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This paper demonstrates that the conditional negative binomial model for panel data, proposed by Hausman, Hall, and Griliches (1984), is not a true fixed-effects method. This method—which has been implemented in both Stata and LIMDEP—does not in fact control for all stable covariates. Three alternative methods are explored. A negative multinomial model yields the same estimator as the conditional Poisson estimator and hence does not provide any additional leverage for dealing with over-dispersion. On the other hand, a simulation study yields good results from applying an unconditional negative binomial regression estimator with dummy variables to represent the fixed effects. There is no evidence for any incidental parameters bias in the coefficients, and downward bias in the standard error estimates can be easily and effectively corrected using the deviance statistic. Finally, an approximate conditional method is found to perform at about the same level as the unconditional estimator.

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2

Abrevaya, Jason. "Rank estimation of a generalized fixed-effects regression model." Journal of Econometrics 95, no. 1 (March 2000): 1–23. http://dx.doi.org/10.1016/s0304-4076(99)00027-5.

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3

Ai, Chunrong, and Chaoying Chen. "Estimation of a fixed effects bivariate censored regression model." Economics Letters 40, no. 4 (December 1992): 403–6. http://dx.doi.org/10.1016/0165-1765(92)90134-k.

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4

Spineli, Loukia M., and Nikolaos Pandis. "Fixed-effect versus random-effects model in meta-regression analysis." American Journal of Orthodontics and Dentofacial Orthopedics 158, no. 5 (November 2020): 770–72. http://dx.doi.org/10.1016/j.ajodo.2020.07.016.

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5

Ullah, Aman, Tao Wang, and Weixin Yao. "Modal regression for fixed effects panel data." Empirical Economics 60, no. 1 (January 2021): 261–308. http://dx.doi.org/10.1007/s00181-020-01999-w.

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6

Li, Yao Xiang, and Li Chun Jiang. "Fitting Growth Model Using Nonlinear Regression with Random Parameters." Key Engineering Materials 480-481 (June 2011): 1308–12. http://dx.doi.org/10.4028/www.scientific.net/kem.480-481.1308.

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Mixed Effect models are flexible models to analyze grouped data including longitudinal data, repeated measures data, and multivariate multilevel data. One of the most common applications is nonlinear growth data. The Chapman-Richards model was fitted using nonlinear mixed-effects modeling approach. Nonlinear mixed-effects models involve both fixed effects and random effects. The process of model building for nonlinear mixed-effects models is to determine which parameters should be random effects and which should be purely fixed effects, as well as procedures for determining random effects variance-covariance matrices (e.g. diagonal matrices) to reduce the number of the parameters in the model. Information criterion statistics (AIC, BIC and Likelihood ratio test) are used for comparing different structures of the random effects components. These methods are illustrated using the nonlinear mixed-effects methods in S-Plus software.

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7

Jochmans, Koen, and Vincenzo Verardi. "Fitting exponential regression models with two-way fixed effects." Stata Journal: Promoting communications on statistics and Stata 20, no. 2 (June 2020): 468–80. http://dx.doi.org/10.1177/1536867x20931006.

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In this article, we introduce the commands twexp and twgravity, which implement the estimators developed in Jochmans (2017, Review of Economics and Statistics 99: 478–485) for exponential regression models with two-way fixed effects. twexp is applicable to generic n × m panel data. twgravity is written for the special case where the dataset is a cross-section on dyadic interactions between n agents. A prime example is cross-sectional bilateral trade data, where the model of interest is a gravity equation with importer and exporter effects. Both twexp and twgravity can deal with data where n and m are large, that is, where there are many fixed effects. These commands use Mata and are fast to execute.

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Allison, Paul D. "Asymmetric Fixed-effects Models for Panel Data." Socius: Sociological Research for a Dynamic World 5 (January 2019): 237802311982644. http://dx.doi.org/10.1177/2378023119826441.

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Standard fixed-effects methods presume that effects of variables are symmetric: The effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this article, I show that there are several aspects of their method that need improvement. I also develop a data-generating model that justifies the first-difference method but can be applied in more general settings. In particular, it can be used to construct asymmetric logistic regression models.

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Christodoulou, Demetris, and Vasilis Sarafidis. "Regression Clustering for Panel-data Models with Fixed Effects." Stata Journal: Promoting communications on statistics and Stata 17, no. 2 (June 2017): 314–29. http://dx.doi.org/10.1177/1536867x1701700204.

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In this article, we describe the xtregcluster command, which implements the panel regression clustering approach developed by Sarafidis and Weber (2015, Oxford Bulletin of Economics and Statistics 77: 274–296). The method classifies individuals into clusters, so that within each cluster, the slope parameters are homogeneous and all intracluster heterogeneity is due to the standard two-way error-components structure. Because the clusters are heterogeneous, they do not share common parameters. The number of clusters and the optimal partition are determined by the clustering solution, which minimizes the total residual sum of squares of the model subject to a penalty function that strictly increases in the number of clusters. The method is available for linear short panel-data models and useful for exploring heterogeneity in the slope parameters when there is no a priori knowledge about parameter structures. It is also useful for empirically evaluating whether any normative classifications are justifiable from a statistical point of view.

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Jochmans, Koen, and Martin Weidner. "Fixed‐Effect Regressions on Network Data." Econometrica 87, no. 5 (2019): 1543–60. http://dx.doi.org/10.3982/ecta14605.

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This paper considers inference on fixed effects in a linear regression model estimated from network data. An important special case of our setup is the two‐way regression model. This is a workhorse technique in the analysis of matched data sets, such as employer–employee or student–teacher panel data. We formalize how the structure of the network affects the accuracy with which the fixed effects can be estimated. This allows us to derive sufficient conditions on the network for consistent estimation and asymptotically valid inference to be possible. Estimation of moments is also considered. We allow for general networks and our setup covers both the dense and the sparse case. We provide numerical results for the estimation of teacher value‐added models and regressions with occupational dummies.

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11

Krejčová, H., J. Přibyl, J. Přibylová, M. Štípková, and N. Mielenz. "Genetic evaluation of daily gains of dual-purpose bulls using a random regression model." Czech Journal of Animal Science 53, No. 6 (June 19, 2008): 227–37. http://dx.doi.org/10.17221/360-cjas.

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Daily gains of 8 243 dual-purpose bulls from 100 to 400 days of age during the years 1971 to 2007 were analyzed by random regression models. Orthogonal Legendre polynomials (LP) of degree 4 were applied to daily gains calculated at 30-day intervals over the test period. Fixed curves were estimated within the station-year-season of birth. The models also included a fixed station-year-season of weighing, animal additive genetic effects and animal permanent environmental effects. The peak daily gain was attained between 230 and 280 days of age, which corresponded to the period of the lowest variance in daily gains. Heritability estimates of daily gain were in the range of 0.014 to 0.043. The reliability of composite trait – cumulative gains over the entire period was 0.87. Genetic correlations between gains at different ages were high for adjacent ages and decreased with increasing difference in ages. Correlations of permanent environmental effects were high for adjacent ages, but became negative for ages that were far apart, indicating the possibility of compensatory growth. The phenotypic correlations were close to zero. The correlations for cumulative daily gains were higher than those for individual daily gains.

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12

von Rosen, Tatjana, and Dietrich von Rosen. "Bilinear regression with random effects and reduced rank restrictions." Japanese Journal of Statistics and Data Science 3, no. 1 (June 14, 2019): 63–72. http://dx.doi.org/10.1007/s42081-019-00050-2.

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AbstractBilinear models with three types of effects are considered: fixed effects, random effects and latent variable effects. In the literature, bilinear models with random effects and bilinear models with latent variables have been discussed but there are no results available when combining random effects and latent variables. It is shown, via appropriate vector space decompositions, how to remove the random effects so that a well-known model comprising only fixed effects and latent variables is obtained. The spaces are chosen so that the likelihood function can be factored in a convenient and interpretable way. To obtain explicit estimators, an important standardization constraint on the random effects is assumed to hold. A theorem is presented where a complete solution to the estimation problem is given.

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13

Agyeman, A. "Estimating the Returns to Schooling: A Comparison of Fixed Effects and Selection Effects Models for Twins." Ghana Journal of Science 61, no. 1 (July 31, 2020): 15–30. http://dx.doi.org/10.4314/gjs.v61i1.2.

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Strong empirical links exist between the number of years spent schooling and earnings. However, the relationship may be masked due to the effect of unobserved factors that influence both wages and schooling. Two of the main econometric models, namely fixed-effects and selection-effects, used to analyse returns to schooling were compared using monozygotic and dizygotic twins’ datasets in Ghana. The efficiency of the models was assessed based on the standard errors associated with the return to schooling estimates. Goodness of fit measures was used as a basis for comparison of the performance of the two models. The results revealed that based on their standard errors, the regression estimates from the selection effects model (MZ = 0.1014±0.0197; DZ = 0.0947±0.0095) were more efficient than the regression estimates from the fixed-effects model (MZ = 0.1115±0.0353; DZ = 0.082±0.0127). However, the AICc values of the fixed effects model (MZAICc = 57.8 and DZAICc = 105.4) were smaller than the AICc values of the selection effects model (MZAICc = 151.6 and DZAICc = 221.6). Findings from the study indicate that, although both models produced consistent estimates of the economic returns to schooling, the fixed effects model provided a better fit to the twins’ data set.

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14

Rusgiyono, Agus, and Alan Prahutama. "GEOGRAPHICALLY WEIGHTED PANEL REGRESSION WITH FIXED EFFECT FOR MODELING THE NUMBER OF INFANT MORTALITY IN CENTRAL JAVA, INDONESIA." MEDIA STATISTIKA 14, no. 1 (April 28, 2021): 10–20. http://dx.doi.org/10.14710/medstat.14.1.10-20.

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One of the regression methods used to model by region is Geographically Weighted Regression (GWR). The GWR model developed to model panel data is Geographically Weighted Panel Regression (GWPR). Panel data has several advantages compared to cross-section or time-series data. The development of the GWPR model in this study uses the Fixed Effect model. It is used to model the number of infant mortality in Central Java. In this study, the weighting used by the fixed bisquare kernel resulted in a significant variable percentage of clean and healthy households. The value of R-square is 67.6%. Also in this paper completed by spread map base on GWPR model.

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15

Mummolo, Jonathan, and Erik Peterson. "Improving the Interpretation of Fixed Effects Regression Results." Political Science Research and Methods 6, no. 4 (January 11, 2018): 829–35. http://dx.doi.org/10.1017/psrm.2017.44.

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Fixed effects estimators are frequently used to limit selection bias. For example, it is well known that with panel data, fixed effects models eliminate time-invariant confounding, estimating an independent variable’s effect using only within-unit variation. When researchers interpret the results of fixed effects models, they should therefore consider hypothetical changes in the independent variable (counterfactuals) that could plausibly occur within units to avoid overstating the substantive importance of the variable’s effect. In this article, we replicate several recent studies which used fixed effects estimators to show how descriptions of the substantive significance of results can be improved by precisely characterizing the variation being studied and presenting plausible counterfactuals. We provide a checklist for the interpretation of fixed effects regression results to help avoid these interpretative pitfalls.

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16

Moon, Hyungsik Roger, and Martin Weidner. "DYNAMIC LINEAR PANEL REGRESSION MODELS WITH INTERACTIVE FIXED EFFECTS." Econometric Theory 33, no. 1 (December 10, 2015): 158–95. http://dx.doi.org/10.1017/s0266466615000328.

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We analyze linear panel regression models with interactive fixed effects and predetermined regressors, for example lagged-dependent variables. The first-order asymptotic theory of the least squares (LS) estimator of the regression coefficients is worked out in the limit where both the cross-sectional dimension and the number of time periods become large. We find two sources of asymptotic bias of the LS estimator: bias due to correlation or heteroscedasticity of the idiosyncratic error term, and bias due to predetermined (as opposed to strictly exogenous) regressors. We provide a bias-corrected LS estimator. We also present bias-corrected versions of the three classical test statistics (Wald, LR, and LM test) and show their asymptotic distribution is a χ2-distribution. Monte Carlo simulations show the bias correction of the LS estimator and of the test statistics also work well for finite sample sizes.

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17

Chen, Peng, Joshua M. Tebbs, and Christopher R. Bilder. "Group Testing Regression Models with Fixed and Random Effects." Biometrics 65, no. 4 (February 5, 2009): 1270–78. http://dx.doi.org/10.1111/j.1541-0420.2008.01183.x.

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18

Li, Zhuokai, Hai Liu, and Wanzhu Tu. "Model selection in multivariate semiparametric regression." Statistical Methods in Medical Research 27, no. 10 (February 6, 2017): 3026–38. http://dx.doi.org/10.1177/0962280217690769.

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Variable selection in semiparametric mixed models for longitudinal data remains a challenge, especially in the presence of multiple correlated outcomes. In this paper, we propose a model selection procedure that simultaneously selects fixed and random effects using a maximum penalized likelihood method with the adaptive least absolute shrinkage and selection operator penalty. Through random effects selection, we determine the correlation structure among multiple outcomes and therefore address whether a joint model is necessary. Additionally, we include a bivariate nonparametric component, as approximated by tensor product splines, to accommodate the joint nonlinear effects of two independent variables. We use an adaptive group least absolute shrinkage and selection operator to determine whether the bivariate nonparametric component can be reduced to additive components. To implement the selection and estimation method, we develop a two-stage expectation-maximization procedure. The operating characteristics of the proposed method are assessed through simulation studies. Finally, the method is illustrated in a clinical study of blood pressure development in children.

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19

You, Jinhong, and Xian Zhou. "ASYMPTOTIC THEORY IN FIXED EFFECTS PANEL DATA SEEMINGLY UNRELATED PARTIALLY LINEAR REGRESSION MODELS." Econometric Theory 30, no. 2 (December 13, 2013): 407–35. http://dx.doi.org/10.1017/s0266466613000352.

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This paper deals with statistical inference for the fixed effects panel data seemingly unrelated partially linear regression model. The model naturally extends the traditional fixed effects panel data regression model to allow for semiparametric effects. Multiple regression equations are permitted, and the model includes the aggregated partially linear model as a special case. A weighted profile least squares estimator for the parametric components is proposed and shown to be asymptotically more efficient than those neglecting the contemporaneous correlation. Furthermore, a weighted two-stage estimator for the nonparametric components is also devised and shown to be asymptotically more efficient than those based on individual regression equations. The asymptotic normality is established for estimators of both parametric and nonparametric components. The finite-sample performance of the proposed methods is evaluated by simulation studies.

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20

Corrales, Marta Lucia, and Edilberto Cepeda-Cuervo. "A Bayesian Approach to Mixed Gamma Regression Models." Revista Colombiana de Estadística 42, no. 1 (January 1, 2019): 81–99. http://dx.doi.org/10.15446/rce.v42n1.69334.

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Gamma regression models are a suitable choice to model continuous variables that take positive real values. This paper presents a gamma regression model with mixed effects from a Bayesian approach. We use the parametrisation of the gamma distribution in terms of the mean and the shape parameter, both of which are modelled through regression structures that may involve fixed and random effects. A computational implementation via Gibbs sampling is provided and illustrative examples (simulated and real data) are presented.

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21

Bertoli, C. D., J. Braccini Neto, C. McManus, J. A. Cobuci, G. S. Campos, M. L. Piccoli, and V. Roso. "Modelling non-additive genetic effects using ridge regression for an Angus–Nellore crossbred population." Animal Production Science 59, no. 5 (2019): 823. http://dx.doi.org/10.1071/an17439.

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Data from 294045 records from a crossbred Angus × Nellore population were used to estimate fixed genetic effects (both additive and non-additive) and to test different non-additive models using ridge regression. The traits studied included weaning gain (WG), postweaning gain (PG), phenotypic scores for weaning (WC) and postweaning (PC) conformation, weaning (WP) and postweaning (PP) precocity, weaning (WM) and postweaning (PM) muscling and scrotal circumference (SC). All models were compared using the likelihood-ratio test. The model including all fixed genetic effects (breed additive and complementarity, heterosis and epistatic loss non-additive effects, both direct and maternal) was the best option to analyse this crossbred population. For the complete model, all effects were statistically significant (P < 0.01) for weaning traits, except the direct breed additive effects for WP and WM; direct complementarity effect for WP, WM, PP and PM and maternal epistatic loss for PG. Direct breed additive effect was positive for weaning traits and negative for postweaning. Maternal breed additive effect was negative for SC and WP. Direct complementarity and heterosis were positive for all traits and maternal complementarity and heterosis were also positive for all traits, except for PG. Direct and maternal epistatic loss effects were negative for all traits. We conclude that the fixed genetic effects are mostly significant. Thus, it is important to include them in the model when evaluating crossbred animals, and the model that included breed additive effects, complementarity, heterosis and epistatic loss differed significantly from all reduced models, allowing to infer that it was the best model. The model with only breed additive and heterosis was parsimonious and could be used when the structure or amount of data does not allow the use of complete model.

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22

Li, Tong, and Tatsushi Oka. "Set identification of the censored quantile regression model for short panels with fixed effects." Journal of Econometrics 188, no. 2 (October 2015): 363–77. http://dx.doi.org/10.1016/j.jeconom.2015.03.005.

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23

Alvitiani, Siska, Hasbi Yasin, and Mochammad Abdul Mukid. "PEMODELAN DATA KEMISKINAN PROVINSI JAWA TENGAH MENGGUNAKAN FIXED EFFECT SPATIAL DURBIN MODEL." Jurnal Gaussian 8, no. 2 (May 30, 2019): 220–32. http://dx.doi.org/10.14710/j.gauss.v8i2.26667.

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Based on data from the Central Statistics Agency, Central Java has 4,20 million people (12,23%) poor population in 2017 with Rp333.224,00 per capita per month poverty line. So, Central Java has got the second rank after East Java as the province which has the highest poor population in indonesia in 2017. In this research use the fixed effects spatial durbin model method for modeling poor population in each city in Central Java at 2014-2017. The spatial durbin model is a spatial regression model which contains a spatial dependence on dependent variable and independent variable. If the spatial dependence on dependent variable or independent variables is ignored, the resulting coefficient estimator will be biased and inconsistent. The fixed effect is one of the panel data regression models which assumes a different intercept value at each observation but fixed at each time, and slope coefficient is constant. The advantage of using fixed effects in spatial panel data regression is able to know the different characteristics in each region. The dependent variable used is poor population in each city in Central Java, and the independent variable is Minimum Wage, Life Expectancy, School Participation Rate 16-18 Years, Expected Years of Schooling, Total Population, and Per Capita Expenditure. The results of the analysis shows that the fixed effects spatial durbin model is significant and can be used. The variables that significantly affect the model are the Life Expectancy and Expected Years of Schooling, and the coefficient of determination (R2) is 99.95%. Keywords: Poverty, Spatial, Panel Data, Fixed Effects Spatial Durbin Model

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24

Brotherstone, S., I. M. S. White, and K. Meyer. "Genetic modelling of daily milk yield using orthogonal polynomials and parametric curves." Animal Science 70, no. 3 (June 2000): 407–15. http://dx.doi.org/10.1017/s1357729800051754.

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AbstractRandom regression models have been advocated for the analysis of test day records in dairy cattle. The effectiveness of a random regression analysis depends on the function used to model the data. To investigate functions suitable for the analysis of daily milk yield, test day milk yields of 7860 first lactation Holstein Friesian cows were analysed using random regression models involving three types of curves. Each analysis fitted the same curve to model overall trends through a fixed regression and random deviations due to animals. Curves included orthogonal polynomials, fitted to order 3 (quadratic), 4 (cubic) and 5 (quartic), respectively, a three-parameter parametric curve and a five-parameter parametric curve. Sets of random regression coefficients were fitted to model both animals’ genetic effects and permanent environmental effects. Temporary measurement errors were assumed independently but heterogeneously distributed, and assigned to one of 12 classes. Results showed that the measurement error variances were generally lowest around peak lactation, and higher at the beginning and end of lactation. Parametric curves yielded the highest likelihoods, but produced negative genetic associations between yield in early lactation and later lactation yields, while positive genetic correlations across the entire lactation were estimated with all models involving orthogonal polynomials. The fit of models using orthogonal polynomials to model test day yield was improved by including higher order fixed regressions.

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Ai, Chunrong, Jinhong You, and Yong Zhou. "Estimation of fixed effects panel data partially linear additive regression models." Econometrics Journal 17, no. 1 (January 14, 2014): 83–106. http://dx.doi.org/10.1111/ectj.12011.

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26

Dai, Xiaowen, Zhen Yan, Maozai Tian, and ManLai Tang. "Quantile regression for general spatial panel data models with fixed effects." Journal of Applied Statistics 47, no. 1 (June 13, 2019): 45–60. http://dx.doi.org/10.1080/02664763.2019.1628190.

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27

Teixeira, B. B., R. R. Mota, R. B. Lôbo, L. P. Silva, A. P. Souza Carneiro, F. G. Silva, G. C. Caetano, and F. F. Silva. "Genetic evaluation of growth traits in Nellore cattle through multi-trait and random regression models." Czech Journal of Animal Science 63, No. 6 (May 25, 2018): 212–21. http://dx.doi.org/10.17221/21/2017-cjas.

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We aimed to evaluate different orders of fixed and random effects in random regression models (RRM) based on Legendre orthogonal polynomials as well as to verify the feasibility of these models to describe growth curves in Nellore cattle. The proposed RRM were also compared to multi-trait models (MTM). Variance components and genetic parameters estimates were performed via REML for all models. Twelve RRM were compared through Akaike (AIC) and Bayesian (BIC) information criteria. The model of order three for the fixed curve and four for all random effects (direct genetic, maternal genetic, permanent environment, and maternal permanent environment) fits best. Estimates of direct genetic, maternal genetic, maternal permanent environment, permanent environment, phenotypic and residual variances were similar between MTM and RRM. Heritability estimates were higher via RRM. We presented perspectives for the use of RRM for genetic evaluation of growth traits in Brazilian Nellore cattle. In general, moderate heritability estimates were obtained for the majority of studied traits when using RRM. Additionally, the precision of these estimates was higher when using RRM instead of MTM. However, concerns about the variance components estimates in advanced ages via Legendre polynomial must be taken into account in future studies.

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Esen, Sinan, and Korhan Gokmenoglu. "Financial Centres Index and GDP Growth." International Journal of Economics and Finance 8, no. 4 (March 23, 2016): 198. http://dx.doi.org/10.5539/ijef.v8n4p198.

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This study investigates the relationship between financial centres index and GDP growth of 20 countries with the world’s largest GDP. In our sample each country is represented by just one financial centre. We tested many models through several panel approaches. Use of a fixed-effects model, fixed-effects (within) regression, random-effects GLS regression, random-effects ML regression, and empirical findings showed that the global financial centres index variable is highly statistically significant, and coefficients obtained from different estimations are very close to each other. Our findings support the idea that financial centres positively affect the GDP growth of countries.

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Bell, Andrew, Daniel Holman, and Kelvyn Jones. "Using Shrinkage in Multilevel Models to Understand Intersectionality." Methodology 15, no. 2 (April 1, 2019): 88–96. http://dx.doi.org/10.1027/1614-2241/a000167.

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Abstract. Multilevel models have recently been used to empirically investigate the idea that social characteristics are intersectional such as age, sex, ethnicity, and socioeconomic position interact with each other to drive outcomes. Some argue this approach solves the multiple-testing problem found in standard dummy-variable (fixed-effects) regression, because intersectional effects are automatically shrunk toward their mean. The hope is intersections appearing statistically significant by chance in a fixed-effects regression will not appear so in a multilevel model. However, this requires assumptions that are likely to be broken. We use simulations to show the effect of breaking these assumptions: when there are true main effects/interactions, unmodeled in the fixed part of the model. We show, while the multilevel approach outperforms the fixed-effects approach, shrinkage is less than is desired, and some intersectional effects are likely to appear erroneously statistically significant by chance. We conclude with advice to make this promising method work robustly.

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Leon, Andrew C., and Moonseong Heo. "Sample sizes required to detect interactions between two binary fixed-effects in a mixed-effects linear regression model." Computational Statistics & Data Analysis 53, no. 3 (January 2009): 603–8. http://dx.doi.org/10.1016/j.csda.2008.06.010.

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Hernández, Freddy, and Viviana Giampaoli. "The Impact of Misspecified Random Effect Distribution in a Weibull Regression Mixed Model." Stats 1, no. 1 (May 31, 2018): 48–76. http://dx.doi.org/10.3390/stats1010005.

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Mixed models are useful tools for analyzing clustered and longitudinal data. These models assume that random effects are normally distributed. However, this may be unrealistic or restrictive when representing information of the data. Several papers have been published to quantify the impacts of misspecification of the shape of the random effects in mixed models. Notably, these studies primarily concentrated their efforts on models with response variables that have normal, logistic and Poisson distributions, and the results were not conclusive. As such, we investigated the misspecification of the shape of the random effects in a Weibull regression mixed model with random intercepts in the two parameters of the Weibull distribution. Through an extensive simulation study considering six random effect distributions and assuming normality for the random effects in the estimation procedure, we found an impact of misspecification on the estimations of the fixed effects associated with the second parameter σ of the Weibull distribution. Additionally, the variance components of the model were also affected by the misspecification.

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FERREIRA, Daniel Furtado. "SISVAR: A COMPUTER ANALYSIS SYSTEM TO FIXED EFFECTS SPLIT PLOT TYPE DESIGNS." REVISTA BRASILEIRA DE BIOMETRIA 37, no. 4 (December 20, 2019): 529. http://dx.doi.org/10.28951/rbb.v37i4.450.

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This paper presents a special capability of Sisvar to deal with fixed effect models with several restriction in the randomization procedure. These restrictions lead to models with fixed treatment effects, but with several random errors. One way do deal with models of this kind is to perform a mixed model analysis, considering only the error effects in the model as random effects and with different covariance structure for the error terms. Another way is to perform a analysis of variance with several error. These kind of analysis, when the data are balanced, can be done by using Sisvar. The software lead a exact $F$ test for the fixed effects and allow the user to applied multiple comparison procedures or regression analysis for the levels of the fixed effect factors, regarding they are single effects, interaction effects or hierarchical effects. Sisvar is an interesting statistical computer system for using in balanced agricultural and industrial data sets.

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Dhaene, Geert, and Koen Jochmans. "LIKELIHOOD INFERENCE IN AN AUTOREGRESSION WITH FIXED EFFECTS." Econometric Theory 32, no. 5 (May 11, 2015): 1178–215. http://dx.doi.org/10.1017/s0266466615000146.

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We calculate the bias of the profile score for the regression coefficients in a multistratum autoregressive model with stratum-specific intercepts. The bias is free of incidental parameters. Centering the profile score delivers an unbiased estimating equation and, upon integration, an adjusted profile likelihood. A variety of other approaches to constructing modified profile likelihoods are shown to yield equivalent results. However, the global maximizer of the adjusted likelihood lies at infinity for any sample size, and the adjusted profile score has multiple zeros. Consistent parameter estimates are obtained as local maximizers inside or on an ellipsoid centered at the maximum likelihood estimator.

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NAGAMINE, Y., and C. S. HALEY. "Using the mixed model for interval mapping of quantitative trait loci in outbred line crosses." Genetical Research 77, no. 2 (April 2001): 199–207. http://dx.doi.org/10.1017/s0016672301004931.

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Interval mapping by simple regression is a powerful method for the detection of quantitative trait loci (QTLs) in line crosses such as F2 populations. Due to the ease of computation of the regression approach, relatively complex models with multiple fixed effects, interactions between QTLs or between QTLs and fixed effects can easily be accommodated. However, polygenic effects, which are not targeted in QTL analysis, cannot be treated as random effects in a least squares analysis. In a cross between true inbred lines this is of no consequence, as the polygenic effect contributes just to the residual variance. In a cross between outbred lines, however, if a trait has high polygenic heritability, the additive polygenic effect has a large influence on variation in the population. Here we extend the fixed model for the regression interval mapping method to a mixed model using an animal model. This makes it possible to use not only the observations from progeny (e.g. F2), but also those from the parents (F1) to evaluate QTLs and polygenic effects. We show how the animal model using parental observations can be applied to an outbred cross and so increase the power and accuracy of QTL analysis. Three estimation methods, i.e. regression and an animal model either with or without parental observations, are applied to simulated data. The animal model using parental observations is shown to have advantages in estimating QTL position and additive genotypic value, especially when the polygenic heritability is large and the number of progeny per parent is small.

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Chen, Songnian. "DISTRIBUTION-FREE ESTIMATION OF THE BOX–COX REGRESSION MODEL WITH CENSORING." Econometric Theory 28, no. 3 (November 25, 2011): 680–95. http://dx.doi.org/10.1017/s0266466611000703.

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The Box–Cox regression model has been widely used in applied economics. However, there has been very limited discussion when data are censored. The focus has been on parametric estimation in the cross-sectional case, and there has been no discussion at all for the panel data model with fixed effects. This paper fills these important gaps by proposing distribution-free estimators for the Box–Cox model with censoring in both the cross-sectional and panel data settings. The proposed methods are easy to implement by combining a convex minimization problem with a one-dimensional search. The procedures are applicable to other transformation models.

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Xiaowen, Dai, Jin Libin, Tian Yuzhu, Tian Maozai, and Tang Manlai. "Quantile regression for panel data models with fixed effects under random censoring." Communications in Statistics - Theory and Methods 49, no. 18 (April 14, 2019): 4430–45. http://dx.doi.org/10.1080/03610926.2019.1601221.

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Wu, Xiu, Jinting Zhang, and Daojun Zhang. "Explore Associations between Subjective Well-Being and Eco-Logical Footprints with Fixed Effects Panel Regressions." Land 10, no. 9 (September 3, 2021): 931. http://dx.doi.org/10.3390/land10090931.

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As environmental degradations constantly and directly threaten human well-being, it is imperative to explore the environmental impacts on people’s happy life. This research investigates the association between subjective well-being (SWB) and ecological footprints (EF) through space-time fixed effects panel regressions. EF, as a vital indicator of environmentally sustainable development, plays a vital role in ecological balance. SWB determines the subjective quality of life for humanity. EF-related factors and socio-economic indexes referring to GDP, urbanization rate, income, education, health, political stability, and political voice accountability in 101 countries were captured. Compared with ordinary least square (OLS), stepwise regression (SR) and fixed effects panel regression models (FEPR) exhibited good fitness regardless of the cross-section or longitudinal models due to R2 beyond 0.9. The finding also discloses that EF and health were positively significant to SWB, while income was negatively significant to SWB. EF was an invert u-shaped link to SWB, which met the assumption of EKC. This research provided a model-driven quantitative method to address environmental impacts on people’s quality life of happiness, and opened shared doors for further research of carbon balance and circular economy.

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Bertoli, Wesley, Katiane S. Conceição, Marinho G. Andrade, and Francisco Louzada. "A New Regression Model for the Analysis of Overdispersed and Zero-Modified Count Data." Entropy 23, no. 6 (May 21, 2021): 646. http://dx.doi.org/10.3390/e23060646.

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Count datasets are traditionally analyzed using the ordinary Poisson distribution. However, said model has its applicability limited, as it can be somewhat restrictive to handling specific data structures. In this case, the need arises for obtaining alternative models that accommodate, for example, overdispersion and zero modification (inflation/deflation at the frequency of zeros). In practical terms, these are the most prevalent structures ruling the nature of discrete phenomena nowadays. Hence, this paper’s primary goal was to jointly address these issues by deriving a fixed-effects regression model based on the hurdle version of the Poisson–Sujatha distribution. In this framework, the zero modification is incorporated by considering that a binary probability model determines which outcomes are zero-valued, and a zero-truncated process is responsible for generating positive observations. Posterior inferences for the model parameters were obtained from a fully Bayesian approach based on the g-prior method. Intensive Monte Carlo simulation studies were performed to assess the Bayesian estimators’ empirical properties, and the obtained results have been discussed. The proposed model was considered for analyzing a real dataset, and its competitiveness regarding some well-established fixed-effects models for count data was evaluated. A sensitivity analysis to detect observations that may impact parameter estimates was performed based on standard divergence measures. The Bayesian p-value and the randomized quantile residuals were considered for the task of model validation.

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Crossa, J., M. Vargas, and A. K. Joshi. "Linear, bilinear, and linear-bilinear fixed and mixed models for analyzing genotype × environment interaction in plant breeding and agronomy." Canadian Journal of Plant Science 90, no. 5 (September 1, 2010): 561–74. http://dx.doi.org/10.4141/cjps10003.

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The purpose of this manuscript is to review various statistical models for analyzing genotype × environment interaction (GE). The objective is to present parsimonious approaches other than the standard analysis of variance of the two-way effect model. Some fixed effects linear-bilinear models such as the sites regression model (SREG) are discussed, and a mixed effects counterpart such as the factorial analytic (FA) model is explained. The role of these linear-bilinear models for assessing crossover interaction (COI) is explained. One class of linear models, namely factorial regression (FR) models, and one class of bilinear models, namely partial least squares (PLS) regression, allows incorporating external environmental and genotypic covariables directly into the model. Examples illustrating the use of various statistical models for analyzing GE in the context of plant breeding and agronomy are given. Key words: Least squares, singular value decomposition, environmental and genotypic covariables

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Verbeek, Marno, and Theo Nijman. "Minimum MSE estimation of a regression model with fixed effects from a series of cross-sections." Journal of Econometrics 59, no. 1-2 (September 1993): 125–36. http://dx.doi.org/10.1016/0304-4076(93)90042-4.

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Varewyck, Machteld, Els Goetghebeur, Marie Eriksson, and Stijn Vansteelandt. "On shrinkage and model extrapolation in the evaluation of clinical center performance." Biostatistics 15, no. 4 (May 8, 2014): 651–64. http://dx.doi.org/10.1093/biostatistics/kxu019.

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Abstract We consider statistical methods for benchmarking clinical centers based on a dichotomous outcome indicator. Borrowing ideas from the causal inference literature, we aim to reveal how the entire study population would have fared under the current care level of each center. To this end, we evaluate direct standardization based on fixed versus random center effects outcome models that incorporate patient-specific baseline covariates to adjust for differential case-mix. We explore fixed effects (FE) regression with Firth correction and normal mixed effects (ME) regression to maintain convergence in the presence of very small centers. Moreover, we study doubly robust FE regression to avoid outcome model extrapolation. Simulation studies show that shrinkage following standard ME modeling can result in substantial power loss relative to the considered alternatives, especially for small centers. Results are consistent with findings in the analysis of 30-day mortality risk following acute stroke across 90 centers in the Swedish Stroke Register.

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Shaver, J. Myles. "Interpreting Interactions in Linear Fixed-Effect Regression Models: When Fixed-Effect Estimates Are No Longer Within-Effects." Strategy Science 4, no. 1 (March 2019): 25–40. http://dx.doi.org/10.1287/stsc.2018.0065.

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AmirKhalkhali, Samad, and Sal AmirKhalkhali. "Predictive Efficiency Of Random Effects Approach: A Real Model Simulation Study." Journal of Business & Economics Research (JBER) 11, no. 11 (October 29, 2013): 497. http://dx.doi.org/10.19030/jber.v11i11.8196.

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This real model simulation study attempts to shed more light on the predictive performances of two of the most commonly used panel data regression methods - fixed effects and random effects. In particular, this paper attempts to address the question, How do these two alternative estimators perform in prediction when errors follow non-normal distributions? The simulation results support the random effects approach as the better choice.

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Mielenz, N., H. Krejčová, J. Přibyl, and L. Schüler. "Anpassung eines Fixed Regression Modells für die tägliche Zunahme von Fleckviehbullen mit Hilfe von Informationskriterien." Archives Animal Breeding 50, no. 1 (October 10, 2007): 47–58. http://dx.doi.org/10.5194/aab-50-47-2007.

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Abstract. Title of the paper: Fitting a fixed regression model for daily gain of bulls using information criterion In this study the model choice is demonstrated exemplarily on data of 6405 Czech Simmental bulls using information criterion. Per bull up to 8 observations were available for the trait daily gain. Because the animals showed different age on control day, the expected gain curves were described in the population and within the herd*year*season-classes by second, third or fourth order Legendre polynomials of age. For optimization of the fixed effects and to choice the covariance structure of the repeated records the information criteria of Akaike (AIC), the Bayesian criteria (BIC) and the ICOMP-criteria, developed mainly from Bozdogan, were used. Within and over all covariance structures AIC selected generally the most complex model. On the other hand, BIC and ICOMP favoured a model with second order polynomials of age nested within the head*year*seasonclasses. All criterion selected models with nested second order polynomials within the herd*year*season-classes in comparison to models with non-nested polynomials of age.

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Silk, Matthew J., Xavier A. Harrison, and David J. Hodgson. "Perils and pitfalls of mixed-effects regression models in biology." PeerJ 8 (August 12, 2020): e9522. http://dx.doi.org/10.7717/peerj.9522.

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Biological systems, at all scales of organisation from nucleic acids to ecosystems, are inherently complex and variable. Biologists therefore use statistical analyses to detect signal among this systemic noise. Statistical models infer trends, find functional relationships and detect differences that exist among groups or are caused by experimental manipulations. They also use statistical relationships to help predict uncertain futures. All branches of the biological sciences now embrace the possibilities of mixed-effects modelling and its flexible toolkit for partitioning noise and signal. The mixed-effects model is not, however, a panacea for poor experimental design, and should be used with caution when inferring or deducing the importance of both fixed and random effects. Here we describe a selection of the perils and pitfalls that are widespread in the biological literature, but can be avoided by careful reflection, modelling and model-checking. We focus on situations where incautious modelling risks exposure to these pitfalls and the drawing of incorrect conclusions. Our stance is that statements of significance, information content or credibility all have their place in biological research, as long as these statements are cautious and well-informed by checks on the validity of assumptions. Our intention is to reveal potential perils and pitfalls in mixed model estimation so that researchers can use these powerful approaches with greater awareness and confidence. Our examples are ecological, but translate easily to all branches of biology.

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Kapaya, Saganga Mussa, and Gwahula Raphael. "Bank-specific, Industry-specific and Macroeconomic Determinants of Banks Profitability: Empirical Evidence from Tanzania." International Finance and Banking 3, no. 2 (September 13, 2016): 100. http://dx.doi.org/10.5296/ifb.v3i2.9847.

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The study analyzed effects of bank-specific, industry-specific and macroeconomic determinants on banks profitability. It used a maximum of 350 firm-years, from 52 banks from 1998 to 2010 in Tanzania. It did proxy profitability using return on asset (ROA), return on equity (ROE) and net interest margin (NIM). The static fixed effects regression model indicated that; credit facilities (CFA), capital adequacy (TEA), credit risk (CFR), diversification ratio (DIV), bank risk (BAR) and financial market development (MCAd) were significantly influencing ROA. The dynamic fixed effects regression model indicated that lagged ROA, TEA, loan losses provisions (PLT) and BAR, were significantly influencing ROA.

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Gonçalves, Sílvia. "THE MOVING BLOCKS BOOTSTRAP FOR PANEL LINEAR REGRESSION MODELS WITH INDIVIDUAL FIXED EFFECTS." Econometric Theory 27, no. 5 (March 25, 2011): 1048–82. http://dx.doi.org/10.1017/s0266466610000630.

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In this paper we propose a bootstrap method for panel data linear regression models with individual fixed effects. The method consists of applying the standard moving blocks bootstrap of Künsch (1989, Annals of Statistics 17, 1217–1241) and Liu and Singh (1992, in R. LePage & L. Billiard (eds.), Exploring the Limits of the Bootstrap) to the vector containing all the individual observations at each point in time. We show that this bootstrap is robust to serial and cross-sectional dependence of unknown form under the assumption that n (the cross-sectional dimension) is an arbitrary nondecreasing function of T (the time series dimension), where T → ∞, thus allowing for the possibility that both n and T diverge to infinity. The time series dependence is assumed to be weak (of the mixing type), but we allow the cross-sectional dependence to be either strong or weak (including the case where it is absent). Under appropriate conditions, we show that the fixed effects estimator (and also its bootstrap analogue) has a convergence rate that depends on the degree of cross-section dependence in the panel. Despite this, the same studentized test statistics can be computed without reference to the degree of cross-section dependence. Our simulation results show that the moving blocks bootstrap percentile-t intervals have very good coverage properties even when the degree of serial and cross-sectional correlation is large, provided the block size is appropriately chosen.

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Gurmu, Shiferaw. "Testing for Fixed Effects in Logit and Probit Models Using an Artificial Regression." Econometric Theory 12, no. 5 (December 1996): 872–74. http://dx.doi.org/10.1017/s0266466600007283.

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Baltagi, Badi H. "Testing for Fixed Effects in Logit and Probit Models Using an Artificial Regression." Econometric Theory 11, no. 5 (October 1995): 1179. http://dx.doi.org/10.1017/s0266466600010057.

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Pérez, Betsabé, Isabel Molina, and Daniel Peña. "Outlier detection and robust estimation in linear regression models with fixed group effects." Journal of Statistical Computation and Simulation 84, no. 12 (June 26, 2013): 2652–69. http://dx.doi.org/10.1080/00949655.2013.811669.

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