Journal articles: 'Fixed effect model'

1

Spineli, Loukia M., and Nikolaos Pandis. "Meta-analysis: Fixed-effect model." American Journal of Orthodontics and Dentofacial Orthopedics 157, no. 1 (January 2020): 134–37. http://dx.doi.org/10.1016/j.ajodo.2019.10.008.

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Wang, Qunyong. "Fixed-Effect Panel Threshold Model using Stata." Stata Journal: Promoting communications on statistics and Stata 15, no. 1 (April 2015): 121–34. http://dx.doi.org/10.1177/1536867x1501500108.

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Wang, Hung-Jen, and Chia-Wen Ho. "Estimating fixed-effect panel stochastic frontier models by model transformation." Journal of Econometrics 157, no. 2 (August 2010): 286–96. http://dx.doi.org/10.1016/j.jeconom.2009.12.006.

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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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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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Xin, Yang, Xu Weidong, Xiang Lei, Zhu Wannian, and Tian Jiyao. "A Camouflage Effect Detection Model for Fixed Targets." Journal of Physics: Conference Series 1187, no. 4 (April 2019): 042097. http://dx.doi.org/10.1088/1742-6596/1187/4/042097.

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Gronau, Quentin F., Daniel W. Heck, Sophie W. Berkhout, Julia M. Haaf, and Eric-Jan Wagenmakers. "A Primer on Bayesian Model-Averaged Meta-Analysis." Advances in Methods and Practices in Psychological Science 4, no. 3 (July 2021): 251524592110312. http://dx.doi.org/10.1177/25152459211031256.

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Meta-analysis is the predominant approach for quantitatively synthesizing a set of studies. If the studies themselves are of high quality, meta-analysis can provide valuable insights into the current scientific state of knowledge about a particular phenomenon. In psychological science, the most common approach is to conduct frequentist meta-analysis. In this primer, we discuss an alternative method, Bayesian model-averaged meta-analysis. This procedure combines the results of four Bayesian meta-analysis models: (a) fixed-effect null hypothesis, (b) fixed-effect alternative hypothesis, (c) random-effects null hypothesis, and (d) random-effects alternative hypothesis. These models are combined according to their plausibilities given the observed data to address the two key questions “Is the overall effect nonzero?” and “Is there between-study variability in effect size?” Bayesian model-averaged meta-analysis therefore avoids the need to select either a fixed-effect or random-effects model and instead takes into account model uncertainty in a principled manner.

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Reich, Robin M., and Loukas G. Arvanitis. "Correcting for nonrandom errors in a fixed-effect model." Canadian Journal of Forest Research 19, no. 12 (December 1, 1989): 1550–54. http://dx.doi.org/10.1139/x89-236.

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In this article, a statistical method of estimating the microsite component (a nonrandom error) in a fixed-effect model is presented and discussed. The approach uses generalized least squares to partition the model variance into two components: one part due to microsite and the other one due to random errors. An iterative procedure is then used to solve for the maximum likelihood estimate of the microsite component. Application of the technique to a slash pine (Pinuselliottii Engelm. var. elliottii) spacing trial indicates that 30% of the variability in total tree height at age 6 years was due to microsite, whereas the remaining 70% was due to random errors.

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Lee, Seung-Chun. "A Bayesian inference for fixed effect panel probit model." Communications for Statistical Applications and Methods 23, no. 2 (March 31, 2016): 179–87. http://dx.doi.org/10.5351/csam.2016.23.2.179.

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LeBeau, Brandon, Yoon Ah Song, and Wei Cheng Liu. "Model Misspecification and Assumption Violations With the Linear Mixed Model: A Meta-Analysis." SAGE Open 8, no. 4 (October 2018): 215824401882038. http://dx.doi.org/10.1177/2158244018820380.

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This meta-analysis attempts to synthesize the Monte Carlo (MC) literature for the linear mixed model under a longitudinal framework. The meta-analysis aims to inform researchers about conditions that are important to consider when evaluating model assumptions and adequacy. In addition, the meta-analysis may be helpful to those wishing to design future MC simulations in identifying simulation conditions. The current meta-analysis will use the empirical type I error rate as the effect size and MC simulation conditions will be coded to serve as moderator variables. The type I error rate for the fixed and random effects will be explored as the primary dependent variable. Effect sizes were coded from 13 studies, resulting in a total of 4,002 and 621 effect sizes for fixed and random effects respectively. Meta-regression and proportional odds models were used to explore variation in the empirical type I error rate effect sizes. Implications for applied researchers and researchers planning new MC studies will be explored.

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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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Nugroho, T., A. Nurhidayati, N. Widyas, and S. Prastowo. "Dam effect confirmation on weaning weight of Boer Goat crosses in Indonesia." IOP Conference Series: Earth and Environmental Science 902, no. 1 (November 1, 2021): 012001. http://dx.doi.org/10.1088/1755-1315/902/1/012001.

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Abstract This study aimed to confirm the present of dam effect on weaning weight trait of Boer goat crosses. A total of 1081 weaning weight records (standardized to 77 days) from 527 does and 16 bucks were analyzed. Data were derived from Boer, Boerja F1 (Boer 3 × Jawarandu ?), and Boerja F2 (Boer 3 × Boerja F1 ?). Two statistic models namely Model 1 and Model 2 were compared using F-test for overall significance. Model 1 is Analysis of Variance (ANOVA) which consist only fixed effect as factor, while Model 2 is mixed model which includes fixed effect as factor and dam as a random effect. The fixed effects in both models are buck, doe type, parity of the dam, sex of kid, birth type, and year of observation. Results showed that buck, doe type, sex, birth type, and observation year affect significantly (P<0.05) to weaning weight, while parity had no effect (P=0.53). Based on the model’s comparison, there was a significant difference (P<0.05) between Model 1 and Model 2. Therefore, it is confirmed the present of dam effect on the weaning weight trait of Boer goat crosses in the studied population.

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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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LaHuis, David M., Daniel R. Jenkins, Michael J. Hartman, Shotaro Hakoyama, and Patrick C. Clark. "The effects of misspecifying the random part of multilevel models." Methodology 16, no. 3 (September 30, 2020): 224–40. http://dx.doi.org/10.5964/meth.2799.

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This paper examined the amount bias in standard errors for fixed effects when the random part of a multilevel model is misspecified. Study 1 examined the effects of misspecification for a model with one Level 1 predictor. Results indicated that misspecifying random slope variance as fixed had a moderate effect size on the standard errors of the fixed effects and had a greater effect than misspecifying fixed slopes as random. In Study 2, a second Level 1 predictor was added and allowed for the examination of the effects of misspecifying the slope variance of one predictor on the standard errors for the fixed effects of the other predictor. Results indicated that only the standard errors of coefficient relevant to that predictor were impacted and that the effect size for the bias could be considered moderate to large. These results suggest that researchers can use a piecemeal approach to testing multilevel models with random effects.

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A, Rajarathinam, and Suba S.S. "Panel Data Modeling for Indian Food Grain Production." YMER Digital 21, no. 01 (January 19, 2022): 314–29. http://dx.doi.org/10.37896/ymer21.01/30.

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The present investigation was carried out to study the food grain production trends in selected states of India for the period 2001-02 to 2020-2021. In this study a panel data regression model is used to combat the complicated relations and strong auto-correlation present in the crop production time series data. Panel data modelling control the heterogeneity of cross-sectional units over time. The results reveal that between state-to-state food grain production is highly significant, the highest food grain production was registered in Uttar Pradesh, followed by Punjab and Madhya Pradesh. Very lowest was registered in Kerala and Himachal Pradesh. Levin, Lin & Chu t statistics values are found to be significant indicating that the variable under study is stationary at level and hence the variable under study is I(0).Among the three different models viz., constant coefficient model, fixed effect model and random effect model, the fixed effect model was selected as an appropriate trend model as per the Hausman test. Based on the statistical significance of the estimated coefficients and the substantial increase in the R2 value to 95%, the fixed effect model or the least-square dummy variable regression model performs better than the panel least square regression model. The Hausman test confirms the selection of fixed effect over the random effect model. The fixed effects are positive in Andhra Pradesh, Karnataka, Kerala, Tamil Nadu, Gujarat, Chhattisgrah, Bihar, Haryana and Madhya Pradesh. Negative fixed effects are observed in Himachal Pradesh, Jharkhand, Maharashtra, Odisha, Punjab, Rajasthan, Uttar Pradesh, Uttarakhand and West Bengal. Increases in food grain production have been observed

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Nazarzadeh, Milad, and Zeinab Bidel. "Meta-Analysis of Sleep Duration and Obesity in Children: Fixed Effect Model or Random Effect Model?" Journal of Paediatrics and Child Health 53, no. 9 (September 2017): 923–24. http://dx.doi.org/10.1111/jpc.13667.

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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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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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Ismaeel, Shelan Saied, Habshah Midi, and Muhammed Sani. "Robust Multicollinearity Diagnostic Measure For Fixed Effect Panel Data Model." Malaysian Journal of Fundamental and Applied Sciences 17, no. 5 (October 30, 2021): 636–46. http://dx.doi.org/10.11113/mjfas.v17n5.2391.

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It is now evident that high leverage points (HLPs) can induce the multicollinearity pattern of a data in fixed effect panel data model. Those observations that are responsible for this phenomenon are called high leverage collinearity-enhancing observations (HLCEO). The commonly used within group ordinary least squares (WOLS) estimator for estimating the parameters of fixed effect panel data model is easily affected by HLCEOs. In their presence, the WOLS estimates may produce large variances and this would lead to erroneous interpretation. Therefore, it is imperative to detect the multicollinearity which is caused by HLCEOs. The classical Variance Inflation Factor (CVIF) is the commonly used diagnostic method for detecting multicollinearity in panel data. However, it is not correctly diagnosed multicollinearity in the presence of HLCEOs. Hence, in this paper three new robust diagnostic methods of diagnosing multicollinearity in panel data are proposed, namely the RVIF (WGM-FIMGT), RVIF (WGM-DRGP) and RVIF (WMM) and compared their performances with the CVIF. The numerical evidences show that the CVIF incorrectly diagnosed multicollinearity but our proposed methods correctly diagnosed no multicollinearity in the presence of HLCEOs where RVIF (WGM-FIMGT) being the best method as it has the least computational running time.

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Wang, Xiaodi, Yingshan Zhang, and Yincai Tang. "Feasible criterion for designs based on fixed effect ANOVA model." Statistics & Probability Letters 87 (April 2014): 134–42. http://dx.doi.org/10.1016/j.spl.2014.01.020.

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NAKANISHI, Hiroki, and Hiroaki MIMIZUKA. "An Estimation of the Effects of Class Size using a Fixed Effect Model:." Journal of Educational Sociology 104 (June 30, 2019): 215–36. http://dx.doi.org/10.11151/eds.104.215.

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Utami, Bernadhita Herindri Samodera, Agus Irawan, Miswan Gumanti, and Gilang Primajati. "Hausman and Taylor Estimator Analysis on The Linear Data Panel Model." Jurnal Varian 5, no. 1 (November 10, 2021): 81–88. http://dx.doi.org/10.30812/varian.v5i1.1481.

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Panel data modelling in the field of econometrics applies two main approaches, namely fixed effect estimators and random effects. The application of the Hausman and Taylor estimator to real data is used to test for fixed effects or random effects based on the idea that the set of estimated coefficients obtained from the fixed effect estimates is taken as a group. A good estimator is an estimator that is as close as possible to represent the characteristics of the population. The characteristics of a good estimator include unbiasedness, efficiency, and consistency. The purpose of this study is to identify the properties of the Hausman and Taylor estimator in the linear model of panel data. Based on the analysis using panel data, it is found that the Hausman and Taylor estimator on the random effects panel data is an estimator that is consistent and efficient even though it is not unbiased.

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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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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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Liu, Wen-Chien. "Trade-off theory of capital structure: evidence from estimations of non-parametric and semi-parametric panel fixed effect models." Investment Management and Financial Innovations 14, no. 1 (March 31, 2017): 115–23. http://dx.doi.org/10.21511/imfi.14(1).2017.12.

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A firm’s capital structure decisions constitute an essential research topic academically and practically. In this study, the author uses the data of US listed firms to test the traditional trade-off theory of capital structure, which posits that firms should balance the benefit of tax shields and costs of financial distress to purse an optimal debt ratio. Therefore, to determine the complex relationship between firm value and debt ratio and avoid the problem of model misspecification, the author adopts the non-parametric fixed effect model and semi-parametric (partially linear) fixed effect model. Our empirical results reveal that a nonlinear and asymmetric relationship exists between firm value and market debt ratio, thus, considerably supporting trade-off theory. Moreover, the use of different definitions of key variables and various kernel functions engenders robust results. Overall, the author suggests that firm managers should employ financial leverages appropriately to maximize firm value.

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Westfall, Jacob, Thomas E. Nichols, and Tal Yarkoni. "Fixing the stimulus-as-fixed-effect fallacy in task fMRI." Wellcome Open Research 1 (December 9, 2016): 23. http://dx.doi.org/10.12688/wellcomeopenres.10298.1.

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Most functional magnetic resonance imaging (fMRI) experiments record the brain’s responses to samples of stimulus materials (e.g., faces or words). Yet the statistical modeling approaches used in fMRI research universally fail to model stimulus variability in a manner that affords population generalization, meaning that researchers’ conclusions technically apply only to the precise stimuli used in each study, and cannot be generalized to new stimuli. A direct consequence of this stimulus-as-fixed-effect fallacy is that the majority of published fMRI studies have likely overstated the strength of the statistical evidence they report. Here we develop a Bayesian mixed model (the random stimulus model; RSM) that addresses this problem, and apply it to a range of fMRI datasets. Results demonstrate considerable inflation (50-200% in most of the studied datasets) of test statistics obtained from standard “summary statistics”-based approaches relative to the corresponding RSM models. We demonstrate how RSMs can be used to improve parameter estimates, properly control false positive rates, and test novel research hypotheses about stimulus-level variability in human brain responses.

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Westfall, Jacob, Thomas E. Nichols, and Tal Yarkoni. "Fixing the stimulus-as-fixed-effect fallacy in task fMRI." Wellcome Open Research 1 (March 17, 2017): 23. http://dx.doi.org/10.12688/wellcomeopenres.10298.2.

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Most functional magnetic resonance imaging (fMRI) experiments record the brain’s responses to samples of stimulus materials (e.g., faces or words). Yet the statistical modeling approaches used in fMRI research universally fail to model stimulus variability in a manner that affords population generalization, meaning that researchers’ conclusions technically apply only to the precise stimuli used in each study, and cannot be generalized to new stimuli. A direct consequence of this stimulus-as-fixed-effect fallacy is that the majority of published fMRI studies have likely overstated the strength of the statistical evidence they report. Here we develop a Bayesian mixed model (the random stimulus model; RSM) that addresses this problem, and apply it to a range of fMRI datasets. Results demonstrate considerable inflation (50-200% in most of the studied datasets) of test statistics obtained from standard “summary statistics”-based approaches relative to the corresponding RSM models. We demonstrate how RSMs can be used to improve parameter estimates, properly control false positive rates, and test novel research hypotheses about stimulus-level variability in human brain responses.

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Boleckova, J., F. Christensen O, P. Sørensen, and G. Sahana. "Strategies for haplotype-based association mapping in a complex pedigreed population." Czech Journal of Animal Science 57, No. 1 (January 27, 2012): 1–9. http://dx.doi.org/10.17221/5478-cjas.

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In association mapping, haplotype-based methods are generally regarded to provide higher power and increased precision than methods based on single markers. For haplotype-based association mapping most studies use a fixed haplotype effect in the model. However, an increase in haplotype length raises the number of parameters in the model, resulting in low accuracy of the estimates especially for the low-frequency haplotypes. Modeling of haplotype effects can be improved if they are assumed to be random effects, as only one parameter, i.e. haplotype variance, needs to be estimated compared to estimating the effects of all different haplotypes in a fixed haplotype model. Using simulated data, we investigated statistical models where haplotypes were fitted either as a fixed or random effect and we compared them for the power, precision, and type I error. We investigated five haplotype lengths of 2, 4, 6, 10 and 20. The simulated data resembled the Danish Holstein cattle pedigree representing a complex relationship structure and QTL effects of different sizes were simulated. We observed that the random haplotype models had high power and very low type I error rates (after the Bonferroni correction), while the fixed haplotype models had lower power and excessively high type I errors. Haplotype length of 4 to 6 gave the best results for random model in the present study. Though the present study was conducted on data structure more frequent in livestock, our findings on random vs. fixed haplotype effects in association mapping models are applicable to data from other species with a similar pedigree structure.  

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Duxbury, Scott W. "A General Panel Model for Unobserved Time Heterogeneity with Application to the Politics of Mass Incarceration." Sociological Methodology 51, no. 2 (May 25, 2021): 348–77. http://dx.doi.org/10.1177/00811750211016033.

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Panel data analysis is common in the social sciences. Fixed effects models are a favorite among sociologists because they control for unobserved heterogeneity (unexplained variation) among cross-sectional units, but estimates are biased when there is unobserved heterogeneity in the underlying time trends. Two-way fixed effects models adjust for unobserved time heterogeneity but are inefficient, cannot include unit-invariant variables, and eliminate common trends: the portion of variance in a time-varying variable that is invariant across cross-sectional units. This article introduces a general panel model that can include unit-invariant variables, corrects for unobserved time heterogeneity, and provides the effect of common trends while also allowing for unobserved unit heterogeneity, time-varying coefficients, and time-invariant variables. One-way and two-way fixed effects models are shown to be restrictive forms of this general model. Other restrictive forms are also derived that offer all the usual advantages of one-way and two-way fixed effects models but account for unobserved time heterogeneity. The author uses the models to examine the increase in state incarceration rates between 1970 and 2015.

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Bermann, Matias, Daniela Lourenco, Vivian Breen, Rachel Hawken, Fernando Brito Lopes, and Ignacy Misztal. "PSXII-9 Modeling genetic differences of combined broiler chicken populations in single-step GBLUP." Journal of Animal Science 99, Supplement_3 (October 8, 2021): 254. http://dx.doi.org/10.1093/jas/skab235.464.

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Abstract The objectives of this study were to model the inclusion of a group of external birds into a local broiler chicken population for the purpose of genomic evaluations and evaluating the behavior of two accuracy estimators under different model specifications. The pedigree was composed by 242,413 birds and genotypes were available for 107,216 birds. A five-trait model that included one growth, two yield, and two efficiency traits was used for the analyses. The strategies to model the introduction of external birds were to include a fixed effect representing the origin of parents and to use UPG or metafounders. Genomic estimated breeding values (GEBV) were obtained with single-step GBLUP (ssGBLUP) using the Algorithm for Proven and Young (APY). Bias, dispersion, and accuracy of GEBV for the validation birds, i.e., from the most recent generation, were computed. The bias and dispersion were estimated with the LR-method, whereas accuracy was estimated by the LR-method and predictive ability. Models with fixed UPG and estimated inbreeding or random UPG resulted in similar GEBV. The inclusion of an extra fixed effect in the model made the GEBV unbiased and reduced the inflation, while models without such an effect were significantly biased. Genomic predictions with metafounders were slightly biased and inflated due to the unbalanced number of observations assigned to each metafounder. When combining local and external populations, the greatest accuracy and smallest bias can be obtained by adding an extra fixed effect to account for the origin of parents plus UPG with estimated inbreeding or random UPG. To estimate the accuracy, the LR-method is more consistent among models, whereas predictive ability greatly depends on the model specification, that is, on the fixed effects included in the model. When changing model specification, the largest variation for the LR-method was 20%, while for predictive ability was 110%.

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Taqiyyuddin, Teguh Ammar, and Muhammad Irfan Rizki. "Pemodelan Fixed Effect Panel Spatial Durbin Error Model Pada Tingkat Kemiskinan." Seminar Nasional Official Statistics 2021, no. 1 (November 1, 2021): 90–98. http://dx.doi.org/10.34123/semnasoffstat.v2021i1.767.

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Permasalahan yang ada di setiap negara khususnya negara berkembang termasuk Indonesia adalah kemiskinan. Program dalam mengentaskan kemiskinan merupakan pokok tujuan dari Sustainable Development Goals (SDGs). Jawa Barat yang merupakan salah satu provinsi dengan jumlah penduduk miskin terbanyak perlu mengatasi permasalah tersebut seperti yang tertuang dalam RPJMD. Dalam hal ini pemerintah seringkali menentukan pembangunan dengan memprioritaskan pembangunan ekonomi pada daerah perkotaan ataupun pusat perekonomian yang mengakibatkan daerah lainnya tertinggal dan kemiskinan menjadi tidak merata. Hal tersebut tentunya memperlihatkan faktor yang berhubungan dengan ekonomi diduga terdapat aspek spasial sehingga harus menggunakan spasial lag variabel prediktor sebagai prediktor variabel, selain itu kemiskinan merupakan masalah multidimensial sehingga banyak faktor yang mempengaruhi tingkat kemiskinan tidak dimasukkan ke dalam pemodelan. Variabel prediktor yang tidak dimasukkan ke dalam pemodelan dinamakan omitted variables. Berdasarkan permasalahan itu, dalam mengetahui faktor-faktor kemiskinan di Jawa Barat diperlukan suatu pendekatan yang mampu mengakomodasi lag spasial prediktor variabel dan error model yang berkorelasi spasial, serta mampu mengatasi bias taksiran akibat omitted variables. Maka dalam penelitian ini dilakukan pendekatan model regresi spasial Durbin Error Model. Pembobot spasial yang digunakan yaitu queen contiguity. Berdasarkan penelitian ini didapatkan bahwa variabel Indeks Pembanguna Manusia (IPM) dan persentase penduduk berpengaruh terhadap tingkat kemiskinan di Provinisi Jawa Barat, dengan nilai R-Square sebesar 98%. Maka hasil tersebut diharapkan dapat menjadi pertimbangan bagi pemerintah Jawa Barat untuk menanggulangi masalah kemiskinan dalam upaya mencapai tujuan pertama SDGs yaitu tanpa kemiskinan.

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Kang, Sung Jin, and Myoung-jae Lee. "Analysis of private transfers with panel fixed-effect censored model estimator." Economics Letters 80, no. 2 (August 2003): 233–37. http://dx.doi.org/10.1016/s0165-1765(03)00083-1.

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Hahn, Jinyong, and Hyungsik Roger Moon. "PANEL DATA MODELS WITH FINITE NUMBER OF MULTIPLE EQUILIBRIA." Econometric Theory 26, no. 3 (October 7, 2009): 863–81. http://dx.doi.org/10.1017/s0266466609990132.

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We study a nonlinear panel data model in which the fixed effects are assumed to have finite support. The fixed effects estimator is known to have the incidental parameters problem. We contribute to the literature by making a qualitative observation that the incidental parameters problem in this model may not be not as severe as in the conventional case. Because fixed effects have finite support, the probability of correctly identifying the fixed effect converges to one even when the cross sectional dimension grows as fast as some exponential function of the time dimension. As a consequence, the finite sample bias of the fixed effects estimator is expected to be small.

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Zavadilová, L., E. Němcová, J. Přibyl, and J. Wolf. "Definition of subgroups for fixed regression in the test-day animal model for milk production ofHolsteincattle in theCzech Republic." Czech Journal of Animal Science 50, No. 1 (December 5, 2011): 7–13. http://dx.doi.org/10.17221/3976-cjas.

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The investigation was based on roughly 3.9, 2.7 and 1.7 million test-day records from first, second and third lactation, respectively, sampled from 596 200 Czech Holstein cows between the years 1991 and 2002. Breeding values were estimated from multi-lactation random-regression test-day models which contained the fixed effect of herd-test day, fixed regression on days in milk and random regressions on the animal level and the permanent environmental effect. Third degree Legendre polynomials (with four coefficients) were used for both the fixed and random regressions. The models differed in fixed regression. In Analysis I, 96 subclasses were defined according to age at calving, season and year of calving within lactation. In Analysis II, days open were additionally included as a grouping factor resulting in 480 subclasses. Rank correlations over 0.98 between both analyses were observed for breeding values for sires. Grouping according to Analysis I was recommended.  

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Cherry, Joshua L. "Should We Expect Substitution Rate to Depend on Population Size?" Genetics 150, no. 2 (October 1, 1998): 911–19. http://dx.doi.org/10.1093/genetics/150.2.911.

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Abstract The rate of nucleotide substitution is generally believed to be a decreasingfunction of effective population size, at least for nonsynonymous substitutions. This view was originally based on consideration of slightly deleterious mutations with a fixed distribution of selection coefficients. A realistic model must include the occurrence and fixation of some advantageous mutations that compensate for the loss of fitness due to deleterious substitutions. Some such models, such as so-called “fixed” models, also predict a population size effect on substitution rate. An alternative model, presented here, predicts the near absence of a population size effect on substitution rate. This model is based on concave log-fitness functions and a fixed distribution of mutational effects on the selectively important trait. Simulations of an instance of the model confirm the approximate insensitivity of the substitution rate to population size. Although much experimental evidence has been claimed to support the existence of a population size effect, the body of evidence as a whole is equivocal, and much of the evidence that is supposed to demonstrate such an effect would also suggest that it is very small. Perhaps the proposed model applies well to some genes and not so well to others, and genes therefore vary with regard to the population size effect.

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Barber, T. J., E. Leonardi, and R. D. Archer. "A Technical Note on the appropriate CFD boundary conditions for the prediction of ground effect aerodynamics." Aeronautical Journal 103, no. 1029 (November 1999): 545–47. http://dx.doi.org/10.1017/s0001924000064368.

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The accurate prediction of ground effect aerodynamics is an important aspect of wing-in-ground effect vehicle (WIG) design. Computational fluid dynamics (CFD) solutions are useful alternatives to expensive (especially in the case of ground effect) wind-tunnel testing. However, the incorporation of the rigid surface effects often leads to confusion due to such a model being in a vehicle fixed reference frame (air moving, vehicle fixed) rather than the real-life situation of a ground fixed reference frame (air fixed, vehicle moving).

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Barbosa, Leandro, Paulo Sávio Lopes, Adair José Regazzi, Robledo de Almeida Torres, Mário Luiz Santana Júnior, and Renata Veroneze. "Estimation of variance components, genetic parameters and genetic trends for litter size of swines." Revista Brasileira de Zootecnia 39, no. 10 (October 2010): 2155–59. http://dx.doi.org/10.1590/s1516-35982010001000008.

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Records of Large White breed animals were used to estimate variance components, genetic parameters and trends for the character total number of born piglets (TNBP) as measure of litter size. For obtaining variance components and genetic parameters, it was used the Restricted Maximum Likelihood Method using MTDFREML software. Two mixed models (additive and repeatability) were evaluated. The additive model contained fixed effect of the contemporary group and the following random effects: direct additive genetic and residual effect for the first parturition. Repeatability model had the same effects of the additive model plus parturition order fixed effect and non-correlated animal permanent environment random effect for the second, third and forth parturition. Direct additive heritability estimates for TNBP were 0.15 and 0.20 for the additive and repeatability models, respectively. The estimate of the ration among variance of the non-correlated effect of animal permanent environment effect and the phenotypic variance, expressed as total variance proportion (c2) was 0.09. The estimates of yearly genetic trends obtained in the additive and repeatability models have similar behaviors (0.02 piglets/sow/year).

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Baetschmann, Gregori, Alexander Ballantyne, Kevin E. Staub, and Rainer Winkelmann. "feologit: A new command for fitting fixed-effects ordered logit models." Stata Journal: Promoting communications on statistics and Stata 20, no. 2 (June 2020): 253–75. http://dx.doi.org/10.1177/1536867x20930984.

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In this article, we describe how to fit panel-data ordered logit models with fixed effects using the new community-contributed command feologit. Fixed-effects models are increasingly popular for estimating causal effects in the social sciences because they flexibly control for unobserved time-invariant heterogeneity. The ordered logit model is the standard model for ordered dependent variables, and this command is the first in Stata specifically for this model with fixed effects. The command includes a choice between two estimators, the blowup and cluster (BUC) estimator introduced in Baetschmann, Staub, and Winkelmann (2015, Journal of the Royal Statistical Society, Series A 178: 685–703) and the BUC- τ estimator in Baetschmann (2012, Economics Letters 115: 416–418). Baetschmann, Staub, and Winkelmann (2015) showed that the BUC estimator has good properties and is almost as efficient as more complex estimators such as generalized method-of-moments and empirical likelihood estimators. The command and model interpretations are illustrated with an analysis of the effect of parenthood on life satisfaction using data from the German Socio-Economic Panel.

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Slaney, Kathleen L., Donna Tafreshi, and Richard Hohn. "Random or Fixed? An Empirical Examination of Meta-Analysis Model Choices." Review of General Psychology 22, no. 3 (September 2018): 290–304. http://dx.doi.org/10.1037/gpr0000140.

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When conducting meta-analyses, researchers must make decisions about which statistical model is most appropriate for the specific context and aims of the meta-analysis. Although there are several meta-analysis models, most researchers choose between two general models: fixed-effect (FE) and random-effects (RE). Yet, the basis on which these two general models are distinguished and of when it is appropriate to use one or the other varies in the methodological literature. Although model-to-inference inconsistencies have been previously noted, there has been little empirical investigation of whether, and to what extent, the varying conceptualizations of the distinctions between FE and RE models are reflected in published meta-analyses. The present study explores whether conceptualizations of model distinctions among psychological researchers are consistent with those in the methods literature. We also examine model choices and rationales given by psychological researchers in two samples of published meta-analyses in psychology-related journals. We identify four primary categories for distinguishing between FE and RE models, only two of which were predominant in our samples. Although model choice appears to be reported at a moderately high rate, many researchers continue not to provide explicit rationales for their model choices or do not clearly tie model choices to the specific research aims of the meta-analyses. Implications of these findings are discussed.

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ASTUTI, NI MADE ARY DHARMA WIDYA, MADE SUSILAWATI, and NI LUH PUTU SUCIPTAWATI. "IMPLEMENTASI DATA PANEL SPASIAL TERHADAP TINGKAT PRODUK DOMESTIK REGIONAL BRUTO DI PROVINSI BALI." E-Jurnal Matematika 10, no. 2 (May 24, 2021): 46. http://dx.doi.org/10.24843/mtk.2021.v10.i02.p319.

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Gross Regional Domestic Product (GRDP) is an economic indicator to see the economic movements of a region during a certain period, whether based on current and constant price. Economic activities in a region use the GRDP calculation based on current prices by industrial base year 2010. In 2019, Bali's economic growth increased by , exceeding national economic growth of . Using spatial panel data in analysis consists of common effect model, fixed individual effect model, fixed time effect model, random effect model, and spatial lag fixed effect model. The best model to modeling GRDP Bali Province is spatial lag fixed effect which has a difference in constant values ??at any time, with of 99.41 percent, the remaining is explained by other variables not examined

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Saptanto, Subhechanis, and Widyono Soetjitpto. "ANALISIS MODEL EKSPOR KOMODITAS PERIKANAN INDONESIA DENGAN PENDEKATAN GRAVITY MODEL." Jurnal Sosial Ekonomi Kelautan dan Perikanan 5, no. 2 (July 17, 2017): 169. http://dx.doi.org/10.15578/jsekp.v5i2.5799.

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Penelitian ini bertujuan untuk mengetahui potensi ekspor perikanan Indonesia di 28 negara tujuan ekspor dan faktor-faktor yang mempengaruhi nilai ekspor perikanan dengan menggunakan pendekatan Gravity Model yang diaplikasikan dalam subsektor perikanan. Variabel-variabel yang digunakan antara lain nilai ekspor riil, GDP nominal, jumlah penduduk, jarak relatif, nilai tukar riil efektif dan interaksi antara tarif dengan dummy integrasi ekonomi. Metode analisis yang digunakan adalah analisis data panel dengan menggunakan fixed effect. Hasil penelitian menunjukkan bahwa secara umum seluruh variabel berpengaruh secara signifikan kecuali untuk variabel nilai tukar riil efektif Indonesia. Tanda variabel yang berlawanan dengan hipotesis adalah variabel jumlah penduduk mitra dagang yang seharusnya bernilai positif dan interaksi antara tarif dengan APEC yang seharusnya bernilai negatif. Peningkatan jumlah penduduk mitra dagang menyebabkan penurunan nilai ekspor. Variabel interaksi tarif dan APEC bernilai positif karena tujuan ekspor perikanan Indonesia lebih banyak ke Amerika Serikat dan Jepang yang memang merupakan anggota dari APEC. Berdasarkan hasil estimasi model dengan menggunakan fixed effect diperoleh informasi bahwa terdapat lima negara yang menjadi tujuan ekspor komoditas perikanan Indonesia, yakni Amerika Serikat, China, Mesir, Inggris dan Jepang. Title: Analysis Model of Indonesian Fisheries Export Commodities by Gravity Model Approach.This research aims to analyze export of fisheries products to 28 countries as potentials market and related factors on fisheries commodities export values by using Gravity Model approach. It uses several variables including export value, nominal GDP, population, real effective exchange rate, relative distance and interaction between tariff and dummy of economic integration. Method of analysis uses a panel data analysis with fixed effect. Results of this research show that all variables provide significant influences, except for real effective exchange rates on Indonesian currency. Trade partner population and interaction between tariff and APEC dummy are not in favor of the hypothesis. Increasing of trade partner population decreases value export. Interaction between tariff and APEC dummy increase value export because large proportions of fish export go United States of America and Japan and members of APEC. It means Indonesia can still export fisheries commodities even in increasing tariff. By using fixed effect approach, this research estimate big five targeted export countries namely United States of America, China, Egypt, England and Japan.

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Elechi, Promise. "Path Loss Prediction Model for GSM Fixed Wireless Access." European Journal of Engineering and Technology Research 1, no. 1 (July 27, 2018): 1–4. http://dx.doi.org/10.24018/ejeng.2016.1.1.68.

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This research investigated the effect of building materials on GSM signals quality. Measurements were conducted on MTN, Glo, Airtel and Etisalat networks using Radio Frequency Signal Tracker on six different building patterns. The results showed that building with alucoboard wall cladding had the highest signal loss while the sandcrete building/unrusted corrugated iron sheet roof had the least signal loss. Also, a model to predict signal penetration through building walls was developed. It was developed using the principles of Fresnel Refraction Coefficient and the knife-edge diffraction. The total losses from the transmitter to the receiver was modelled as a combination of three different effects; losses due to free-space propagation from transmitter to building; the penetration loss was modelled as a combination of the wall penetration loss and the diffraction loss. The results show that despite the condition of the building walls, movement of people in the environment/room also affected the wireless signal quality as well as the chairs and gadgets in the room. The indoor signal path loss in the rooms increased from when the walls were plastered and continued until when the walls were covered with curtains, both rooms reduced by 4dBm. The mean squared error ranged between 1.6dBm and 2.1dBm with a standard deviation between 11.1 and 11.5.

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Manfredi, E. J., M. San Cristobal, and J. L. Foulley. "Some factors affecting the estimation of genetic parameters for cattle dystocia under a threshold model." Animal Science 53, no. 2 (October 1991): 151–56. http://dx.doi.org/10.1017/s0003356100020067.

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AbstractGenetic parameters for dystocia in the Main-Anjou breed were estimated. Data consisted of 28 178 birth records collected between 1978 and 1989 in 995 herds, with 161, 71 and 12 415 sires, maternal grandsires and dams, respectively, represented. Original scores (1 through 5) were collapsed in order to set two dystocia definitions: dystocia 1 (scores 1+2 v. 3+4+5) and dystocia 2 (scores 1 v. 2+3+4+5). Four models were proposed for genetic parameter estimation: (1) fixed effects plus sire effects; (2) model 1 plus maternal grandsire effect; (3) model 2 plus dam within maternal grandsire effects; (4) same as model 3 but a random effect ‘herds’ replaced a fixed effect ‘regions’. Two methods of fitting models were applied: marginal maximum likelihood and the ‘tilde-hat’ approach. Estimates of genetic parameters by the two methods were similar. Models ignoring maternal effects overestimated the heritability of direct effects especially in the case of dystocia 2. Dystocia definition was responsible for the greatest difference among estimated genetic parameters. Possible reasons for this are discussed. When analysing large data sets, it is recommended judiciously to collapse dystocia categories and to apply approximate statistical procedures to complete models including maternal effects.

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Park, Minho, Dongmin Lee, and Jinwoo Jeon. "Random Parameter Negative Binomial Model of Signalized Intersections." Mathematical Problems in Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/1436364.

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Factors affecting accident frequencies at 72 signalized intersections in the Gyeonggi-Do (province) over a four-year period (2007~2010) were explored using the random parameters negative binomial model. The empirical results from the comparison with fixed parameters binomial model show that the random parameters model outperforms its fixed parameters counterpart and provides a fuller understanding of the factors which determine accident frequencies at signalized intersections. In addition, elasticity and marginal effect were estimated to gain more insight into the effects of one-percent and one-unit changes in the dependent variable from changes in the independent variables.

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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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Schröder, W., K. F. Stock, and O. Distl. "Genetic evaluation of Hanoverian warmblood horses for conformation traits considering the proportion of genes of foreign breeds." Archives Animal Breeding 53, no. 4 (October 10, 2010): 377–87. http://dx.doi.org/10.5194/aab-53-377-2010.

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Abstract. Conformation data of in total 29 053 Hanoverian warmblood mares were used to determine whether genetic evaluation for conformation in the Hanoverian could benefit from the inclusion of the proportion of genes of foreign breeds in the model. For our analyses, we considered all Hanoverian mares born from 1992 to 2005 with available studbook inspection data. Genetic parameters were estimated univariately for eight routinely scored conformation traits (head, neck, saddle position, frontlegs, hindlegs, type, frame, and general impression and development), and height at withers from studbook inspections, in a linear animal model using Residual Maximum Likelihood (REML). Genetic evaluation was subsequently performed using Best Linear Unbiased Prediction. To investigate the effect of correcting for the proportion of genes of foreign breeds, two different models were used for the analyses. In Model 1, the fixed effect age at studbook inspection, and the random effect date-place interaction were considered. In Model 2, proportions of genes of Thoroughbred, Trakehner and Holsteiner were additionally included as fixed effects. Heritabilities of analyzed conformation traits and withers height ranged in both models between 0.10 and 0.57, with standard errors of ≤0.01. Pearson correlation coefficients determined between breeding values of corresponding traits using Model 1 and 2 were highly positive (>0.99), indicating little effect of the model on the results of genetic evaluation. According to the results using a model which includes the proportion of genes of Thoroughbred, Trakehner and Holsteiner as fixed effects will not relevantly improve genetic evaluation for conformation in the Hanoverian.

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Yuan, Jiangle, Yaning Li, and Yayu Li. "Forecast China’s monthly urban fixed asset investment based on the ARIMA model." E3S Web of Conferences 290 (2021): 02017. http://dx.doi.org/10.1051/e3sconf/202129002017.

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In recent years, as China’s urbanization level has risen, China’s urban fixed asset investment has also been rising. Judging from monthly data, China’s urban fixed asset investment has shown a volatile upward trend, with an obvious 11-month cycle. And in each cycle, the fluctuation range of the investment amount is getting larger. This paper uses an ARIMA model with an additive seasonal effect to fit the monthly urban fixed asset investment sequence and predict the future investment. In the end, this paper established a fitting model for China’s urban fixed asset investment, and obtained a good forecasting effect.

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Wantah, Feiby Novita, Agustin Fadjarenie, and Dwi Asih Surjandari. "Does Financial Reporting Quality Moderate Factors Affecting Fraud Tendency?" Saudi Journal of Economics and Finance 6, no. 7 (July 16, 2022): 244–56. http://dx.doi.org/10.36348/sjef.2022.v06i07.004.

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This research examines the influence of Narcissism, Board of Directors Bonus Scheme, Age, Gender and Term of Service with the moderating variable of Financial Reporting Quality on the tendency to commit fraud. Research respondents are CEOs of banks listed on the Indonesia Stock Exchange in 2015-2018 and the sampling technique uses the Purposive Sampling method. This study uses statistical regression analysis to see the effect of the independent variable on the dependent variable or the response to the moderating variable. The regression parameter test consists of the F test, to find out whether the independent variable has a simultaneous effect on the response variable or not. Then the t test, to test the effect of the independent variables one by one on the response variable. By using panel data regression modeling using an unweighted Fixed Effect Model and a weighted Fixed Effect Model, also using partial testing on the unweighted Fixed Effect model and the weighted Fixed Effect model. The results showed that panel data regression modeling using the unweighted Fixed Effect Model resulted in a significant simultaneous test, meaning that the variables of narcissism, directors' bonus scheme, age, gender and tenure affect the tendency to commit fraud simultaneously. Partial testing on the unweighted Fixed Effect model shows that individually only the tenure of service variable affects the tendency to commit fraud. The Fixed Effect Model with weights produces significant simultaneous tests, meaning that simultaneously the variables of narcissism, directors' bonus scheme, age, gender and years of service affect the tendency to commit fraud. And a partial test on the Fixed Effect model with weights shows that individually there are no independent variables that affect the tendency to commit fraud.

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Kim, DaeHwan. "Estimation of Wealth and Income Effect Utilizing Two-Way Fixed Effect Model and Policy Implications." Journal of Housing and Urban Finance 4, no. 2 (December 2019): 27–49. http://dx.doi.org/10.38100/jhuf.2019.4.2.27.

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Hedges, Larry V. "Meta-Analysis." Journal of Educational Statistics 17, no. 4 (December 1992): 279–96. http://dx.doi.org/10.3102/10769986017004279.

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The use of statistical methods to combine the results of independent empirical research studies (meta-analysis) has a long history. Meta-analytic work can be divided into two traditions: tests of the statistical significance of combined results and methods for combining estimates across studies. The principal classes of combined significance tests are reviewed, and the limitations of these tests are discussed. Fixed effects approaches treat the effect magnitude parameters to be estimated as a consequence of a model involving fixed but unknown constants. Random effects approaches treat effect magnitude parameters as if they were sampled from a universe of effects and attempt to estimate the mean and variance of the hyperpopulation of effects. Mixed models incorporate both fixed and random effects. Finally, areas of current research are summarized, including methods for handling missing data, models for publication selection, models to handle studies that are not independent, and distribution-free models for random effects.

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Journal articles on the topic 'Fixed effect model'

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