Relevant bibliographies by topics / Fixed effects regression model

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Fixed effects regression model'

Author: Grafiati
Published: 5 September 2021
Last updated: 18 February 2022

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Journal articles on the topic "Fixed effects regression model"

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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Fixed effects regression model"

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Abdel-Salam, Abdel-Salam Gomaa. "Profile Monitoring with Fixed and Random Effects using Nonparametric and Semiparametric Methods." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/29387.

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Profile monitoring is a relatively new approach in quality control best used where the process data follow a profile (or curve) at each time period. The essential idea for profile monitoring is to model the profile via some parametric, nonparametric, and semiparametric methods and then monitor the fitted profiles or the estimated random effects over time to determine if there have been changes in the profiles. The majority of previous studies in profile monitoring focused on the parametric modeling of either linear or nonlinear profiles, with both fixed and random effects, under the assumption of correct model specification. Our work considers those cases where the parametric model for the family of profiles is unknown or at least uncertain. Consequently, we consider monitoring profiles via two techniques, a nonparametric technique and a semiparametric procedure that combines both parametric and nonparametric profile fits, a procedure we refer to as model robust profile monitoring (MRPM). Also, we incorporate a mixed model approach to both the parametric and nonparametric model fits. For the mixed effects models, the MMRPM method is an extension of the MRPM method which incorporates a mixed model approach to both parametric and nonparametric model fits to account for the correlation within profiles and to deal with the collection of profiles as a random sample from a common population. For each case, we formulated two Hotelling's T 2 statistics, one based on the estimated random effects and one based on the fitted values, and obtained the corresponding control limits. In addition,we used two different formulas for the estimated variancecovariance matrix: one based on the pooled sample variance-covariance matrix estimator and a second one based on the estimated variance-covariance matrix based on successive differences. A Monte Carlo study was performed to compare the integrated mean square errors (IMSE) and the probability of signal of the parametric, nonparametric, and semiparametric approaches. Both correlated and uncorrelated errors structure scenarios were evaluated for varying amounts of model misspecification, number of profiles, number of observations per profile, shift location, and in- and out-of-control situations. The semiparametric (MMRPM) method for uncorrelated and correlated scenarios was competitive and, often, clearly superior with the parametric and nonparametric over all levels of misspecification. For a correctly specified model, the IMSE and the simulated probability of signal for the parametric and theMMRPM methods were identical (or nearly so). For the severe modelmisspecification case, the nonparametric andMMRPM methods were identical (or nearly so). For the mild model misspecification case, the MMRPM method was superior to the parametric and nonparametric methods. Therefore, this simulation supports the claim that the MMRPM method is robust to model misspecification. In addition, the MMRPM method performed better for data sets with correlated error structure. Also, the performances of the nonparametric and MMRPM methods improved as the number of observations per profile increases since more observations over the same range of X generally enables more knots to be used by the penalized spline method, resulting in greater flexibility and improved fits in the nonparametric curves and consequently, the semiparametric curves. The parametric, nonparametric and semiparametric approaches were utilized for fitting the relationship between torque produced by an engine and engine speed in the automotive industry. Then, we used a Hotelling's T 2 statistic based on the estimated random effects to conduct Phase I studies to determine the outlying profiles. The parametric, nonparametric and seminonparametric methods showed that the process was stable. Despite the fact that all three methods reach the same conclusion regarding the –in-control– status of each profile, the nonparametric and MMRPM results provide a better description of the actual behavior of each profile. Thus, the nonparametric and MMRPM methods give the user greater ability to properly interpret the true relationship between engine speed and torque for this type of engine and an increased likelihood of detecting unusual engines in future production. Finally, we conclude that the nonparametric and semiparametric approaches performed better than the parametric approach when the user's model is misspecified. The case study demonstrates that, the proposed nonparametric and semiparametric methods are shown to be more efficient, flexible and robust to model misspecification for Phase I profile monitoring in a practical application. Thus, our methods are robust to the common problem of model misspecification. We also found that both the nonparametric and the semiparametric methods result in charts with good abilities to detect changes in Phase I data, and in charts with easily calculated control limits. The proposed methods provide greater flexibility and efficiency than current parametric methods used in profile monitoring for Phase I that rely on correct model specification, an unrealistic situation in many practical problems in industrial applications.
Ph. D.
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Li, Zhengrong. "Model-based Tests for Standards Evaluation and Biological Assessments." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/29108.

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Implementation of the Clean Water Act requires agencies to monitor aquatic sites on a regular basis and evaluate the quality of these sites. Sites are evaluated individually even though there may be numerous sites within a watershed. In some cases, sampling frequency is inadequate and the evaluation of site quality may have low reliability. This dissertation evaluates testing procedures for determination of site quality based on modelbased procedures that allow for other sites to contribute information to the data from the test site. Test procedures are described for situations that involve multiple measurements from sites within a region and single measurements when stressor information is available or when covariates are used to account for individual site differences. Tests based on analysis of variance methods are described for fixed effects and random effects models. The proposed model-based tests compare limits (tolerance limits or prediction limits) for the data with the known standard. When the sample size for the test site is small, using model-based tests improves the detection of impaired sites. The effects of sample size, heterogeneity of variance, and similarity between sites are discussed. Reference-based standards and corresponding evaluation of site quality are also considered. Regression-based tests provide methods for incorporating information from other sites when there is information on stressors or covariates. Extension of some of the methods to multivariate biological observations and stressors is also discussed. Redundancy analysis is used as a graphical method for describing the relationship between biological metrics and stressors. A clustering method for finding stressor-response relationships is presented and illustrated using data from the Mid-Atlantic Highlands. Multivariate elliptical and univariate regions for assessment of site quality are discussed.
Ph. D.
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Luna, Soledad. "Population Dynamics And Factors Affecting Spiny Lobster Small Scale Fisheries." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-234815.

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This dissertation analyses the effects of current fisheries practices and management regulations of the green spiny lobster (Panulirus gracilis) in the Eastern Tropical Pacific Region (ETP). P. gracilis has reached a critical state in the ETP. Country-based studies report that between 60 and 98% of lobsters caught in the wild are under the minimum landing size (MLS). This means that spiny lobsters are being extracted before reproducing and contributing to the replenishment of interconnected populations. The recovery of green spiny lobster populations in the ETP and the future maintenance of a sustainable fishery will depend on effective management decisions and on taking in account environmental factors that influence the population dynamics of the lobsters. In the first study (Chapter 2), the B52 Spiny Lobster individual based simulation model was used for conducting a population viability analysis to quantify the effect of current fishing practices and the effect of varying management regulations on minimum landing size (MLS) and fishing effort. The best suit of regulations to maintain the highest abundance, production of offspring and catch is to protect juveniles and egged females, and to establish a MLS that assures the reproduction of individuals before being extracted. This study revealed regional variations, however the patterns and the causes for variation were not yet clear. This led to the next chapters in this dissertation. In Chapter 3, I used a meta-analysis to explore regional lobster variability by comparing published studies from the ETP. The objective was to identify patterns of variation related to geographic and environmental factors of the region that can inform the establishment and evaluation of coordinated regulations. Morphological relationships showed to be more variable at northern latitudes, where the mean annual sea surface temperatures are higher than at lower temperatures at the Equator. In terms of management, MLS regulations should be adapted accounting for the effect of sea surface temperature and its variation. Additionally, it was observed that monitoring methodologies are not standardized within the region and even in some cases, neither within countries. Furthermore, in most places monitoring of the spiny lobster fishery happens sporadically, only in Galapagos takes place every year. Identifying patterns of variations can improve the accuracy of prediction models which can help to explore, design, and apply more effective management measures, as well as promote regional coordination to support the recovery and maintenance of spiny lobsters. In Chapter 4, I contrast current Ecuadorian minimum landing size (MLS) regulation to lobster empirical measurements within Ecuador in order to recognize potential pitfalls for management enforcement. I used linear regression and multiple regression models with the objective of identifying potential relative size variations of the individuals caught in the wild over time and in the different fishing areas in Ecuador, as well as to analyse the effect of locality, sex, age and mean SST on the tail length/total length ratio. Morphological relations were significantly different among sexes, in time and by all sites. Most importantly, this study shows that current minimum size regulations are not applicable to all sites. Additionally, I found that water temperature has a significant effect on morphological relationship variations. However, it was not the main site-specific variable responsible for explaining such variations. In general, this work emphasizes the need for length data collection standardization and the consideration of temporal and spatial variation implications in national and regional fishery management planning, enforcement and evaluation.
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Cau, Nicklasson Ronnie, and Simon Hansson. "Investment Companies’ Discount Fluctuation on the Swedish Market : A statistical analysis regarding different micro- and macroeconomic factors influence on Swedish closed-end funds’ discount." Thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Economics, Finance and Statistics, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-21250.

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Closed-end funds’ (CEF) discount and discount fluctuations have been puzzling researchers for decades. Up to date, there are no multidimensional or cross-sectional variables that have been proved to influence CEFs simultaneously. Fact is that, earlier research and theories on the subject are contradictious and several suggestions on the origin of the CEF’s discount and its fluctuations have been proposed. To mention a few, investor sentiments, taxation issues, dividend policies, agency costs and agency problems are considered to influence these discounts. The purpose of this report is to examine the relationship between micro- and macroeconomic variables fluctuations, and how these can explain the discount fluctuation of the Swedish CEFs. This report focuses upon the CEFs traded at NASDAQ OMX Stockholm, which have been selected through a comprehensive multistage selection process. 10 CEFs were selected. Monthly data for calculating micro- and macroeconomic variables was collected for the period March 2003 – February 2013, which resulted in approximately 1 200 observations. OLS regression analysis, Fixed- and Random Effect Models and Hausman tests were conducted. The findings conclude that some of this report’s chosen micro- and macro variables influence on the Swedish CEFs’ discount fluctuation, although these findings are conditioned. The CEFs’ individual characteristics or traits result in a significant impact on the fluctuation of CEFs’ discount. Hence, only by controlling these characteristics, multidimensional or cross-sectional micro- and macroeconomic variables can be proved to affect the CEFs’ discount fluctuation.
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Kamangar, Daniel, and Richard Sundin. "Management and CEO Stock Ownership and its Effect on Company Performance." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229670.

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This is a study on the effect of management and CEO stock ownership on company performance. A regression analysis is performed on panel data consisting of a sample of 30 companies listed on OMX Stockholm Mid Cap. A total of 210 and 2520 observations is considered on a yearly and monthly basis, respectively, for seven years (2010-2016). The Hausman test is applied for determining between the fixed effects and random effects regression models. Results show that management relative stock ownership has a significant positive effect on company net income growth and return on assets. The effect is not significant for CEO stock ownership, which is contrary to what commonly has been shown for large companies in previous research. Moreover, alternative methodology is discussed for the benefit of the future researcher. The authors illustrate how the selection of dummy variables can be vital for final model outcomes, and it is thus an important aspect to consider when performing panel data analysis.
I den här studien undersöks hur aktieinnehav hos ledning och den verkställande direktören i ett företag påverkar företagsutvecklingen. Studien genomförs med regressionsanalys på paneldata som består av 30 företag, samtliga noterade på OMX Stockholm Mid Cap. Totalt samlas 210 och 2520 observationer på årsbasis respektive månadsbasis över sju år (2010-2016). Hausman-testet används för att bestämma vilken av fixed effects-modellen och random effects-modellen som ska användas i regressionen. Resultaten visar att relativt aktieinnehav hos ledningen har en positiv signifikant påverkan på ett företags nettoinkomstutveckling och avkastning på tillgångar. Den verkställande direktörens aktieinnehav visas inte vara signifikant, vilket är motsatt till det som generellt har visats för stora företag i tidigare forskning. Regressionerna genomförs även med alternativa metoder, vilka det resoneras kring i en diskussion som bör vara till gagn för vidare forskning. Författarna illustrerar hur val av dummy-variabler kan ha en avgörande betydelse för regressionsanalysen, och att det således är en viktig aspekt att ta hänsyn till när regressioner genomförs på paneldata.
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Fernando, Shasika. "Våldsbrott i Stockholms län och Uppsala län : En statistisk undersökning för att skatta effekten på våldsbrott med en multipel linjär regression genom studier av ungdomsbrottslighet i kombination med samhällsrelaterade faktorer." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-432998.

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This following report estimate the effect of violent crime with juvenile delinquency together with some societal factors with the help of a multiple regression of panel data. The results show that there are some variables that have a significant effect on violent crime. In my conclusion I discuss that the economic situation might contributes to violent crimes initially.
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Li, Guangjie. "Essays on economic and econometric applications of Bayesian estimation and model comparison." Thesis, University of Leicester, 2009. http://hdl.handle.net/2381/4792.

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This thesis consists of three chapters on economic and econometric applications of Bayesian parameter estimation and model comparison. The first two chapters study the incidental parameter problem mainly under a linear autoregressive (AR) panel data model with fixed effect. The first chapter investigates the problem from a model comparison perspective. The major finding in the first chapter is that consistency in parameter estimation and model selection are interrelated. The reparameterization of the fixed effect parameter proposed by Lancaster (2002) may not provide a valid solution to the incidental parameter problem if the wrong set of exogenous regressors are included. To estimate the model consistently and to measure its goodness of fit, the Bayes factor is found to be more preferable for model comparson than the Bayesian information criterion based on the biased maximum likelihood estimates. When the model uncertainty is substantial, Bayesian model averaging is recommended. The method is applied to study the relationship between financial development and economic growth. The second chapter proposes a correction function approach to solve the incidental parameter problem. It is discovered that the correction function exists for the linear AR panel model of order p when the model is stationary with strictly exogenous regressors. MCMC algorithms are developed for parameter estimation and to calculate the Bayes factor for model comparison. The last chapter studies how stock return's predictability and model uncertainty affect a rational buy-and-hold investor's decision to allocate her wealth for different lengths of investment horizons in the UK market. The FTSE All-Share Index is treated as the risky asset, and the UK Treasury bill as the riskless asset in forming the investor's portfolio. Bayesian methods are employed to identify the most powerful predictors by accounting for model uncertainty. It is found that though stock return predictability is weak, it can still affect the investor's optimal portfolio decisions over different investment horizons.
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Persson, Sanna, and Jerry Pettersson. "The connection between household savings ratio and human development index : Which factors affect the household savings ratio?" Thesis, Linnéuniversitetet, Institutionen för ekonomistyrning och logistik (ELO), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-87939.

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This thesis investigates which factors affecting savings behavior by using a fixed effect regression model. To see what affects the household savings rate the following independent variables is considered: Natural logarithm of trend per capita income, natural logarithm of deviation from trend per capita income, growth of disposable income, real interest rate, inflation, wealth in relation to household disposable income, foreign savings in relation to disposable income, dependency ratio and human development index. To see whether changes of human development within a county impacts the household´s savings ratio this variables was included in a separate regression. To avoid possible biasedness from ordinary least square, a panel data technique called fixed effect regression model is used. The investigated time period is between year 1999 and 2016 and to make a restriction, variables from 25 developed countries were studied. The involved economic theories in this work are Keynesianism, permanent income hypothesis and the savings theory behind Maslow´s behavioral pyramid. The result made by using this study is that growth in income and foreign savings in relation to disposable income is insignificant and can´t be used in explaining the differences between household´s savings. Human development index within a country has a negative effect on the savings ratio but a conclusion regarding whether changes in HDI´s does affect savings can´t be made and more research within that field is needed.
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Waterman, Megan Janet Tuttle. "Linear Mixed Model Robust Regression." Diss., Virginia Tech, 2002. http://hdl.handle.net/10919/27708.

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Mixed models are powerful tools for the analysis of clustered data and many extensions of the classical linear mixed model with normally distributed response have been established. As with all parametric models, correctness of the assumed model is critical for the validity of the ensuing inference. Model robust regression techniques predict mean response as a convex combination of a parametric and a nonparametric model fit to the data. It is a semiparametric method by which incompletely or incorrectly specified parametric models can be improved through adding an appropriate amount of a nonparametric fit. We apply this idea of model robustness in the framework of the linear mixed model. The mixed model robust regression (MMRR) predictions we propose are convex combinations of predictions obtained from a standard normal-theory linear mixed model, which serves as the parametric model component, and a locally weighted maximum likelihood fit which serves as the nonparametric component. An application of this technique with real data is provided.
Ph. D.
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Mingolini, Riccardo. "Investimenti in lobby: Un modello per stimare il loro impatto sull'azienda." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13291/.

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In questo elaborato di tesi verrà analizzata l’attitudine di una azienda ad investire in lobbying, misurato attraverso varie variabili importanti per la stessa quali ad esempio il capitale, il reddito netto dell’impresa, il numero degli impiegati (et simila) e il loro impatto scoraggiante o incentivante rispetto alla nostra variabile dipendente. Cercheremo infine di trovare un modello che approssima in modo sostanziale suddette dipendenze e variabili, in modo da tracciare un filo logico e matematico fra la nostra variabile dipendente Y ( investimento in lobbying) e le nostre variabili indipendenti X cioè gli indici e le variabili in valore monetario importanti per definire una azienda e il suo settore di appartenenza (SIC CODE).
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Books on the topic "Fixed effects regression model"

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Fixed effects regression models. Thousand Oaks, Calif: Sage Publications, 2009.

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Allison, Paul. Fixed Effects Regression Models. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications, Inc., 2009. http://dx.doi.org/10.4135/9781412993869.

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Linear and nonlinear models: Fixed effects, random effects, and mixed models. Berlin: Walter de Gruyter, 2006.

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Fixed effects regression methods for longitudinal data using SAS. Cary, N.C: SAS Institute, 2005.

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Heitmueller, Axel. A note on decompositions in fixed effects models in the presence of time-invariant characteristics. Bonn, Germany: IZA, 2005.

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Stock, James H. Heteroskedasticity-robust standard errors for fixed effects panel data regression. Cambridge, MA: National Bureau of Economic Research, 2006.

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LeBlanc, Michael R. Step-function covariate effects in the proportional hazards model. Toronto, Ont: University of Toronto, Department of Statistics, 1993.

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Yeom, Namsoo. Effects of asymmetric demand shocks in a fixed exchange rate two-country model. [s.l.]: typescript, 1992.

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Extending the linear model with R: Generalized linear, mixed effects and nonparametric regression models. Boca Raton: Taylor & Francis, 2016.

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Hahn, Jinyong. Asymptotically unbiased inference for a dynamic panel model with fixed effects when both n and T are large. Cambridge, MA: Massachusetts Institute of Technology, Dept. of Economics, 2000.

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Book chapters on the topic "Fixed effects regression model"

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Wilson, Jeffrey R., and Kent A. Lorenz. "Fixed Effects Logistic Regression Model." In ICSA Book Series in Statistics, 225–46. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23805-0_11.

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Vannucci, Giulia, Anna Gottard, Leonardo Grilli, and Carla Rampichini. "Random effects regression trees for the analysis of INVALSI data." In Proceedings e report, 29–34. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-304-8.07.

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Mixed or multilevel models exploit random effects to deal with hierarchical data, where statistical units are clustered in groups and cannot be assumed as independent. Sometimes, the assumption of linear dependence of a response on a set of explanatory variables is not plausible, and model specification becomes a challenging task. Regression trees can be helpful to capture non-linear effects of the predictors. This method was extended to clustered data by modelling the fixed effects with a decision tree while accounting for the random effects with a linear mixed model in a separate step (Hajjem & Larocque, 2011; Sela & Simonoff, 2012). Random effect regression trees are shown to be less sensitive to parametric assumptions and provide improved predictive power compared to linear models with random effects and regression trees without random effects. We propose a new random effect model, called Tree embedded linear mixed model, where the regression function is piecewise-linear, consisting in the sum of a tree component and a linear component. This model can deal with both non-linear and interaction effects and cluster mean dependencies. The proposal is the mixed effect version of the semi-linear regression trees (Vannucci, 2019; Vannucci & Gottard, 2019). Model fitting is obtained by an iterative two-stage estimation procedure, where both the fixed and the random effects are jointly estimated. The proposed model allows a decomposition of the effect of a given predictor within and between clusters. We will show via a simulation study and an application to INVALSI data that these extensions improve the predictive performance of the model in the presence of quasi-linear relationships, avoiding overfitting, and facilitating interpretability.
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Hilpert, Martin, and Damián E. Blasi. "Fixed-Effects Regression Modeling." In A Practical Handbook of Corpus Linguistics, 505–33. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46216-1_21.

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Maldonado, Luis, and Pablo Geraldo. "Fixed Effects Regression and Effect Heterogeneity." In Lebensbedingungen in Deutschland in der Längsschnittperspektive, 301–14. Wiesbaden: Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-19206-8_17.

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Tommasi, Chiara, Maria Teresa Santos-Martín, and Juan Manuel Rodríguez-Díaz. "Discrimination Between Random and Fixed Effect Logistic Regression Models." In Contributions to Statistics, 205–12. Heidelberg: Physica-Verlag HD, 2010. http://dx.doi.org/10.1007/978-3-7908-2410-0_27.

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Ravishanker, Nalini, Zhiyi Chi, and Dipak K. Dey. "Fixed-Effects Linear Models." In A First Course in Linear Model Theory, 305–40. 2nd ed. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781315156651-10.

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Blasco, Agustín. "The Linear Model: I. The ‘Fixed Effects’ Model." In Bayesian Data Analysis for Animal Scientists, 119–35. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54274-4_6.

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Baur, Dirk G., and Renée A. Fry. "A Fixed-Time Effects Model of Contagion." In Financial Contagion, 87–92. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118267646.ch10.

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Hanji, Mallikarjun B. "Methods for Pooling Estimates: Fixed Effects Model." In Meta-Analysis in Psychiatry Research, 85–95. Toronto ; New Jersey : Apple Academic Press, 2017.: Apple Academic Press, 2017. http://dx.doi.org/10.4324/9781315366234-10.

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Toutenburg, Helge. "Single-Factor Experiments with Fixed and Random Effects." In Experimental Design and Model Choice, 95–143. Heidelberg: Physica-Verlag HD, 1995. http://dx.doi.org/10.1007/978-3-642-52498-1_4.

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Conference papers on the topic "Fixed effects regression model"

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Wang, Tieping. "Regression analysis of tax preferences and innovation based on two-way fixed effects model —— Evidence from China." In 2020 2nd International Conference on Economic Management and Model Engineering (ICEMME). IEEE, 2020. http://dx.doi.org/10.1109/icemme51517.2020.00028.

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Slaveski, Trajko, and Darko Lazarov. "How Do Institutions Determine Economic Growth? Evidеnce from Central and Eastern Europe before and during Global Economic Crisis." In International Conference on Eurasian Economies. Eurasian Economists Association, 2014. http://dx.doi.org/10.36880/c05.01040.

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We investigate the influence of institutions on economic growth and the level of income per capita in CEE region, before and during the global economic crisis. We use principal factor component analysis in order to create a more reliable and representative variable that will measure the institutional quality in our regression models, and avoid the multi colinearity, a common statistical weakness for this type of regression models. The results from panel (random and fixed effects) regressions and GMM dynamic panel regression lead to two contrasting insights. The first regression model shows positive and statistically significant correlation between institutions and economic growth, which would imply that the CEE countries that have created a strong institutional capacity during transition and post-transition period have experienced higher economic growth. The second regression model, which refers to the global economic crisis period, shows a negative influence of institutions on economic growth for the same sample of countries. One explanation for this result might be the fact that countries with a higher degree of integration into the EU were also more vulnerable to the global economic crisis.
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Cali`, M., G. Orsello, M. Santarelli, and P. Leone. "Experimental Activity on the Tubular SOFC CHP100 kWe Field Unit in Italy: Factor Significance, Effects and Regression Model Analysis." In ASME 8th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2006. http://dx.doi.org/10.1115/esda2006-95802.

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The CHP 100 kWe tubular SOFC plant built by Siemens is operating at the Gas Turbine Technologies (GTT) in Turin. The generator started up on the June 2005 and produces electric and thermal power used in the factory. A first period of the activity was needed to lead the operation of the SOFC plant at the nominal condition and to reach the complete conditioning of the SOFC tubular fuel cells. Then, in order to characterize the operation of the generator and of the cogenerative system a first experimental campaign was designed by using the factorial analysis. With these methods, the effect of some independent variables (factors) on the plant operation is analyzed in form of screening tests: the analysis allows one to estimate the significance of the main and combined effects of each considered factor through an analysis of variance (ANOVA) on the experimental data. Moreover, the test plan has been designed by using a simple 2k factorial and a 2k factorial with spherical central composite design (CCD); these approaches allow to obtain respectively first-order and second-order regression models for some chosen dependent variables (i.e. DC and AC electric power, generator voltage, recovered heat etc...). The test plan has been performed at the fixed generator current of 500 Amps with two repetitions for each designed treatment. The factorial analysis has been applied considering two factors (22 factorial analysis): setup temperature of the generator and fuel utilization factor. First, the significance of the main and combined effects of the two considered factor has been evaluated. Then, the obtained data have been analyzed by using the Response Surface Methodology analysis (RSM). Finally, the regression models have been obtained for every dependent variable analyzed, with an outline of the sensitivity coefficient linking the dependent and the independent variables.
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Morgan, Neal R., Alexandrina Untaroiu, Patrick J. Migliorini, and Houston G. Wood. "Design of Experiments to Investigate Geometric Effects on Fluid Leakage Rate in a Balance Drum Seal." In ASME Turbo Expo 2014: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/gt2014-27021.

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Annular labyrinth seals are designed as tortuous paths that force a working fluid to expand and contract repeatedly through small clearances between high and low pressure stages of turbomachinery. The resulting expansion and recirculation reduces kinetic energy of the flow and minimizes leakage rate between regions of high and low pressure through the seal. Most current seal geometries are selected based on what has worked in the past, or by incremental improvements on existing designs. In the present research, a balance drum used in a multi-stage centrifugal pump was chosen as a starting point. A design of experiments study was performed to investigate the influence of groove scale on leakage rate across the seal for a fixed pressure differential. The CFD model of the selected labyrinth seal has an upstream region leading to 20 evenly spaced semicircular grooves along a 267 mm seal length, with a clearance region of 0.305 mm. The seal geometry was specified by a set of five variables. The variables allow for variation in scale of the semicircular grooves within a pattern of five independently scaled grooves repeated four times along the seal length. The seal was constructed with a parameterized CFD model in ANSYS CFX as a five degree sector of the full 3D seal. A non-central composite designed experiment was performed to investigate the effects of five parameters on leakage rate in the system. This study demonstrates a practical approach for investigating the effects of various geometric factors on leakage rate for balance drum seals. The empirical 10-parameter linear regression model fitted to the results of the experimental design yields suggested groove radii that could be applied to improve performance of future seals.
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Koşan, Naime İrem, and Sudi Apak. "Trade Openness and Macroeconomic Policy in OECD Countries." In International Conference on Eurasian Economies. Eurasian Economists Association, 2015. http://dx.doi.org/10.36880/c06.01373.

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Trade openness has been subject to an important issue many studies in literature. It allows us to analyze potential trade as a percentage of gross domestic product. Total value of international trade in goods and services shows the countries’ integration into the world economy. Generally, small countries are more integrated because of their dependency on imports. On the other hand, there many variables which effects trade integration. Our study focuses on to analyze the effects on trade openness and make inferences for OECD countries. In this paper we aim to examine the relationship between trade openness and macro-economic indicators in OECD countries. To analyze the relationship, we used panel data regression analysis. Data obtained from World Bank, The Heritage Foundation and United Nations Conference on Trade and Development (UNCTAD). The panel data covers 2000-2013 periods and 33 countries. The analysis made through the Stata econometric packet program. We predicted pooled, fixed effects and random effects panel data models and analyzed them. It has been found that gross domestic savings, investment freedom, and unemployment rate are statistically significant. The results found in this paper show that investment freedom and gross domestic savings have positive effect on trade openness as we expected. On the other hand, unemployment rate has positive effect on trade openness. These findings have important policy implications for OECD countries. Our interpretation of these findings is that, integration to world economy has generally positive effects for macroeconomic factors in OECD countries, but it should be limited.
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Koşan, Naime İrem, Sudi Apak, and Selahattin Sarı. "International Trade and Macro-Economic Policy in Eurasian Economies." In International Conference on Eurasian Economies. Eurasian Economists Association, 2016. http://dx.doi.org/10.36880/c07.01494.

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International trade is defined the exchange of goods, services, and capital among various countries and regions. Also the potential of imports and exports account for an important part of growth. On the other hand, total value of international trade in goods and services shows the countries’ integration into the world economy. In this paper we focused on to analyze the effects on imports and make inferences for Eurasian Countries. In this paper we aim to examine the relationship between imports and macro-economic indicators in 6 Eurasian economies. To analyze the relationship, we used panel data regression analysis. Data obtained from World Bank. The panel data covers 1996-2012 periods and 6 countries which named Kazakhstan, Russian Federation, Uzbekistan, Kyrgyz Republic, Azerbaijan and Turkmenistan. We predicted pooled, fixed effects and random effects panel data models using the Stata and analyzed them. The dependent variable is defined the imports in our model. It has been found that gross domestic savings, foreign direct investments and, and exports are statistically significant for this countries. The results found in this paper show that gross domestic savings has negative effects on imports. On the other hand, for this 6 countries foreign direct investments (inflow) and exports have positive effects on imports as we expected. It shows us the economic positions of Eurasian countries still depend on Russian Federation. Also, these findings have important policy implications for Eurasian Countries. Our interpretation of these findings is that, integration to world economy has generally positive effects on foreign direct investments for this countries.
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Mondal, S. C. "Process Capability — A Surrogate Measure of Process Robustness: A Case Study." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12418.

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A robust process is insensitive to the effect of noise variables. Noise variables are the main source for producing variation. Noise variables are included in the outer array in robust design experiment for enhancing robustness. The approach of robust design is to make the process robust (insensitive) to variation due to noise variables. The effect of noise factors can be modelled in a response surface model which helps to determine the settings of the design factors that neutralize the effects of the noise factors and improve robustness. In experimental design the noise factors are assumed fixed value whereas in real world manufacturing noise factors vary randomly. Again for a large scale manufacturing, it is extremely difficult to study robustness using experimentation as there are chances of stoppage of production. In such a situation a simulation-based model can be developed using industrial data to study robustness of a real manufacturing process. This paper proposed a method (a combination of simulation, regression modelling and robust design technique) to study robustness of a hardening and tempering process producing component worm shaft used in the steam power plant. The process capability indices (both univariate and multivariate) are determined based on the model responses. The variation of process performance (process capability values) due to random noise variation is studied using a general purpose process control chart (R-chart). The results show that noise factors in hardening and tempering process are insensitive to manufacturing variation and process capability indices act as a surrogate measure of process robustness.
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Ayad, A. Farid, H. M. Abdalla, and A. Abou El-Azm. "Study of the Effect of Impeller Side Clearance on the Centrifugal Pump Performance Using CFD." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-50756.

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Centrifugal pumps (CP) are probably among the most often used machinery in industrial facilities as well as in common practice. Compared to other types of rotating pumps, CP yield higher efficiency. In aerospace application reducing the weight of the CP impeller has the advantage of reducing mechanical stresses and enable using the CP at high number of revolution. In order to minimize the impeller weight the requirements to study and develop the CP with semi-open impeller appears. Using this type of impeller results in clearance between the impeller blades and the casing which degrade the centrifugal pump performance. The impact of this side clearance has not been deeply investigated in open literature. The present paper is devoted to reveal more details about the impact of CP side clearance on its performance. This is done by numerically investigating the influence of the variation of the CP side clearance width (0:0.2 impeller width) on the CP performance parameters at different flow rates (0:5 Liter/s). These CP performance parameters include the pump head, efficiency, slip factor, blades loads and the internal flow structure. 3-D steady numerical simulation has been carried out using commercial software, ANSYS® CFX. The computational domain consists of four zones: inlet, side gab, impeller and volute with outlet. They are defined by means of the multi-reference frame technique. The impeller is situated in the rotating reference frame, while the inlet, side gab and outlet zones are in the fixed reference frame, and they are related to each other through the “frozen rotor” interface. The meshes of four computational domains are generated separately after performing mesh sensitivity analysis. The boundary conditions are set as total pressure at inlet and the mass flow at outlet. A no-slip condition is imposed at the wall boundary defined at the blade and casing. A turbulent, incompressible flow solver has been adapted using SST k–ω turbulent model. The numerical simulation has been compared with own experimental results and a published empirical formulas to verify the numerical solution. The CFD results show an acceptable agreement with the results of the experimental work and the empirical formulas. It has been shown that the impeller side clearance have a great regression effect on the centrifugal pump performance. An explanation to the performance regression has been proposed based on the flow field feature. Performance regression could be attributed to the drop in the pressure difference between the impeller inlet and outlet. And the redistribution of the velocity inside the impeller channel and the side clearance.
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Simeonovski, Kiril, Elena Naumovska, and Mihail Petkovski. "THE EFFECT OF BANK DENSITY ON FINANCIAL DEVELOPMENT AND ECONOMIC PERFORMANCE." In Economic and Business Trends Shaping the Future. Ss Cyril and Methodius University, Faculty of Economics-Skopje, 2020. http://dx.doi.org/10.47063/ebtsf.2020.0014.

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This paper provides evidence about the link between bank density as a form of financial deepening, and financial development and economic performance. We construct a panel of European countries and develop a dynamic regression model with GDP dynamics up to three lags and a full set of fixed effects to study the effect that the number of bank branches and automated teller machines per capita have on real GDP per capita. Our baseline estimates point out to a weak negative impact of the increased number of bank branches per capita on economic performance by around 0.3 per cent annually. We find similar results from the subsequent IV and GMM estimates as well as when swapping the population basis of the bank density measures with the area. The IV strategy reveals that our both measures are endogenous with the respect to the level of urbanisation and the share of Internet users up to three lags. We further include financial development as a covariate and find weaker negative impact of the number of bank branches and a weak positive impact of the number of automated teller machines by about 0.15 per cent annually. Our estimates with respect to financial development reveal that both bank measures can be considered significant drivers given the positive impact of about 0.8 to 1.2 percentage points obtained for the number of bank branches and about 0.6 to 0.7 percentage points for the number of automated teller machines. We do not find any significant differences between the countries with harmonised regulations and shared currency as a result of the EU and Eurozone membership.
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Putri, Anggi Resina, Eti Poncorini Pamungkasari, and Hanung Prasetya. "Effect of Maternal Skills in Early Detection and Stimulation on Receptive Language Skill of Children Age 4 to 6 Years." In The 7th International Conference on Public Health 2020. Masters Program in Public Health, Universitas Sebelas Maret, 2020. http://dx.doi.org/10.26911/the7thicph.03.100.

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ABSTRACT Background: Language is the system someone uses to communicate with another person. Receptive language is important to the understanding of a child’s overall language skills. The purpose of this study was to examine the effect of maternal skills in early detection and stimulation on receptive language skill of children age 4 to 6 years. Subjects and Method: A cross sectional study was conducted at kindergarten in Surakarta, Central Java, from December 2019 to January 2020. A sample of 200 preschool children was selected by fixed disease sampling. The dependent variable was receptive language. The independent variables were early detection and early stimulation. Receptive language was measured by receptive one word picture vocabulary test (ROWPVT). The data were analyzed by a multiple linier regression. Results: Receptive language skill increased with early detection (b= 4.86; 95% CI= 2.68 to 6.33; p<0.001) and early stimulation (b= 3.70; 95% CI= 1.03 to 3.38; p<0.001). Conclusion: Receptive language skill increased with early detection and early stimulation. Keywords: receptive language, early detection, early stimulation, health belief model Correspondence: Anggi Resina Putri. Masters Program in Public Health, Universitas Sebelas Maret. Jl. Ir. Sutami 36A, Surakarta 57126, Central Java. Email: anggiresinaputri@gmail.com. Mobile: 085727387689. DOI: https://doi.org/10.26911/the7thicph.03.100
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Reports on the topic "Fixed effects regression model"

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Moon, Hyungsik Roger, and Martin Weidner. Dynamic linear panel regression models with interactive fixed effects. IFS, December 2013. http://dx.doi.org/10.1920/wp.cem.2013.6313.

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Moon, Hyungsik Roger, and Martin Weidner. Dynamic linear panel regression models with interactive fixed effects. IFS, December 2014. http://dx.doi.org/10.1920/wp.cem.2014.4714.

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Stock, James, and Mark Watson. Heteroskedasticity-Robust Standard Errors for Fixed Effects Panel Data Regression. Cambridge, MA: National Bureau of Economic Research, June 2006. http://dx.doi.org/10.3386/t0323.

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Moon, Hyungsik Roger, Nayoung Lee, and Martin Weidner. Analysis of interactive fixed effects dynamic linear panel regression with measurement error. Institute for Fiscal Studies, December 2011. http://dx.doi.org/10.1920/wp.cem.2011.3711.

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Moon, Hyungsik Roger, and Martin Weidner. Linear regression for panel with unknown number of factors as interactive fixed effects. Cemmap, October 2013. http://dx.doi.org/10.1920/wp.cem.2013.4913.

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Weidner, Martin, and Hyungsik Roger Moon. Linear regression for panel with unknown number of factors as interactive fixed effects. Cemmap, August 2014. http://dx.doi.org/10.1920/wp.cem.2014.3514.

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Graham, M. L. Linear Regression to a Lower Order Model: Effects and Implications. Fort Belvoir, VA: Defense Technical Information Center, November 1989. http://dx.doi.org/10.21236/ada218258.

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Vogt, Michael, Lena Boneva (Körber), and Oliver Linton. A semiparametric model for heterogeneous panel data with fixed effects. Institute for Fiscal Studies, January 2013. http://dx.doi.org/10.1920/wp.cem.2013.0213.

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Linton, Oliver, and Lena Boneva (Körber). A discrete choice model for large heterogeneous panels with interactive fixed effects with an application to the determinants of corporate bond issuance. The IFS, January 2017. http://dx.doi.org/10.1920/wp.cem.2017.0217.

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Beuermann, Diether, Nicolas L. Bottan, Bridget Hoffmann, C. Kirabo; Jackson, and Diego A. Vera-Cossio. Does Education Prevent Job Loss during Downturns?: Evidence from Exogenous Schools Assignments and COVID-19 in Barbados. Inter-American Development Bank, September 2021. http://dx.doi.org/10.18235/0003624.

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Canonical human capital theories posit that education, by enhancing worker skills, reduces the likelihood that a worker will be laid off during times of economic change. Yet, this has not been demonstrated causally. We link administrative education records from 1987 through 2002 to nationally representative surveys conducted before and after the onset of COVID-19 in Barbados to explore the causal impact of improved education on job loss during this period. Using a regression discontinuity (RD) design, Beuermann and Jackson (2020) show that females (but not males) who score just above the admission threshold for more selective schools in Barbados attain more years of education than those that scored just below (essentially holding initial ability fixed). Here, in follow-up data, we show that these same females (but not males) are much less likely to have lost a job after the onset of COVID-19. We show that these effects are not driven by sectoral changes, or changes in labor supply. Because employers observe incumbent worker productivity, these patterns are inconsistent with pure education signaling, and they suggest that education enhances worker skill.
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