Model seçimi için uygulanan testler sonucunda veriye sabit etkiler modelin uygun olduğu görülmüş, heteroskedasite, otokorelasyon ve birimler arası korelasyonun varlığı sınanmıştır. Birimler arası korelasyonun varlığından dolayı, serinin durağanlığı ikinci kuşak panel birim kök testleriyle incelenmiştir. Birimler arası korelasyonun varlığından dolayı, değişkenler arasında uzun dönemde bir denge ilişkisinin olup olmadığı ikinci kuşak panel eşbütünleşme testleriyle incelenmiştir. Homojenlik testi sonucunda bu testlerden heterojen olanlar kullanılmıştır. Model tahmin edilmiştir.
This document discusses nested designs in experiments. It defines nested designs as those where levels of one factor (B) are nested within or occur only with levels of another factor (A). An example is given of a forest genetics study measuring tree seedling height where seeds come from trees nested within different forests. The document outlines the linear model, effects, null hypotheses, partitioning variation, nested ANOVA table, testing hypotheses, and computations for a nested design.
The document discusses panel data and techniques for analyzing it, specifically fixed effects and random effects models. It defines panel data as data that observes the behavior of entities over time, providing examples like countries, companies, or individuals. Fixed effects models control for time-invariant characteristics of entities by including entity-specific intercepts or dummy variables for each entity. This allows analyzing the impact of variables that change over time by removing the influence of fixed characteristics. The document provides equations to demonstrate fixed effects models and discusses using the least squares dummy variable approach.
This document discusses panel data analysis. Some key points:
- Panel data combines cross-sectional and time series data to observe multiple subjects over time in balanced and unbalanced panels.
- Panel data is useful for reducing noise, studying dynamic changes, and addressing issues with limited data availability.
- Choosing between fixed effects and random effects models depends on tests like the Hausman test and whether the unobserved effects are correlated with regressors.
- Panel data regression techniques like pooled mean group allow for heterogeneity across subjects while assuming some parameters are the same.
This webinar looks at answering this question, not by going deeply into the various designed experiment types, but from a process improvement perspective. Progressing from a definition of a designed experiment, to Why and when do I need a designed experiment?, What’s the concept? (and why can’t I do a “one-factor-at-a-time” series of experiments? , to Will this tool solve REAL WORLD problems?
Logit and Probit and Tobit model: Basic IntroductionRabeesh Verma
This document provides an overview of regression analysis and different types of regression models used when the dependent variable is dichotomous (can only take two values, such as 0 and 1). It defines regression analysis and discusses linear regression assumptions. It then introduces logistic regression, probit regression, and tobit regression as alternatives to linear regression when the dependent variable is dichotomous. The key differences between these models and their applications are summarized.
This document discusses nested designs in experiments. It defines nested designs as those where levels of one factor (B) are nested within or occur only with levels of another factor (A). An example is given of a forest genetics study measuring tree seedling height where seeds come from trees nested within different forests. The document outlines the linear model, effects, null hypotheses, partitioning variation, nested ANOVA table, testing hypotheses, and computations for a nested design.
The document discusses panel data and techniques for analyzing it, specifically fixed effects and random effects models. It defines panel data as data that observes the behavior of entities over time, providing examples like countries, companies, or individuals. Fixed effects models control for time-invariant characteristics of entities by including entity-specific intercepts or dummy variables for each entity. This allows analyzing the impact of variables that change over time by removing the influence of fixed characteristics. The document provides equations to demonstrate fixed effects models and discusses using the least squares dummy variable approach.
This document discusses panel data analysis. Some key points:
- Panel data combines cross-sectional and time series data to observe multiple subjects over time in balanced and unbalanced panels.
- Panel data is useful for reducing noise, studying dynamic changes, and addressing issues with limited data availability.
- Choosing between fixed effects and random effects models depends on tests like the Hausman test and whether the unobserved effects are correlated with regressors.
- Panel data regression techniques like pooled mean group allow for heterogeneity across subjects while assuming some parameters are the same.
This webinar looks at answering this question, not by going deeply into the various designed experiment types, but from a process improvement perspective. Progressing from a definition of a designed experiment, to Why and when do I need a designed experiment?, What’s the concept? (and why can’t I do a “one-factor-at-a-time” series of experiments? , to Will this tool solve REAL WORLD problems?
Logit and Probit and Tobit model: Basic IntroductionRabeesh Verma
This document provides an overview of regression analysis and different types of regression models used when the dependent variable is dichotomous (can only take two values, such as 0 and 1). It defines regression analysis and discusses linear regression assumptions. It then introduces logistic regression, probit regression, and tobit regression as alternatives to linear regression when the dependent variable is dichotomous. The key differences between these models and their applications are summarized.
This document discusses economic models and their use and verification. It explains that economists use simplified theoretical models to understand economic behavior, despite their abstraction from reality. Models can be verified either by validating their assumptions or showing they can predict real-world events. A key model is supply and demand, which balances production costs and what buyers are willing to pay. However, general equilibrium models are needed to look at interactions between multiple markets. Testing models is challenging as it requires assessing assumptions and predictive power.
A Simple Tutorial on Conjoint and Cluster AnalysisIterative Path
A simple tutorial to show conjoint analysis and cluster analysis. please send your feedback, this version is still rough and I would like to iteratively improve it so it is useful for most.
Oration delivered by Dr Sujoy Dasgupta at Yuvacon, conference organized by the BOGS (Bengal Obstetric and Gynaecological Society) held on 22-23 April, 2023
Dans le cadre du parcours de l’Ecole Internationale de Recherche EIR-A, un séminaire organisé par Agreenium a réuni 83 doctorants de l’Inra, français ou étrangers, sur le site d’AgroCampusOuest.
Autour du thème « Les enjeux de l’élevage dans nos sociétés de demain », des conférences, table-rondes, visites et des travaux en groupes se sont succédés au cours de la semaine du 20 au 24 mars 2017.
The document discusses mixed models, which contain both fixed and random effects. Fixed effects have all possible levels included in the study, while random effects are a random sample from the total population. The mixed model is represented as Y = Xβ + Zγ + ε, where β are fixed effects, X are fixed effect variables, Z are random effects, γ are random effect parameters, and ε is the error term. Mixed models can model both fixed and random effects, account for correlation in errors, and handle missing data. They provide correct standard errors compared to general linear models (GLMs). Model fitting involves likelihood ratio tests and information criteria to select the best fitting model.
Regresyonda Çoklu Bağlantı (Multicollinearity) Probleminin Temel Bileşenler A...yigitcanozmeral
Çoklu Bağlantının tanımı, nedenleri, teşhis yöntemleri ve giderilme yöntemleri ayrıntılı olarak açıklanmıştır. Temel Bileşenler Analizi anlatılmış ve uygulama yapılmıştır.
The document summarizes the Rubin causal model and key assumptions and methods for causal inference using observational data, including linear regression models.
It introduces the Rubin model for causal effects, noting the need for a good counterfactual to estimate causal parameters. It then covers simple linear regression models (SLRM) and assumptions needed for causal interpretation, including the zero conditional mean assumption.
Finally, it discusses multivariate linear regression models (MLRM), outlining additional assumptions required like no multicollinearity between covariates and the independence of errors from covariates. It also introduces ordinary least squares estimation and the Frisch-Waugh theorem for interpreting slope estimates from MLRM.
Presentation on Multiple Decrement Life Table by aminAminul Islam
The document presents information on multiple decrement life tables (MDLT). It defines MDLT as modeling simultaneous operation of several causes of decrement where an individual fails due to one cause. Examples include double and triple decrement life tables. MDLT is compared to conventional and other types of life tables. Its uses include analyzing population socioeconomic characteristics, labor force growth/changes, and calculating average working hours. Assumptions of MDLT include each death from a single cause and independent probabilities of dying from different causes. The document concludes that MDLT can be used where other life tables have limitations due to modeling multiple decrements.
Ragui Assaad- University of Minnesota
Caroline Krafft- ST. Catherine University
ERF Training on Applied Micro-Econometrics and Public Policy Evaluation
Cairo, Egypt July 25-27, 2016
www.erf.org.eg
This document provides an introduction to experimental design and sampling methods used to produce data for statistical analysis. It discusses the differences between observational studies and experimental studies, as well as key concepts in experimental design including randomization, control groups, placebos, and blocking/stratification. Specific experimental designs covered include completely randomized designs, blocked/stratified designs, and matched pairs designs. Examples are provided to illustrate how different experimental designs can be applied.
This document discusses simple and multiple regression analysis. Simple regression considers the relationship between one explanatory variable and one response variable, while multiple regression considers the relationship between one dependent variable and multiple independent variables. The document provides the formulas for simple and multiple linear regression. It also presents an example using SPSS to analyze the relationship between firm size, age, and performance. The SPSS output includes measures of model fit like R, R-squared, adjusted R-squared, ANOVA, regression coefficients, and diagnostics for assumptions. Hypothesis testing is conducted on the regression coefficients.
Improving accuracy by using information from relatives—The animal modelILRI
Presented by Raphael Mrode, ILRI, at the Australian Africa Universities (AAUN) Workshop on New Plant Breeding Methods for Sustainable Use of Genetic Resources and Security of Food Production, Mauritius University, Mauritius, 29-31 January 2018
ONS Local has been established by the Office for National Statistics (ONS) to support evidence-based decision-making at the local level. We aim to host insightful events that connect our users with exciting developments happening in subnational statistics and analysis at the ONS and across other organisations.
In recent months, the ONS Data Science Campus has published data and insights on feasible travel at a hyperlocal level and rail schedule disruptions at every station in Great Britain. We are currently scoping the need for further work in this area, which may include topics such as reliability, cost, international comparability, and the type and quality of services accessible. In this webinar, we present existing work and gathered user needs from local institutions for data and statistics in this area.
If you have any questions, please contact ons.local@ons.gov.uk.
Covariance and correlation(Dereje JIMA)Dereje Jima
The document discusses covariance and correlation, which are mathematical models used to assess relationships between variables. Covariance measures how two variables change together, while correlation measures both the strength and direction of the linear relationship between variables. Correlation coefficients range from -1 to 1, where values closer to 1 or -1 indicate a strong linear relationship and values closer to 0 indicate no linear relationship. The document also discusses partial correlation and multiple correlation, which measure relationships while controlling for additional variables. Factors that can affect correlation analyses include sample size and outliers.
Ordinary least squares linear regressionElkana Rorio
Ordinary Least Squares Linear Regression is commonly used but often misunderstood and misapplied. It works by minimizing the sum of squared errors between predictions and actual values in the training data to determine coefficients for the linear regression equation. However, it is very sensitive to outliers in the data which can dramatically affect the determined coefficients and reduce prediction accuracy. Alternative regression techniques like least absolute deviations are more robust to outliers but less computationally efficient. Preprocessing data to remove or de-emphasize outliers can help address these issues with Ordinary Least Squares regression.
This document discusses economic models and their use and verification. It explains that economists use simplified theoretical models to understand economic behavior, despite their abstraction from reality. Models can be verified either by validating their assumptions or showing they can predict real-world events. A key model is supply and demand, which balances production costs and what buyers are willing to pay. However, general equilibrium models are needed to look at interactions between multiple markets. Testing models is challenging as it requires assessing assumptions and predictive power.
A Simple Tutorial on Conjoint and Cluster AnalysisIterative Path
A simple tutorial to show conjoint analysis and cluster analysis. please send your feedback, this version is still rough and I would like to iteratively improve it so it is useful for most.
Oration delivered by Dr Sujoy Dasgupta at Yuvacon, conference organized by the BOGS (Bengal Obstetric and Gynaecological Society) held on 22-23 April, 2023
Dans le cadre du parcours de l’Ecole Internationale de Recherche EIR-A, un séminaire organisé par Agreenium a réuni 83 doctorants de l’Inra, français ou étrangers, sur le site d’AgroCampusOuest.
Autour du thème « Les enjeux de l’élevage dans nos sociétés de demain », des conférences, table-rondes, visites et des travaux en groupes se sont succédés au cours de la semaine du 20 au 24 mars 2017.
The document discusses mixed models, which contain both fixed and random effects. Fixed effects have all possible levels included in the study, while random effects are a random sample from the total population. The mixed model is represented as Y = Xβ + Zγ + ε, where β are fixed effects, X are fixed effect variables, Z are random effects, γ are random effect parameters, and ε is the error term. Mixed models can model both fixed and random effects, account for correlation in errors, and handle missing data. They provide correct standard errors compared to general linear models (GLMs). Model fitting involves likelihood ratio tests and information criteria to select the best fitting model.
Regresyonda Çoklu Bağlantı (Multicollinearity) Probleminin Temel Bileşenler A...yigitcanozmeral
Çoklu Bağlantının tanımı, nedenleri, teşhis yöntemleri ve giderilme yöntemleri ayrıntılı olarak açıklanmıştır. Temel Bileşenler Analizi anlatılmış ve uygulama yapılmıştır.
The document summarizes the Rubin causal model and key assumptions and methods for causal inference using observational data, including linear regression models.
It introduces the Rubin model for causal effects, noting the need for a good counterfactual to estimate causal parameters. It then covers simple linear regression models (SLRM) and assumptions needed for causal interpretation, including the zero conditional mean assumption.
Finally, it discusses multivariate linear regression models (MLRM), outlining additional assumptions required like no multicollinearity between covariates and the independence of errors from covariates. It also introduces ordinary least squares estimation and the Frisch-Waugh theorem for interpreting slope estimates from MLRM.
Presentation on Multiple Decrement Life Table by aminAminul Islam
The document presents information on multiple decrement life tables (MDLT). It defines MDLT as modeling simultaneous operation of several causes of decrement where an individual fails due to one cause. Examples include double and triple decrement life tables. MDLT is compared to conventional and other types of life tables. Its uses include analyzing population socioeconomic characteristics, labor force growth/changes, and calculating average working hours. Assumptions of MDLT include each death from a single cause and independent probabilities of dying from different causes. The document concludes that MDLT can be used where other life tables have limitations due to modeling multiple decrements.
Ragui Assaad- University of Minnesota
Caroline Krafft- ST. Catherine University
ERF Training on Applied Micro-Econometrics and Public Policy Evaluation
Cairo, Egypt July 25-27, 2016
www.erf.org.eg
This document provides an introduction to experimental design and sampling methods used to produce data for statistical analysis. It discusses the differences between observational studies and experimental studies, as well as key concepts in experimental design including randomization, control groups, placebos, and blocking/stratification. Specific experimental designs covered include completely randomized designs, blocked/stratified designs, and matched pairs designs. Examples are provided to illustrate how different experimental designs can be applied.
This document discusses simple and multiple regression analysis. Simple regression considers the relationship between one explanatory variable and one response variable, while multiple regression considers the relationship between one dependent variable and multiple independent variables. The document provides the formulas for simple and multiple linear regression. It also presents an example using SPSS to analyze the relationship between firm size, age, and performance. The SPSS output includes measures of model fit like R, R-squared, adjusted R-squared, ANOVA, regression coefficients, and diagnostics for assumptions. Hypothesis testing is conducted on the regression coefficients.
Improving accuracy by using information from relatives—The animal modelILRI
Presented by Raphael Mrode, ILRI, at the Australian Africa Universities (AAUN) Workshop on New Plant Breeding Methods for Sustainable Use of Genetic Resources and Security of Food Production, Mauritius University, Mauritius, 29-31 January 2018
ONS Local has been established by the Office for National Statistics (ONS) to support evidence-based decision-making at the local level. We aim to host insightful events that connect our users with exciting developments happening in subnational statistics and analysis at the ONS and across other organisations.
In recent months, the ONS Data Science Campus has published data and insights on feasible travel at a hyperlocal level and rail schedule disruptions at every station in Great Britain. We are currently scoping the need for further work in this area, which may include topics such as reliability, cost, international comparability, and the type and quality of services accessible. In this webinar, we present existing work and gathered user needs from local institutions for data and statistics in this area.
If you have any questions, please contact ons.local@ons.gov.uk.
Covariance and correlation(Dereje JIMA)Dereje Jima
The document discusses covariance and correlation, which are mathematical models used to assess relationships between variables. Covariance measures how two variables change together, while correlation measures both the strength and direction of the linear relationship between variables. Correlation coefficients range from -1 to 1, where values closer to 1 or -1 indicate a strong linear relationship and values closer to 0 indicate no linear relationship. The document also discusses partial correlation and multiple correlation, which measure relationships while controlling for additional variables. Factors that can affect correlation analyses include sample size and outliers.
Ordinary least squares linear regressionElkana Rorio
Ordinary Least Squares Linear Regression is commonly used but often misunderstood and misapplied. It works by minimizing the sum of squared errors between predictions and actual values in the training data to determine coefficients for the linear regression equation. However, it is very sensitive to outliers in the data which can dramatically affect the determined coefficients and reduce prediction accuracy. Alternative regression techniques like least absolute deviations are more robust to outliers but less computationally efficient. Preprocessing data to remove or de-emphasize outliers can help address these issues with Ordinary Least Squares regression.
2. Değişkenler
• Expense (current LCU)
Expense is cash payments for operating activities of the government
in providing goods and services. It includes compensation of employees
(such as wages and salaries), interest and subsidies, grants, social benefits,
and other expenses such as rent and dividends.
• GDP (current LCU)
GDP at purchaser's prices is the sum of gross value added by all
resident producers in the economy plus any product taxes and minus any
subsidies not included in the value of the products. It is calculated without
making deductions for depreciation of fabricated assets or for depletion and
degradation of natural resources. Data are in current local currency.
3. Tahminciler Arasında Karar Vermek İçin
Kullanılan Testler
Tüm gözlemlerin homojen olduğu (birim ve/veya zaman
etkilerinin olmadığı) düşünülüyorsa, klasik modelin; aksi durumda sabit
veya tesadüfi etkiler modellerinin kullanılması daha mantıklı olacaktır.
Klasik model, birinci farklar modeli, sabit etkiler modeli ve
tesadüfi etkiler modelinden hangisinin kullanılacağı kararı bir takım
testler sonucunda yapılıp karar verilmesi daha güvenilir olacaktır.
4. Klasik Modelin Testi
Klasik modelin geçerliliğinin sınanması aynı zamanda birim ve/veya
zaman etkilerin olup olmadığını gösterir.
• F Testi
Tüm birim etkilerin sıfıra eşit olduğu 𝐻0 hipotezi sınanmaktadır.(1. Çıktı)
Tüm zaman etkilerin sıfıra eşit olduğu 𝐻0 hipotezi sınanmaktadır.(2. Çıktı)
5. Sonuçlara göre, birim etkilerin sıfıra eşit olduğu 𝐻0 hipotezi
reddedilmektedir. Birim etkilerin var olduğu gözükmektedir. Dolayısıyla
klasik model uygun değildir.
6. Sonuçlara göre, zaman etkilerin sıfıra eşit olduğu 𝐻0 hipotezi
reddedilememektedir. Zaman etkilerinin bu model için önemi
bulunmamaktadır.
7. Sabit Etkiler Tahmincisi İle Tesadüfi Etkiler
Tahmincisi Arasında Karar Vermek
Uygulanan testler sonucunda birim ve/veya zaman etkilerinin olduğu
ortaya çıkmışsa, bu etkilerin sabit mi yoksa tesadüfi mi olduğuna karar
vermek için;
• Hausman Testi
Tesadüfi etkiler modeli uygundur şeklindeki 𝐻0 hipotezi sınanmaktadır.
• . xtreg expense gdp, fe
• . estimates store fe
• . xtreg expense gdp, re
• . estimates store re
• . hausman fe re
8. Test sonucunda 𝐻0 hipotezi reddedilir. Tesadüfi etkiler tahmincisinin
tutarsız olduğuna ve sabit etkiler tahmincisinin geçerli olduğuna karar
verilir.
9. Sabit Etkiler Modelinde Heteroskedasite
• Değiştirilmiş Wald Testi
. xtreg expense gdp, fe
Varyansın birimlere göre değişmediği şeklinde kurulan 𝐻0 hipotezi
reddedilir. Birimlere göre heteroskedasite olduğu sonucuna varılır.
10. Sabit Etkiler Modelinde Otokorelasyon
• Bhargava, Franzini ve Narendranathan’ın Durbin-Watson Testi
Otokorelasyon katsayısının
sıfıra eşit olduğu 𝐻0 hipotezi
test edilmektedir.
1.179 ve 1.302 değerleri
2’den küçük olduğu için
sabit etkiler modeli için
otokorelasyonun ciddi
olduğu kanısına varılır.
11. Sabit Etkiler Modelinde Birimler Arası Korelasyon
• Breusch-Pagan Lagrange Çarpanı Testi
. xtreg expense gdp, fe
Sonuçlara göre birimler arası korelasyonsuzluğu gösteren 𝐻0 hipotezi
reddedilir. Birimler arası korelasyon olduğu sonucuna varılır.
12. İkinci Kuşak Panel Birim Kök Testleri
Birimler arası korelasyonun varlığı nedeniyle ikinci kuşak panel birim
kök testleri kullanılır.
Panel birim kök testleri serinin durağan olup olmadığını inceler.
13. • Çok Değişkenli Genişletilmiş Dickey Fuller (MADF) Panel Birim Kök Testi
N<T olması koşulu bulunan bu test, kullanılan örnek için uygundur.
N=9, T=21
Gecikme uzunluğu 1 olarak seçilmiştir. Testin temel hipotezi, panelin 9
zaman serisinin tümünün (1) olduğu şeklinde kurulmuştur. Sonuçlara göre,
MADF test istatistiği verilen kritik değerden büyük olduğu için 𝐻0 hipotezi
reddedilir. Panel veri serisi durağandır sonucuna varılır.
14. • Levin, Lin ve Chu (LLC) Panel Birim Kök Testi
Birimler birim kök içermektedir olarak olarak kurulan 𝐻0 hipotezi
reddedilir. Serinin durağan olduğu kanısına varılır. AIC kriterine göre
seçilen gecikmelerin ortalaması 0’dır.
15. • Harris ve Tzavalis (HT) Panel Birim Kök Testi
Birimler arası korelasyonun etkisini azaltabilmek amacıyla yatay kesit
ortalamalardan fark alınmış serilere uygulanmaktadır.
Birimler birim kök içermektedir olarak olarak kurulan 𝐻0 hipotezi
reddedilir. Serinin durağan olduğu kanısına varılır.
16. • Hadri Panel Birim Kök Testi
Birimler arası korelasyonun etkisini azaltabilmek amacıyla yatay kesit
ortalamalardan fark alınmış serilere uygulanmaktadır.
Panel veri setinde yer alan birimler durağandır şeklinde kurulan 𝐻0
hipotezi reddedilememektedir. Serinin durağan olduğu kanısına varılır.
17. İkinci Kuşak Panel Eşbütünleşme Testleri
Birimler arası korelasyonun varlığı nedeniyle ikinci kuşak panel birim
kök testleri kullanılır.
Değişkenler arasında uzun dönemde bir denge ilişkisi olabilir, bu
ilişkinin varlığı panel eşbütünleşme testleri ile incelenir.
Panel eşbütünleşme testleri değişkenler arasında uzun dönemde bir
ilişki olduğunu gösterirse, bu uzun dönemli ilişkiler tahmin edilebilir.
Uzun dönem parametresinin tüm birimler için homojen veya heterojen
olmasına göre iki grupta tahmin edilmektedir.
18. Homojenlik Testi
• Swamy S Testi
Sonuçlara göre 𝐻0 hipotezi reddedilir. Parametrelerin homojen olmadığı
birimden birime değiştiği kabul edilir. Bu durumda eşbütünleşme
testlerinden heterojen olanların tercih edilmesi daha güvenilir olacaktır.
20. Gecikme uzunluğu heterojen seçilmiş, birimlere göre değişmektedir.
y(t-1)’in anlamlılığı incelendiğinde (P-val>0.1 olduğundan) 𝐻0 hipotezi
reddedilememektedir. Yani expense ve gdp değişkenleri arasında
eşbütünleşme (uzun dönemli ilişki) olmadığı anlaşılmaktadır.
21. Panel Eşbütünleşme Modelinin Tahmini
• Ortalama Grup Dinamik En Küçük Kareler (DOLSMG) Tahmincisi
Çıktıda, expense ile gdp değişkenleri arasında uzun dönemli
ilişkinin tahmini yer almaktadır. Tahmin edilen parametre
(-0.21) olup uzun dönem parametresidir. t istatistiği anlamlıdır.
Bu sonuçlara göre uzun dönemde gdp, expense değişkenini
etkilemektedir. Gdp’deki %1’lik artış, expense üzerinde %0.21’lik
azalmaya sebep olur.
22. KAYNAKÇA
• Prof. Dr. Ferda Yerdelen Tatoğlu; Panel Zaman Serileri Analizi
• Prof. Dr. Ferda Yerdelen Tatoğlu; Panel Veri Ekonometrisi