1 Estimation of GDP in Turkey by nonparametric regression models DURSUN AYDIN ANADOLU UNIVERSITY TURKEY.

Slides:



Advertisements
Similar presentations
1 Non-Linear and Smooth Regression Non-linear parametric models: There is a known functional form y=  x,  derived from known theory or from previous.
Advertisements

Factor Model Based Risk Measurement and Management R/Finance 2011: Applied Finance with R April 30, 2011 Eric Zivot Robert Richards Chaired Professor of.
Small Area Prediction under Alternative Model Specifications By Wayne A. Fuller and Andreea L. Erciulescu Department of Statistics, Iowa State University.
HSRP 734: Advanced Statistical Methods July 24, 2008.
Numbers
The Multiple Regression Model Hill et al Chapter 7.
Prediction Methods Mark J. van der Laan Division of Biostatistics U.C. Berkeley
Nonparametric Regression -m(x) is a general function that relates x to y. This specification includes the linear regression model as a special case. -Now.
A Novel 2-D Model Approach for the Prediction of Hourly Solar Radiation F. Onur Hocaoğlu, Ö. Nezih Gerek, Mehmet Kurban Anadolu University, Dep. of Electrical.
Review of the fundamental concepts of probability Exploratory data analysis: quantitative and graphical data description Estimation techniques, hypothesis.
7. Nonparametric inference  Quantile function Q  Inference on F  Confidence bands for F  Goodness- of- fit tests 1.
Fitting a Function to Data Adapted from Walch Education.
Simple Linear Regression NFL Point Spreads – 2007.
THE RESULTS OF MULTIPLE INTELLIGENCE TEST HADIM ÇOK PROGRAMLI ANADOLU LISESI KONYA, TURKEY.
Christopher (Kitt) Carpenter and Carlos Dobkin The Effects of Alcohol Access on Consumption and Mortality We thank NIH/NIAAA for financial support R01-AA
CHAPTER 3 Quantitative Demand Analysis Copyright © 2014 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written.
Topics in Microeconometrics William Greene Department of Economics Stern School of Business.
Advanced Higher Statistics Data Analysis and Modelling Hypothesis Testing Statistical Inference AH.
When and why to use Logistic Regression?  The response variable has to be binary or ordinal.  Predictors can be continuous, discrete, or combinations.
LBSRE1021 Data Interpretation Lecture 11 Correlation and Regression.
Simple Linear Regression. The term linear regression implies that  Y|x is linearly related to x by the population regression equation  Y|x =  +  x.
An exploration of the relationship between productivity and diversity in British Grasslands Adam Butler & Janet Heffernan, Lancaster University Department.
Survival Analysis 1 Always be contented, be grateful, be understanding and be compassionate.
Computational statistics, lecture3 Resampling and the bootstrap  Generating random processes  The bootstrap  Some examples of bootstrap techniques.
Linear Prediction Correlation can be used to make predictions – Values on X can be used to predict values on Y – Stronger relationships between X and Y.
Univ logo Piecewise Gaussian Process for System Identification Juan Yan Prof Kang Li and Prof Erwei Bai Queen’s University Belfast UKACC PhD Presentation.
The Assessment of Improved Water Sources Across the Globe By Philisile Dube.
Treat everyone with sincerity,
ON NONPARAMETRIC INTERVAL ESTIMATION OF A REGRESSION FUNCTION BASED ON THE RESAMPLING ALEXANDER ANDRONOV Riga Technical University Riga, Latvia.
New Information Technologies in Learning Statistics M. Mihova, Ž. Popeska Institute of Informatics Faculty of Natural Sciences and Mathematics, Macedonia.
Incorporating Uncertainties into Economic Forecasts: an Application to Forecasting Economic Activity in Croatia Dario Rukelj Ministry of Finance of the.
Variance Stabilizing Transformations. Variance is Related to Mean Usual Assumption in ANOVA and Regression is that the variance of each observation is.
SOCW 671 #11 Correlation and Regression. Uses of Correlation To study the strength of a relationship To study the direction of a relationship Scattergrams.
Notes Over 3.1 Solving a System Graphically Graph the linear system and estimate the solution. Then check the solution algebraically.
EXAMPLE 1 Solve a system graphically Graph the linear system and estimate the solution. Then check the solution algebraically. 4x + y = 8 2x – 3y = 18.
Chapter 14 Introduction to Regression Analysis. Objectives Regression Analysis Uses of Regression Analysis Method of Least Squares Difference between.
10-1 人生与责任 淮安工业园区实验学校 连芳芳 “ 自我介绍 ” “ 自我介绍 ” 儿童时期的我.
浙江省. 浙江省位于中国 东南沿海东濒东海 ,南界福建,西与 江西、安徽相连, 北与上海,江苏为 邻。境内最大的河 流钱塘江,因江流 曲折,又称浙江, 省以江名,简称为 浙。
“ 东方明珠 ”── 香港和澳门 东北师大附中 王 瑶. 这是哪个城市的夜景? 香港 这是哪个城市的夜景? 澳门.
ENM 310 Design of Experiments and Regression Analysis
Part Three. Data Analysis
Yahoo Mail Customer Support Number
Most Effective Techniques to Park your Manual Transmission Car
How do Power Car Windows Ensure Occupants Safety
مراقبت خانواده محور در NICU
استخراج فلزات 1 آماده‌سازی بار. استخراج فلزات 1 آماده‌سازی بار.
Linear Regression.
Linear Regression.
Outline. A 2010 Mapping of the Constructed Surface Area Density for China Preliminary Results.
دانشگاه شهیدرجایی تهران
Polynomial Regression
تعهدات مشتری در کنوانسیون بیع بین المللی
Stochastic Frontier Models
Lecture 7 Nonparametric Regression: Nadaraya Watson Estimator
THANK YOU!.
Lecture 1 INTRODUCTION.
بسمه تعالی کارگاه ارزشیابی پیشرفت تحصیلی
دومین کمیته مترجمین حاکمیت بالینی دانشگاه
Extended Response Graphic Organizer
Thank you.
Thank you.
Chapter 12 Linear Regression and Correlation
Section 2: Linear Regression.
NDM Data Sample Analysis: Final Results (2)
Scatter Plots and Least-Squares Lines
Shodmonov M.. The main goal of the work Analysis.
Cases. Simple Regression Linear Multiple Regression.
Centre for Technology Alternatives for Rural Areas, IIT Bombay
Microeconometric Modeling
Presentation transcript:

1 Estimation of GDP in Turkey by nonparametric regression models DURSUN AYDIN ANADOLU UNIVERSITY TURKEY

2 Outline Summary General Model  Semi-parametric model  Model fitting for semi-parametric estimation  Additive model  Model fitting for nonparametric estimation Application  Results obtained by semi-parametric regression  Results obtained by additive regression  Results obtained by parametric linear regression  Graphics of regression models Conclusions

3 Summary

4 General Model

5 Semi-parametric model

6 Model fitting

7 Additive regression model

8 Model fitting

9 Application

10 Results obtained by semi-parametric regression

11 Results obtained by additive regression

12 Results obtained by parametric linear regression

13 Graphics of the regression model

14 Graphics of the regression model

15 Conclusions

16

17 Thanks for your attention