Natalia-ekonometria - Essay on the Example of Data 6-5...

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Essay on the Example of Data 6-5 Softwood Harvest in Oregon From Gretl - Ramanathan Natalia Antczak I MSG IB SS2 248909 LIST OF CONTENT
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1 1. Introduction 2. Formulation of the general model 3. Data 6-5 Softwood Harvest in Oregon Model Estimation 4. Estimated model hypotheses testing 5. Non-linear model testing 5. Appendices 6. Conclusions LIST OF TABLES
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2 1. Picture 1. Dataset info 2. Picture 2. Ordinary Least Squares estimation 3. Picture 3. Runs test 4. Picture 4. Normality of Residuals test 5. Picture 5. Normality test 6. Picture 6. Breusch-Pagan test for heteroskedasticity 7. Picture 7. Breusch-Godfrey test for autocorrelation 8. Picture 8. Auxiliary regression for RESET specification test 9. Picture 9. Ordinary Least Squares estimation for Model 3. 10. Picture 10. Normality of residuals test for Model 3. 11. Picture 11. Breusch-Pagan test for heteroskedasticity 12. Picture 12. Breusch-Godfrey test for autocorrelation 1. Introduction
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3 The main aim of this empirical project is to formulate the problem to be studied and interpret the results 1 . The dataset to be studied comes from Ramanathan - GRETL. The paper is divided into three main parts. These are: introduction, formulation of the general model, Data 6-5 Softwood Harvest in Oregon Model Formulation, hypotheses testing, conclusions and appendices. As far as the methodology is concerned, the Ordinary Least Squares (OLS) estimation is the main source used to conduct the research on the data. Test for normality of residuals, autocorrelation, specification and heteroskedasticity are the next to be interpreted. The essay will evaluate both static and dynamic models in order to choose the one closer to the data. The main sources of knowledge used for conducting the research are: the Ramu Ramanathan’s book Introductory Econometrics with Applications and Tadeusz Kufel’s work Ekonometria. Rozwiązywanie problemów z wykorzystaniem programu GRETL. 2. Formulation of the General Model S imple linear regression is a linear regression model with a single explanatory variable (X) related to the dependent variable (Y). Multiple regression model is an extension of simple linear regression in which more than one independent variable (X 1, X 2,... ) is used to predict a single dependent variable (Y). Since the dataset includes more than one explanatory variables related to the dependent variable, multiple regression model is the one to be used for the dataset. The multiple linear regression model is represented by the following equation: Y t = β 0 + β 1 X t1 + ... + β k X k + ɛ t Ɛ t stands for the unobserved random variable- random error used to represent the d iscrepancy or uncontrolled variation between an observed value and the value predicted by a model.
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