CHAPTER 1

1. CHAPTER 1 BY A. SHINTA

2. EMPIRICAL ECONOMETRIC MODELLING FRISCH 1933 in Hendry 1995 said that ECONOMETRICS involves the “MUTUAL PENETRATION OF QUANTITATIVE ECONOMIC THEORY AND STATISTICAL OBSERVATION” so… it will examine the CONCEPTS, MODELS, PROCEDURES and TOOLS OF ECONOMETRICS and INVESTIGATE THE SYSTEMATIC APPLICATION OF ECONOMETRIC METHODS TO THE ANALYSIS OF ECONOMIC TIME SERIES DATA

3. All models are not born equal, and we seek those which are useful in practise : - for understanding economic behavior - for testing economic theories - for forecasting the future - for analysing economic policy Achieving these four objectives requires discovering sustainable relationships between observed economic magnitudes. To find such relationships entails rejecting models which lack desirable characteristics, so we must be able to critically evaluate empirical models and test their associated theories against the available evidence This group of activities constitutes econometric modelling

4. Empirical econometric models are systems of quantitative relationships linking observed data series There are 4 main roles in economics • Data summarize : there exist too many variables of potential interest in economics for us to investigate them all, so summarization is essensial, and econometric models are one way of doing so • Econometric models allow us to interpret empirical evidence : facsts rarely speak themselves

5. 3. There are often several competing theoritical explanations for economic phenomena; econometric models play an important role in evaluating the relative explanatory powers of these theories 4. Econometric models are the primary vehicle for the accumulation and consolidation of empirical knowledge about how economies function .

6. THE PROBLEMS OF ECONOMETRICS • These are many and varied • The economy is a complicated, dynamic, non linier, high – dimensional and evolving entity • It was caused : - society, social change - laws change - tehcnological innovations - time series data samples are : * short * higlyagregated * heterogenous * non stationary * time dependent and interdependent

7. AIMS OF THE COURSE : ABILITY TO GOOD ECONOMETRICS : • REGRESSION METHODS - ESTIMATION AND TESTING - DIAGNOSTIC TESTING - MODEL BUILDING 2. TIME SERIES METHODS - STATIONARY SERIES - NON-STATIONARY SERIES 3. LIMITED DEPENDENT VARIABLES 4. PANEL DATA

8. Regression methods ESTIMATION the Magnitude of quantitative relationships • Evaluating and comparing alternative economic models • Example : a. market research : finding the parameters of demand or supply curves to compute own price, cross price, income and supply elasticities b. How does labor supply, employment and investment respond to changes in taxes and other policy variables c. How will a carbon tax affect pollution levels?

9. How will changes in penalties or conviction rates affect crime rates • How will changing welfare rules affect income distribution, employment, poverty and health care So : Econometric techniques are used in many fields besides economics : • Political science • Psychology • History • Biology • Medical reseacrh • Law • Financial analysis

10. The basic tool for econometrics is the linear regression model. Estimating a linear regression on two variables can be visualized as fitting a line through data points representing paired values of the independent and dependent variables. For example, consider Okun's law, which relates GDP growth to the unemployment rate. This relationship is represented in a linear regression where the change in unemployment rate is a function of an intercept, a given value of GNP growth multiplied by a slope coefficient and an error term, :

11. Linearity • The dependent variable is assumed to be a linear function of the variables specified in the model. The specification must be linear in its parameters. This does not mean that there must be a linear relationship between the independent and dependent variables. The independent variables can take non-linear forms as long as the parameters are linear. The equation qualifies as linear while , does not. • Data transformations can be used to convert an equation into a linear form. For example, the Cobb-Douglas equation—often used in economics—is nonlinear: • But it can be expressed in linear form by taking the natural logarithm of both sides

12. Expected error is zero • The expected value of the error term is assumed to be zero. This assumption can be violated if the measurement of the dependent variable is consistently positive or negative. The miss-measurement will bias the estimation of the intercept parameter, but the slope parameters will remain unbiased] • The intercept may also be biased if there is a logarithmic transformation. See the Cobb-Douglas equation above. The multiplicative error term will not have a mean of 0, so this assumption will be violated • This assumption can also be violated in limited dependent variable models. In such cases, both the intercept and slope parameters may be biased

13. Econometric theory uses statistical theory to evaluate and develop econometric methods. Econometricians try to find estimators that have desirable statistical properties including unbiasedness, efficiency, and consistency. An estimator is unbiased if its expected value is the true value of the parameter; It is consistent if it converges to the true value as sample size gets larger, and it is efficient if the estimator has lower standard error than other unbiased estimators for a given sample size. Ordinary least squares (OLS) is often used for estimation since it provides the BLUE or "best linear unbiased estimator" (where "best" means most efficient, unbiased estimator) given the Gauss-Markov assumptions.

14. Makasih……