Chapter 2: Steps of Econometric Analysis Econometrics Econ. 405 Chapter 2: Steps of Econometric Analysis

Steps of econometric analysis Traditional econometric methodology proceeds along the following lines: 1. Statement of theory or hypothesis. 2. Specification of the mathematical model of the theory (Economic Model) 3. Collecting the data 4. Specification of the statistical, or econometric model 5. Estimation of the parameters of the econometric model 6. Hypothesis testing 7. Using the model for control or policy purposes.

1. Statement of theory or hypothesis. on average, Kuwaiti consumers increase their consumption of goods and services as their incomes increase

2. Specification of the mathematical model of the theory (Economic Model) Y = β1 + β2 X 0 < β2 < 1 Y = consumption expenditure (dependent variable) X = income (independent, or explanatory variable) β1 = the intercept β2 = the slope coefficient The slope coefficient β2 measures the MPC in this case.

Illustrate Geometrically the relationship between Consumption (Y) and Income (X):

3. Collecting the data

Y = β1 + β2X + u Consumption = β1 + β2 Income + u 4. Specification of the statistical (econometric model) The mathematical (economic) model is modified as : Y = β1 + β2X + u Consumption = β1 + β2 Income + u Note: The relationships between economic variables are generally inexact. In addition to income, other variables affect consumption expenditure. For example, size of family, ages, gender, etc., are likely to have some influence on consumption. Thus: term “u” is known as the disturbance (error term), is a random (stochastic) variable that has well-defined probabilistic properties. The disturbance term “u “ may well represent all those factors (size of family, ages, behavior, etc) that affect consumption but are not taken into account explicitly.

The relationship between Consumption (Y) and Income (X) is a linear regression model( i.e., it hypothesizes that Y is linearly related to X), but that the relationship between the two is not exact; it is subject to individual variation:

5. Estimation of parameters of econometric model Regression analysis uses an econometrics software to obtain estimates. Using this technique and the data given, we obtain the following estimates of β1 and β2, namely, −184.08 and 0.7064. Thus, the estimated model is: Yˆ = −184.08 + 0.7064Xi The estimated regression line is shown in the following Figure. The regression line fits the data quite well. The slope coefficient (i.e., MPC) was about 0.70. β2 states that an increase in income by 1 KD led, on average, to an increase consumption by 700 Files (Or, if income increases by 1 unit, consumption increases by 70%.

The econometrics model is now plotted the following estimated regression line : Y X

6. Hypothesis Testing A- Significance of the findings (estimates ) It is the most common expression used when dealing with quantitative methods. Here, the slopes (β’s) are the focus. Statistically significant refers to the case that the relationship is unlikely due to chance ( probably true) There are several ways to determine the significance of the result ( t-test, P-value, F-test….etc.)

B- Consistency of the findings (estimates ) The question : DOES your result (estimated parameters) make sense? The Answer : Check what the theory says. In this example: Theory: Keynes expected MPC to be positive but less than 1 Findings: MPC to be positive and about 0.70

7. Using the model for control or policy purposes The question : DOES your result (estimated parameters) reveal any policy implications? The Answer : Depends on how readable they are !! In this example: Findings: People of Kuwait spend more when they have higher incomes Policy implication: if the government wants to reduce inflation they might think of reducing spending by public, this is can be done through influencing their income. Then the solution is to tax them !! Note: Policy implication should be linked to your findings.