Econometrics model is a theoretical construct that represents economic processes by a set of variables and a set of logical and/or quantitative relationships between them. The economic model is a simplified framework designed to illustrate complex processes, and often mathematical techniques. Economic models posit structural parameters. Structural parameters are underlying parameters in a model or class of models. A model may have various parameters and those parameters may change to create various properties. Methodological uses of models include investigation, theorizing, and fitting theories to the world.

Irrespective of the approach, the scientific method requires that every model yield precise and verifiable implications about the economic phenomena it is trying to explain. Formal evaluation involves testing the model’s key implications and assessing its ability to reproduce facts. Economists may use many tools to test their models, including case studies, lab-based experimental studies, and statistics.

The randomness of economic data often gets in the way, so economists must be precise when saying that a model “successfully explains” something. From a forecasting perspective that means errors are unpredictable and irrelevant on average. When two or more models satisfy this condition, economists generally use the volatility of the forecast errors to break the tie—smaller volatility is generally preferred.

An objective signal that an empirical model needs to be revised is if it produces systematic forecasting errors. Systematic errors imply that one or more equations of the model are incorrect. Understanding why such errors arise is an important part of the regular assessment economists make of models.

Econometric analysis can be described in terms of a process flow as below:

1. Statement of Theory

2. Mathematical model

3. Econometric Model

4. Obtaining Data

5. Estimation of Parameters

6. Hypothesis testing

7. Using the model

The process kicks start by formulation of the statement of the problem. This involves the statement of the theory or financial theory that has two or more variables. To illustrate the above steps Keynesian theory of consumption will be used. Keynes postulated that the marginal propensity to consume (MPC), the rate of change of consumption for a unit (dollar) change in income, is greater than zero but less than 1.

The theory will be postulated in mathematical modeling. Keynesian consumption function:

Y = β1 + β2X , but 0 < β2 < 1, Where Y = consumption expenditure and X = income, and where β1 and β2, known as the parameters of the model the intercept and slope coefficients.

After this the economic model will be developed, this is so because mathematical model of the consumption function given above is of limited interest, for it assumes that there is an exact or deterministic relationship between consumption and income.

The inexact relationships between economic variables, would be modified the deterministic consumption function as follows:

Y = β1 + β2X + u, Where u, known as the disturbance, or error, term, is a random (stochastic) variable that has well-defined probabilistic properties

The model at this stage has variables are unknown and data collection centred on the selected variables. Sources of information of the data are critical as it determine the level of accuracy. To estimate the econometric model, that is, to obtain the numerical values of β1 and β2, we need data.

The numerical estimates of the parameters give empirical content to the consumption function and it can be estimated that consumption function is:

Y= −184.08 + 0.7064Xi.

A statistical evaluation of the model should then be carried. Assuming that the fitted model is a reasonably good approximation of reality, there is need to develop suitable criteria to find out whether the estimates obtained are in accord with the expectations of the theory that is being tested.

When the researcher is satisfied with the model, then it can be used in the testing the theory or for forecasting or policy purposes. The theory can be used to predict the future value(s) of the dependent, or forecast, variable Y on the basis of known or expected future value(s) of the explanatory, or predictor, variable X.