The econometric techniques use economic theories and statistical tools to measure the economic variables and forecast the research variables. The econometric model can either be a system of simultaneous equations or a one-equation regression model. In most cases, the one-equation regression model is adopted. Econometric methods constitute to primary methods, including regression method and simultaneous equation models.
In the methodology of econometrics, determining the demand function is the most important stage in getting it right in the development of a good econometric model. Under the regression methodology, the very first stage and the first thing that has to be determined is the demand function. While singling out the demand functions for numerous variables, the researcher may come across several variables/commodities whose demands are dependent largely on just one independent variable. For instance, assume in a town, the demand for a common product such as bread and cakes depend largely on the city population. Therefore, the demand function of bread and cake are said to be single-variable demand functions. Alternatively, if it is discovered that the demand for the items in the market like ice-creams, sweets, vegetables, and fruits amongst others are dependent on the number of variables like the price of the commodity, the prices of alternative goods, the income of the households, the population, tastes and preferences amongst others. Therefore, this kind of demand function is referred to as a multiple-variable demand function. Overall, the most important step would be determining the demand function.
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If I were an economist, there are a number of ways I would use to check if my model made accurate predictions. Normally, root means squared error is applied. Several Linear Regression models may be trained, removing or adding the dataset to determine which one of them has the lowest RMSE.
References
Maiti, M. (2017). Book Review: Shankar Kumar Bhaumik, Principles of Econometrics: A Modern Approach Using EViews.