Functional forms in regression
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The document discusses various functional forms of regression models, including log-linear, log-lin, lin-log, reciprocal, and polynomial models. It explains the concept of logarithms, with examples of calculating the log and natural log of a value. Additionally, it touches on the elasticity of production and its calculation.
Functional forms in regression
- 1. Functional forms of regression models: 1. The log-linear model, in which both the dependent variable and the explanatory variable are in logarithmic form. 2. The log-lin or growth model, in which the dependent variable is logarithmic but the independent variable is linear. 3. The lin-log model, in which the dependent variable is linear but the independent variable is logarithmic. 4. The reciprocal model, in which the dependent variable is linear but the independent variable is not. 5. The polynominal model, in which the independent variable enters with various powers.
- 2. Log and Natural log • For Log, the base is (usually) 10. • So, if the acreage of cotton in Junagadh is 28250 ha and you wanna work log of it – then what actually you are doing is that you are seeing 10 raised to the power of what gives you 28250. • LOG(28250) is 4.451018452. • In case of natural log, the base is e and its value being 2.718. • So, ln (28250) = 10.24885.
- 21. Elasticity of Production (EP) • EP = % change in output % change in input • EP = % change in output = MP % change in input AP 2/3/2021 10:52:59 AM 21