Background
The log-normal distribution is a continuous probability distribution of a random variable whose logarithm is normally distributed. This implies that if the random variable \(X\) is log-normally distributed, then \(Y = \ln(X)\) follows a normal distribution.
Historical Context
The concept of the log-normal distribution traces its origins to the work of mathematicians and statisticians studying phenomena where multiplicative processes prevail. Over time, its application has expanded across various fields, particularly in finance, environmental science, and economic modeling.
Definitions and Concepts
A log-normal distribution typically results from the effect of a large number of independent multiplicative sources of variation. It is characterized by asymmetry (positive skewness) and has a mean larger than its median. This contrasts with the normal distribution, which is symmetrical and results from additive effects.
Key properties include:
- If \(X\) is log-normally distributed, \(Y = \ln(X)\) follows a normal distribution.
- \(X\) appears as \( e^{(\mu + \sigma Z)} \), where \(Z\) is normally distributed.
- Its Probability Density Function (PDF) and Cumulative Density Function (CDF) differ in formulation from those of the normal distribution.
Major Analytical Frameworks
Classical Economics
Classical frameworks often assume normal distributions for variables; however, where multiplicative processes are relevant, the log-normal distribution provides a more accurate representation.
Neoclassical Economics
Used in consumer choice theory to model how prices and incomes, considered log-normally distributed, affect demand under constraints of rational behavior optimization.
Keynesian Economics
Less frequently applied, but may offer insight into irregular patterns of investment and growth rates that better fit multiplicative models.
Marxian Economics
Log-normal distributions can represent the inequality transmission mechanisms due to capital concentration effects.
Institutional Economics
Used to model the uneven distribution of opportunities and risks caused by institutional structures.
Behavioral Economics
Helps explain exponentially growing gambling behavior, overconfidence, or other cumulative behavior processes.
Post-Keynesian Economics
Can illustrate variations in growth and distribution rates from heterodox influences.
Austrian Economics
Highlights the impact of entrepreneur-driven multiplicative processes on market variations.
Development Economics
Log-normal distribution is relevant for analyzing skewed distribution of income, wealth, and economic growth rates in developing economies.
Monetarism
Applied in analyzing asset pricing and the skewed nature of financial returns relevant to money supply effects.
Comparative Analysis
While the normal distribution centers symmetrically around its mean implying equal likelihood for deviations on either side, the log-normal distribution is right-skewed, which suits data growing exponentially over time such as income and stock prices.
Case Studies
Empirical cases of log-normal distributions are common in financial portfolios, city sizes, income distribution across populations, and certain biological phenomena where growth factors multiply over time.
Suggested Books for Further Studies
- The Log-Normal Distribution by Malcolm B. Miller and Richard A. Freund.
- Probability Theory and Statistical Inference by Aris Spanos.
- Lognormal Distribution and Physical Processes: An Empirical Study by M. Varadharajan.
Related Terms with Definitions
- Normal Distribution: A symmetric, bell-shaped distribution of values, representing numerous independent additive sources of variation.
- Probability Density Function (PDF): A function that describes the likelihood of a random variable taking on a given value.
- Skewness: A measure of asymmetry in the probability distribution.
