Background
A moving average is a statistical technique used to smooth out short-term fluctuations and highlight longer-term trends or cycles in data. It involves calculating averages of different subsets of the full data set to produce a series of averages.
Historical Context
The concept of moving averages has been used in the analysis of economic and financial time series for many years. Initially popularized in technical analysis during the mid-20th century, moving averages have become standard tools in various fields including economics, finance, and data science.
Definitions and Concepts
Simple Moving Average (SMA)
The arithmetic average of n previous data points. Each data point in the window receives an equal weight.
Weighted Moving Average (WMA)
A technique that assigns higher weights to more recent data points, which implies the weights decline linearly or exponentially over time.
Exponential Moving Average (EMA)
A specific type of WMA where the weights decrease exponentially, giving more importance to recent observations. This is also known as *exponential smoothing.
Moving Average Process
A model for random processes where future values are expressed as a linear combination of past values plus noise.
Major Analytical Frameworks
Classical Economics
Primarily focused on the long-term trends and equilibria, classical economists may use moving averages to identify cyclical patterns unaltered by short-term anomalies.
Neoclassical Economics
Moving averages can help neoclassical econometricians extract trends from cyclical data for microeconomic analysis and general equilibrium modeling.
Keynesian Economics
Keynesians might use moving averages to smooth out statistical noise in macroeconomic time series, like GDP or inflation rates, to better understand aggregate demand fluctuations.
Marxian Economics
Marxian analysts could use moving averages to decompose time series of production, capital accumulation, and labor data to study long-term class struggles and capitalist crises.
Institutional Economics
Moving averages assist in analyzing evolving institutional patterns over time, smoothing data reflecting policy impacts on economic growth or small business development.
Behavioral Economics
Behavioral economists could apply moving averages to identify deviations or tricky patterns brought by irrational behavior in market data.
Post-Keynesian Economics
Post-Keynesians might utilize moving averages for deriving smoothed trends in effective demand, financial stability, and income distribution considerations.
Austrian Economics
Austrians might leverage moving averages to smoothe data on credit cycles or capital theory-related time series for illustration of business cycles.
Development Economics
To distinguish underlying trends from volatile series in development metrics, practitioners use moving averages on indicators like poverty rates or GDP growth in developing economies.
Monetarism
Monetarists can use moving averages to decipher trends in monetary aggregates over time, aiding in long-term monetary policy assessments.
Comparative Analysis
Different types of moving averages, such as simple, weighted, or exponential, have varied applicability. The selection often rests on the specific characteristics of the time series being analyzed and the exact purpose of the analysis, whether it’s detecting trend lines, filtering white noise, or forecasting future values.
Case Studies
Several empirical studies illustrate the applicability of moving averages in various contexts like stock price analysis, economic cycle prediction, and policy planning.
Suggested Books for Further Studies
- “Technical Analysis of the Financial Markets” by John J. Murphy
- “Economic and Financial Analysis with Moving Averages” by Sophia C. Lee
- “Time Series Analysis and Its Applications” by Robert H. Shumway & David S. Stoffer
Related Terms with Definitions
- Time Series: Data points indexed or listed in time order.
- Exponential Smoothing: Particular form of weighted moving average that applies exponentially decreasing weights.
- Technical Analysis: Method of evaluating securities by analyzing statistics generated by market activity, such as past prices and volume.
- Forecasting: Method of making predictions based on past and present data.
