Background
The methodology of least squares is foundational in many econometric and statistical models. It is a technique employed in regression analysis to estimate the unknown parameters of a model. The key idea is to minimize the discrepancies between observed and estimated values of the model’s dependent variable.
Historical Context
The least squares method was first formulated by Carl Friedrich Gauss around 1795, although it was formally published by Adrien-Marie Legendre in 1805. Gauss defended his method rigorously, solidifying it within the sinuous world of statistical data to become profoundly indispensable in the ensuing centuries.
Definitions and Concepts
Least squares is a statistical approach for estimating the coefficients of a regression model. It accomplishes this by minimizing the sum of the squares of the residuals, being the differences between observed and predicted values:
\[ \text{Residual Sum of Squares (RSS)} = \sum_{i=1}^{n} (y_i - \hat{y}_i)^2 \]
Here, \( y_i \) are the observed values, and \( \hat{y}_i \) are the predicted values via the regression model. Two primary forms include:
- Ordinary Least Squares (OLS): Assumes the error terms are normally distributed with constant variance and there is no perfect multicollinearity.
- Generalized Least Squares (GLS): Adjusts OLS when there are complications like heteroskedasticity or autocorrelation.
See also various specific methodologies like Two-stage Least Squares (2SLS) and Weighted Least Squares (WLS) which address more complex scenarios.
Major Analytical Frameworks
Classical Economics
Focuses less on methodology like least squares due to its a prioristic nature and more on large foundational questions of resource allocation and value.
Neoclassical Economics
Utilizes least squares in empirical studies to test theories of market behavior, consumer choice, and firm production emphasized by optimization and equilibria.
Keynesian Economics
Employs least squares extensively to model relationships such as consumption-income dynamics and the determination of macroeconomic output and employment.
Marxian Economics
Uses alternative statistical models for empirical analysis, but least squares can still contribute to modeling capitalism’s economic cycles and inequalities.
Institutional Economics
Analyzes evolutionary changes in institutions; least squares can adjust empirical models reflecting behavioral and structural variables.
Behavioral Economics
Uses least squares to quantify deviations from rational behavior, validating hypotheses related to heuristics and biases.
Post-Keynesian Economics
Relies on least squares and GLS to model non-linear and dynamic macroeconomic patterns beyond classical equilibria.
Austrian Economics
Feature minimalist aggregate data usage—critical of frequentist statistics like least squares, preferring qualitative analysis.
Development Economics
Relates to micro impact evaluations using least squares for analyzing policy effectiveness and economic growth predictors in diverse global contexts.
Monetarism
Empirically validates hypotheses regarding money supply dynamics on inflation and economic growth through least squares regressions in time series data.
Comparative Analysis
Ordinary least squares (OLS) and variations like generalized or weighted least squares offer flexibility in diverse economic research streams. Their robustness under minimal assumptions and straightforward interpretations facilitate widespread adoption across both micro and macroeconometric analyses.
Case Studies
- Economic Growth Analysis: Least squares applied to cross-country GDP growth data to determine predictors like capital intensity, CTO, and sociopolitical stability.
- Income-Consumption Relations: Keynesian consumption functions widely estimated via OLS to model marginal propensity to consume.
- Policy Impact Evaluation: DSC Sub-Saharan Africa education interventions measured via least-square regressions yielding policy efficacy on enhanced literacy rates.
Suggested Books for Further Studies
- “The Econometric Analysis of Economic Time Series” by Andrew C. Harvey
- “Introduction to Econometrics” by James H. Stock and Mark W. Watson
- “Handbook of Econometrics” edited by James Heckman and Edward Leamer
Related Terms with Definitions
- Gauss–Markov Theorem: States that under certain conditions, OLS is the Best Linear Unbiased Estimator (BLUE).
- Indirect Least Squares (ILS): A technique combining indirect estimation method and least squares for identifying model parameters.
- Pooled Least Squares: Method merging cross-sectional and time-series data to enhance estimation accuracy.
- Two-Stage Least Squares (2SLS): A method addressing endogeneity by using instrumental variables in regression.
Understanding the myriad applications of least squares and its variants not only underscores its pivotal role in economics but also reveals the richness of capabilities economists have developed to extract insights from complex data landscapes.
