What Is Serial Correlation?

Serial correlation, also known as autocorrelation, occurs in regression analysis involving time series data when successive values of the random error term are not independent.

Serial correlation, also known as autocorrelation, is a common issue in regression analysis involving time series data. It occurs when the residual (error) terms from a regression model are correlated across time periods. This violates one of the key assumptions of classical linear regression models—that the error terms are independent.

Implications of Serial Correlation

Impact on Estimation

When serial correlation is present in a regression model, the ordinary least squares (OLS) estimators of the coefficients remain unbiased; however, they become inefficient and their standard errors are underestimated. This leads to misleading inferences.

Detection

There are several tests to detect serial correlation:

Durbin-Watson Test

$$ DW = \frac{\sum_{t=2}^T (e_t - e_{t-1})^2}{\sum_{t=1}^T e_t^2} $$

Values of DW near 2 suggest no serial correlation, values approaching 0 indicate positive serial correlation, and values toward 4 suggest negative serial correlation.

Breusch-Godfrey Test

A more general test that can detect higher-order serial correlation and is adaptable to complex models like those including lagged dependent variables.

Causes of Serial Correlation

• Omitted Variables: A variable that should be included in the model is left out, resulting in correlated error terms.
• Model Misspecification: Incorrect functional form or dynamic specification.
• Dynamic Relationships: When past values of the dependent variable influence current values (dynamic lag effects).

Mathematical Representation

If \( \epsilon_t \) represents the error term at time \( t \):

First-Order Autoregressive Process (AR(1))

$$ \epsilon_t = \rho \epsilon_{t-1} + u_t $$

where \( \rho \) is the autocorrelation coefficient, and \( u_t \) is a white noise error term.

Higher-Order Processes

More complex forms include AR(p), where p indicates the order of autoregression.

Examples and Applications

Economics and Finance

In financial models, serial correlation can appear in the returns of financial assets. For example, stock market indices may exhibit autocorrelation due to inherent market trends or trading patterns.

Climate Studies

Serial correlation is often seen in climate data analysis, where current temperatures may be correlated with past temperatures.

Corrective Measures

Generalized Least Squares (GLS)

Transforms the original model to nullify the serial correlation, improving efficiency of the estimators.

Cochrane-Orcutt Procedure

Specifically designed iterative method for correcting first-order autoregressive scheme.

Autoregressive Integrated Moving Average (ARIMA) Models

In time series analysis, ARIMA models accommodate serial correlation by incorporating autoregressive and moving average components.

Related Terms

• Autocorrelation: Another term for serial correlation.
• White Noise: A series of uncorrelated random variables with constant mean and variance.
• Time Series Analysis: The statistical techniques for analyzing time series data.

FAQs

Q1: How does serial correlation affect hypothesis testing in regression models? A1: Serial correlation causes incorrect estimation of standard errors, leading to misleading t-statistics and F-statistics and thus, unreliable hypothesis tests.

Q2: Can serial correlation be present in non-time-series data? A2: Yes, although it is more common in time series data, spatial data and panel data can also exhibit serial correlation.

Q3: What software tools can detect and correct serial correlation? A3: Statistical software like R, Python (statsmodels), Stata, and EViews provide functions and procedures for detecting and correcting serial correlation.

References

• Gujarati, D. N. (2003). Basic Econometrics. McGraw-Hill.
• Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (2008). Time Series Analysis: Forecasting and Control. Wiley.
• Wooldridge, J. M. (2012). Introductory Econometrics: A Modern Approach. Cengage Learning.

Summary

Serial correlation is a critical issue in regression analyses involving time series data. It indicates the presence of correlation between error terms across different time periods, often due to omitted variables or model misspecifications. Multiple statistical tests and corrective methods exist to handle this issue, ensuring the reliability and efficiency of the regression models and their corresponding inferences.