# Predetermined variable

A predetermined variable is a variable whose current and lagged values, but not necessarily future values, are uncorrelated with the current error term in a dynamic econometric model.
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Updated: Jun 19, 2024

## 3 key takeaways

• Predetermined variables are uncorrelated with the current error term but may be influenced by past values.
• They are often used as instrumental variables to address endogeneity issues in econometric models.
• These variables are determined prior to the current period, providing stability and predictability in analysis.

## What is a predetermined variable?

In the context of dynamic econometric models, a predetermined variable is a variable whose current and past values are uncorrelated with the current error term, although its future values may not be.

More generally, it is a variable whose value is determined prior to the current period, making it an important tool in addressing endogeneity issues within models.

### Role in dynamic econometric models

Predetermined variables play a crucial role in dynamic econometric models by ensuring that the explanatory variables are not contemporaneously correlated with the error term.

This property helps obtain unbiased and consistent parameter estimates, which are essential for accurate model estimation and inference.

### Examples of predetermined variables

• Lagged dependent variables: Variables that represent past values of the dependent variable.
• Exogenous variables: Variables that are determined outside the model and are not influenced by the endogenous variables within the current period.

## Importance of predetermined variables

Predetermined variables are essential in econometric modeling for several reasons:

• Instrumental variables: They are often used as instruments to tackle endogeneity problems, helping to isolate the causal relationship between variables.
• Model stability: By being uncorrelated with the current error term, predetermined variables provide stability to the model, improving the reliability of the estimates.
• Predictive accuracy: These variables help in achieving more accurate predictions by ensuring that the model parameters are not biased.

### Applications

Predetermined variables are widely used in various fields of economics and finance:

• Macroeconomic modeling: Using lagged economic indicators to predict future trends without bias from current period shocks.
• Time series analysis: Employing past values to forecast future movements while mitigating endogeneity.
• Policy analysis: Evaluating the impact of policies where predetermined variables help in isolating policy effects from other contemporaneous influences.

## Challenges in using predetermined variables

Despite their usefulness, predetermined variables come with certain challenges:

• Identification: Correctly identifying variables that are truly predetermined can be difficult, especially in complex models.
• Model specification: Mis-specifying the model by incorrectly assuming a variable is predetermined when it is not can lead to biased results.
• Data limitations: Limited availability of historical data can constrain the use of lagged variables as predetermined instruments.

Predetermined variables are vital tools in dynamic econometric modeling, helping to address endogeneity and improve the reliability of estimates.

For a more comprehensive understanding, consider exploring related topics such as instrumental variables, endogeneity, and time series analysis, which provide deeper insights into the complexities and applications of econometric modeling.

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