What are the core econometric techniques used in data analysis?
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As the data collection methods have extreme influence over the validity of the research outcomes, it is considered as the crucial aspect of the studies
What are the core econometric techniques used in data analysis?
Introduction
1. Introduction
Econometric methods are meant to analyze large and complex datasets, with testing and prediction of theories among other applications such as economics, finance, health care, and policy analysis. These methods also assist researchers in estimating the nature of relationships between variables while handling the influence of heteroscedasticity, autocorrelation, endogeneity, and omitted variable bias.
Linear Regression (Ordinary Least Squares - OLS)
Definition:
An OLS model estimates the relationship between the dependent variable and one or more independent variables by minimizing the sum of squared residuals.
Example:
A researcher investigates the effect of education on income:
Incomei​ = β0​ + β1 ​× Educationi​ + ϵi​
In this case, Outcome is the dependent variable, and Education (measured as years of schooling) is the independent variable.
Time Series Models
Time series analysis is done whenever we’ve collected data points over time. It’s an effective system for relating trends, seasonal behaviours, and cyclical behaviours.
3.1 Autoregressive Integrated Moving Average (ARIMA)
Definition:
ARIMA models generate forecasts of future points by regressing the variable onto its own lagged (historical) values and previous errors in forecasts.
Example:
Forecasting monthly inflation rates based on values in past months’ inflation rates.
3.2 Vector Autoregression (VAR)
Definition:
VAR models capture the linear interdependencies among several time series.
Example:
Assessing the dynamic relationship between rates and GDP growth over time.
Panel Data Models
Panel Data Models are compliances on multiple entities (individuals, firms, or countries) across time
Fixed Effects Model
Definition:
Controls for the time-invariant characteristics in each entity and only examines the changes in those characteristics over time.
Example:
Examining the effect of changes in the minimum pay envelope in different states.
Random Effects Model
Definition:
Assumes each entity’s error terms are random and not correlated with the predictors.
Example:
Examining a set of firms’ productivity when individual firm factors are modelled as random.
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Instrumental Variables (IV) Approach
Definition:
Refers to situations when an independent variable is identified with the error term, which results in endogeneity bias. An instrument is a variable that’s identified with the endogenous explicatory variable and uncorrelated with the error term.
Example:
Looking at the relationship between education and earnings, with distance to colleges as the instrument for educational attainment.
Difference-in-Differences (DiD) Technique
Definition:
A quasi-experimental design where changes in outcomes are compared over time for treatment and control groups.
Example:
An evaluation of the effect of a new tax policy on small business revenue would compare business revenue in areas affected by the tax and areas unaffected by the tax, before and after policy implementation.
Logit and Probit Models
These models are used when the dependent variable is double.
Logit Model
Definition:
A model to predict the probability that a binary outcome variable takes on a value of one by adopting a logistic form.
Example:
Modeling the probability that a job aspirant is considered “hired” (1 = hired, 0 = not hired) based on experience and education.
Probit Model
Definition:
An analogous model to a logit model, and it uses the accretive normal distribution function.
Example:
Estimating a patient’s probability of recovering from an illness based on the type of treatment and the demographic factors affecting these patients.
Conclusion
Knowing the core econometrics to increase the credibility of the empirical findings to ultimately increasing decision-making quality in academic, government, or corporate environments. Picking the appropriate model is determined by the research question, data type, and study assumptions.
References
- Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach. 7th Edition. Cengage Learning.
- Stock, J. H., & Watson, M. W. (2020). Introduction to Econometrics. 4th Edition. Pearson.