**Econometrics** is the quantitative application of statistical conclusions, economic theory, and mathematical models using data to create theories or verify preexisting assumptions in economics and estimate future trends from the massive amount of data gathered over time.

Its purpose is to statistically analyse real-world data and then compare the results to the theory or ideas being tested for patterns. Firstly, you need to understand why we study economic theory, we do it to apply it in real life. We call it the empirical application of the theory but when we apply it in real life, we need data to support it. When we do not have data it is difficult to accept that theory, whether it is true or not, which is exactly why econometrics is a need. The next thing we need to understand is why do we need this subject, the things which come under this domain are:

- Economic theory
- Mathematical economics
- Statistics

When we combine these three, econometrics comes into the picture. We need to study econometrics because when we are studying economic theory, you’re moving towards graphical representation. But graphical representation has a limitation which is that it can only have two dimension variables which we can easily show on the graph, but when we have more than two variables we need an equation. Which is exactly where econometric comes into the picture. So when you study economics theory, for eg: you understand that consumption depends on income, these are the two factors which you can show on graph but what happens when we take more than one variable because our consumption depends on our income but also out wealth, taste and preferences as well as price of the other goods which is why we cannot represent it on a graph but we can represent it on a econometric equation.

**What does Econometrics mean ? **

The term econometrics comes from a polish Economist Pawel Ciompa in 1910. However, econometrics became relevant quite late, which was in 1969 after the work of Ragnar Frsich and Jan Tinberg who defined the path for its future use.Econometrics is a branch of economics which uses mathematical methods for describing economic systems. Econometrics analysis and tests economics theories to verify hypotheses and improve prediction of financial trends. It helps economists to convert theories into quantifiable metrics. It is a crucial branch of economics which is crucial for establishing trends between datasets. These trends help economists get specific results from cluttered data.

**How Do I study Econometrics ?**

Even if you didn’t have maths as a subject earlier, you can still study econometrics even though it is one of the toughest branches of economics if you try to study it in a simple way. The first thing is, when you study econometrics you need to draw a timeline and then proceed with that subject.

**Linear regression:**First thing you should start with should be the linear regression model, which is a simple regression model and also to keep in that regression is the heart and soul of economics.**Multiple regression model:**The second is the multiple regression model, so these two you can study at one place or study a combination of these two major concepts.**OLS:**The OLS model stands for Ordinary Least Squares, where we have a total of six assumptions. If you understand those six assumptions and gain conceptual clarity then you’ll be able to understand econometrics because they form the foundation of this subject.**Problems in regression:**When you study regression, whether it is multiple or linear then these assumptions will hold true but as we try to relax those assumptions there are certain problems which come into the picture, there problems are called**multicollinearity, heteroscedasticity**and**autocorrelation.**There are some other errors in the variables as well but these are the three major problems.**Simultaneous Equation method:**Apart from this there is another important concept which is simultaneous Equation Methods, where there is one dependent variable and one independent variable. Here one dependent variable is also an independent variable in another equation. For example money supply depends on income, then income ultimately depends on money supply. So it’s like a simultaneous equation comes into the picture. Then the problem comes about identification. So these were the major problems.**Time series and data analysis**: If you’re able to understand these assumptions then you’ll be able to understand this subject.

## What is the syllabus for Econometrics?

### 1. Introduction

1.1 The Nature of Statistics

1.2 Statistics and Econometrics

1.3 The Methodology of Econometrics

### 2. Descriptive statistics

2.1 Frequency Distribution

2.2 Measures of Central Tendency

2.3 Measures of Dispersion

2.4 Shape of Frequency Distribution

### 3. Probability and probability distribution

3.1 Probability of Single Event

3.2 Probability of Multiple Events

3.3 Discrete Probability Distributions: The Binomial Distribution

3.4 The Poison Distribution

3.5 Continuous Probability Distributions: The Normal Distribution

### 4. Statistical inference estimation

4.1 Sampling

4.2 Sampling Distribution of The Mean

4.3 Estimation Using The Normal Distribution

4.4 Confidence Intervals for The Mean Using The T Distribution

### 5. Statistical inference : Testing hypothesis

5.1 Testing Hypothesis

5.2 Testing Hypothesis about The Population Mean And Proportion

5.3 Testing Hypothesis for Differences Between Two Means Or Proportions

5.4 Chi Square Test of Goodness of Fit and Independence

5.5 Analysis of Variance

5.6 Non Parametric Testing

### 6. Simple regression analysis

6.1 The two variable linear model

6.2 The ordinary least squares method

6.3 Test of significance of parameter estimates

6.4 Test of goodness of fit and correlation

6.5 Properties of Ordinary Least-Squares Estimators

### 7. Multiple regression analysis

7.1 The Three Variable Linear Model

7.2 Tests of Significance of Parameter Estimates

7.3 The Coefficient of Multiple Determinations

7.4 Test of The Overall Significance Of The Regression

7.5 Partial Correlation Coefficient

7.6 Matrix Notation

### 8. Further techniques in regression and analysis

8.1 Functional Form

8.2 Dummy Variables

8.3 Distributed Lag Models

8.4 Forecasting

8.5 Binary Choice Models

8.6 Interpretation of Binary Choice Models

### 9. Problems in Regression Analysis

9.1 Multicollinearity

9.2 Heteroscedasticity

9.3 Autocorrelation

9.4 Errors in Variables

### 10. Simultaneous Equations Method

10.1 Simultaneous Equations Models

10.2 Identification

10.3 Estimation Indirect Least Squares

10.4 Estimation Two Stage Least Squares

### 11. Time series method

11.1 ARMA

11.2 Identifying ARMA

11.3 Non Stationary Series

11.4 Testing For Unit Root

11.5 Cointegration and Error Correlation

11.6 Casuality

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