Estimation and Types of Estimation

Meaning of Estimation

Estimation is a critical component of econometrics and statistics, which involves the analysis of economic data using statistical methods. In this context, estimation refers to estimating or inferring the values of population parameters such as the mean, proportion, or variance from sample data. Economic phenomena are measured using variables, and samples are drawn from a population of interest. Estimation results are used to make business decisions, such as whether to launch a new product or invest in a specific market.

Estimation is a part of Inferential statistics. Using sample data, inferential statistics are used to test hypotheses and draw conclusions about the population. Estimation is important in assisting economists in understanding and making sense of the complex economic phenomena that affect our world.

To summarize, Estimation is a process by which the population parameters like mean, proportion, variance, etc., are forecasted and predicted on the basis of information that is collected from the samples.

Types of Estimation

Estimation is a fundamental concept in statistics and econometrics, and it is crucial in data analysis and decision-making. Point estimation and interval estimation are the two main types of estimation.

1. Point Estimation

Point estimation is the process of estimating the value of an unknown population parameter, such as the mean or variance, using sample data. Point estimation is the simpler of the two types of estimation because it involves estimating a single value. As an estimate of the population parameter, a sample statistic such as the sample mean, or sample variance is used in this method.

For example, if we wanted to estimate a population’s average income, we could take a random sample of people and use their average income to estimate the population means.

Merits of Point Estimation	Demerits of Point Estimation
Simple to calculate	Does not provide a measure of uncertainty
Easy to interpret	May be less precise than interval estimation
Provides a single estimate	Sensitive to outliers and extreme values
Useful for hypothesis testing	Requires assumptions about the underlying distribution
It can be used with any sample size.	It may be biased if the sample is not representative.

2. Interval Estimation

Interval estimation includes estimating the range of values that the population parameter is likely to fall within. Interval estimation entails estimating a range of values. When the population parameter is unknown, and we want to quantify our uncertainty about its value, we use this type of estimation. A sample statistic is used in interval estimation to estimate a confidence interval, which is a range of values that we believe the population parameter is likely to fall within.

For example, if we wanted to estimate a population’s average height, we could take a random sample of people and use their heights to calculate a confidence interval that includes the true population means with a certain degree of certainty.

Merits of Interval Estimation	Demerits of Interval Estimation
Provides a measure of uncertainty around the point estimate	Calculations can be more complex and time-consuming
Allows for greater precision in estimates	It may require larger sample sizes to achieve the desired precision.
It can be used to quantify the degree of confidence in the estimate.	May be less intuitive for some audiences than point estimates
Helps avoid overconfidence in point estimates	The resulting interval may be too wide to be practically useful
Enables researchers to compare multiple estimates to determine which is the most reliable.	The choice of confidence level can affect the width of the interval and the degree of certainty in the estimate.