Statistical methods in economics
Economics is the branch of science that investigates how goods and services are produced, distributed, and consumed. Economists employ a wide range of statistical methods to study the production of goods and how they are distributed to the consumers and come up with proper findings on these variables. There are quite a number of statistical methods in economics that students need to familiarize themselves with throughout their econ course. Some of these include:
- Conditional probability and independence
- Continuous and discrete random variables
- Sampling techniques
- Data visualization
- Estimation of population parameters
- Point and interval estimation
- Hypothesis testing
- Regression analysis
Specifically, our economics assignment help experts argue that you need to be competent in probability distributions, hypothesis testing, and calculating descriptive statistics to perform effective economics data analysis. Other skills that will help students apply the aforementioned statistical methods to their economics projects include:
- A good understanding of the statistical inferential procedures used to analyze data in real word
- Ability to formulate models that can solve real empirical economics problems
- A sound comprehension of the relationships between variables in the data being studied
- Ability to interpret and present the numeric outcomes
- A hands on experience in utilizing statistical software programs
- Ability to carry out research and present findings using relevant statistical tools
- Limitations associated with statistical methods in economics
The student’s ability to quantify economic relationships accurately depends highly on his/her ability to build econometric models and the quality of the data used for the analysis. One also needs to have the capability to apply the right strategies to estimate models that have a likelihood of violating statistical assumptions. Economics data that needs to be analyzed must be obtained from a reliable source and one should be aware of any challenges or limitations that may occur during the analysis including:
- Statistically correlated but economically irrelevant data: You may have a data set that contains voluminous information but most of the variables in the data may have no relationship with the economic issue being addressed.
- Qualitative data: Comprehensive data sets will usually contain qualitative variables like geographical information, age, race, and so on. However, this information requires sorting and cleaning before it can be used in an economics model.
- Liner regression model assumption failure: The accuracy of your results and importantly, econometric approach, always depends on the available set of statistical assumptions. One must make sure that linear regression model assumptions are not violated to increase the chances of producing the most effective results.
If you wish to learn more about the challenges and limitations that most people overlook when analyzing data, connect with our economics assignment help experts.
Incorporating quantitative and qualitative statistical methods
Economics outcomes can be influenced by both the numeric (quantitative) factors and non-numeric (qualitative) factors. Basically, quantitative data has a straightforward application in econometric models as well as their interpretation. Qualitative variables on the other hand are associated with information that has no numeric representation. In order to use these variables in your models, you have to first convert them into dummy variables. Dummy variables are dichotomous variables that apply values 1 or 0 to qualitative data to allow for effective analysis. For instance, instead of using M or F for gender, you can simply use 1 or 0. Our economics assignment help experts state that knowing the type of variables you have in your economics data will help you choose the right statistical methods to perform your analysis and create the most effective models.