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What is Econometrics?
Econometrics can be defined as the testing or development of economic theory by analyzing data using statistical methods. To analyze and quantify these economic theories, the methods used greatly rely on statistical inferences. You can make use of methods such as probability, simple and multiple regression analysis, correlation analysis, frequency distribution, time-series, simultaneous equations, and statistical inference. At Statistics Homework Helper, we specialize in all the concepts of Econometrics. Our experts can help you ace all your homework by providing you with customized solutions. If you are struggling with this topic, avail of our econometrics homework help immediately. Our professional homework writers can relieve you of all your homework stress and give you the peace of mind you need.
Lawrence Klein, Simon Kuznets, and Ragnar Frisch were the pioneers of econometrics. Their contributions were recognized when they went ahead to win the Nobel Prize in Economics in 1971. Econometrics is now very popular in today’s world. It is regularly used by Wall Street traders and in academics. It is applied in the study of income effect using observable data. In the economic domain a hypothesis may be made that if a person increases his income, his spending will definitely increase. If such an association is present and supported by data, an economist can conduct a regression analysis to understand if the relationship between income and consumption is statistically significant.
The first step is to get and analyze a set of data and establish a specific hypothesis that explains the shape and nature of the data set. For example, you can use data from historical prices of a stock index, unemployment, and inflation rates in different countries, or a survey on consumer finances. If you are testing the idea of high income leads to high spending, the dependent variable is spending while income rate is the independent variable. A linear relationship is the most common relationship. This means that any change in the independent variable will have a correlation with the dependent variable. This relationship is often explored using a simple regression model. This involves generating the best line of fit and testing how far each data point is. If you decide to use more than one independent variable then the model is referred to as multiple linear regression.
Although there is a myriad of regression models, you can optimize one depending on the nature of the data you are analyzing and the type of question that is being asked. Ordinary least-squares (OLS) regression is the most common example. This tool can be used on a number of time-series or cross-sectional data. Econometrics is now done using statistical analysis software packages such as STATA, SPSS, or R.
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