Panel data analysis is a statistical analysis technique that is common in econometrics and social sciences where data is collected and observed at different time periods from the same subjects. This kind of analysis gives more insights about the data in terms of variability and more precise estimates of the relationships between variables. EViews is a specialized Statistical software that makes it easy to work with panel data. It is one of the essential software tools used by every student learning data analysis.
Are you a statistics student struggling with Eviews to analyze panel data? Do not worry, there are many students feeling overwhelmed with the technicality and complexity of the use of Eviews software. The most common issue that many students encounter is the complex interface and unique command language which they need to extensively practice in order to be a master on it. Cleaning and transforming big raw data can be a cumbersome tasks and students may commit mistakes in the process. In this post, we will guide you how to use eviews effectively to analyze panel data. We will also describe the very helpful data analysis assignment support services that we extend in order to help student overcome the complexities associated with analysis of data using commonly used softwares like sas, eviews, stata, R, minitab, spss etc.
Panel data or also known as longitudinal data contains the observation on multiple individuals or entities over multiple time periods. Examples include economic indicators relating to performance of various economies over several years, or customer behaviour tracked for several months.
Fixed effects model (FE) in panel data analysis is a statistical method that allows examining how predictor variables are related to an outcome variable within an entity (like individual, firm or country), taking into account the unobserved time-invariant characteristics of that entity. These aspects may involve things like cultural factors for individuals and management styles for firms.
In RE model, we have αi being assumed as random variables for the unobserved individual-specific effects, rather than fixed parameters as seen under FE model. It is assumed that these random effects are independent of the explanatory variables.
The Pooled Ordinary Least Squares (OLS) is a basic estimation method applied to panel data. This involves pooling observations from multiple entities and time periods into one cross-sectional dataset and then running a standard OLS regression model.
EViews is a powerful statistical package tool particularly important for econometric analysis which is commonly used in academic and industry environments due to the compatibility of a broad number of functions and robust interface. The capabilities for data analysis are numerous and can include regression analysis, time series forecasting, and hypothesis testing, therefore it can be used in a wide variety of complex tasks.
It is in the field of econometric analysis that EViews excels having a variety of tools for estimating and testing different econometric models. For example, it can take care of time series, cross-sectional and panel data analysis with features like linear and non-linear regression, ARIMA models, GARCH models, VAR models etc. EViews provides all-rounded tools to import, manage or transform data from various sources. It handles many forms of data such as spreadsheets, text files and databases. EViews comes with several forecasting algorithms that permit users to generate forecasts using their estimated models. It supports both point forecasts and interval forecasts. In addition to these econometric features, EViews has also been equipped with general statistical analysis including descriptive statistics, hypothesis testing and analysis of variance (ANOVA). EViews has a relatively simple graphical interface which is quite user-friendly even for people who are not experienced programmers. It comes with easily understandable tools for producing graphs and tables. For advanced users though there is a powerful command language in EViews that allows more flexibility and customisation for analyses.
Data Preparation:
Exploratory Data Analysis (EDA):
Model Selection:
Estimation and Interpretation:
Do’s
Dont’s
Do not ignore panel structure: Do not use pooled OLS without taking into account whether your data is panel or not. When the panel structures are not followed appropriately, then it leads to distorted outcomes and even bias.
Do not overlook variable selection: In order to avoid omitted variable bias you need to ensure that the correct set of variables are included in the model. A lot relies on choosing the right variables that will produce a correct result to your analysis.
Do not forget to check assumptions: You should always ensure that none of the assumptions that are related to the model are ever violated. Validation is a fundamental step for any work, to ensure accuracy of the results.
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