Praktikum Ekonometrika II - Analisis Panel Statis di EViews

Summary

This video demonstrates a static panel data analysis using EViews, focusing on poverty and its relationship with factors like income inequality, minimum wage, life expectancy, and unemployment in 35 cities of Central Java (2011-2018). The analysis explores different models, including fixed and random effects, using Hausman and Chow tests to determine the best approach. Results show that economic growth and life expectancy reduce poverty, while unemployment exacerbates it. The fixed effect model was found to be the most appropriate, with statistical checks ensuring the validity of assumptions for the final model.

Takeaways

• 😀 Data panel analysis is conducted using EViews software with data from 35 districts in Central Java, Indonesia, spanning 2011 to 2018.
• 😀 Panel data combines cross-sectional and time-series data, with time variables indicated in columns to reflect year-based data.
• 😀 The dependent variable used is poverty, measured by the number of poor people in households, while independent variables include Gini index, minimum wage, life expectancy, and unemployment rate.
• 😀 The dataset setup in EViews requires importing and copying only the relevant data after arranging the panel data structure correctly.
• 😀 The analysis involves the selection of the most appropriate model—fixed effect, random effect, or pooled—using tests like Hausman and Chow.
• 😀 The Hausman test helps determine whether the fixed effect model is more appropriate than random effect, with a small p-value indicating the former.
• 😀 The Chow test helps compare fixed effect models against pooled models, assisting in model selection based on statistical significance.
• 😀 Autocorrelation is checked using the Durbin-Watson statistic to ensure the Gauss-Markov assumptions are met in the regression model.
• 😀 Heteroskedasticity is checked in higher versions of EViews using additional tests, although it's noted that older versions lack an automatic heteroskedasticity feature.
• 😀 The fixed effect model reveals that poverty is significantly influenced by economic growth and life expectancy, while unemployment has a positive effect, but minimum wage does not show a statistically significant impact.
• 😀 The importance of individual heterogeneity in the fixed effect model is emphasized, with cross-sectional constants provided to understand unique regional effects.

Q & A

What is the main focus of the video script?

The video focuses on performing static panel data analysis using EViews software, specifically for data from 35 districts in Central Java, Indonesia, spanning from 2011 to 2018.

What type of data is being analyzed in the script?

The data being analyzed is panel data, which combines cross-sectional data and time series data for 35 districts in Central Java, Indonesia, from 2011 to 2018.

Which variables are used in the panel data analysis?

The independent variables used in the analysis are poverty (measured by the number of poor people in each district), the Gini coefficient, the minimum wage, life expectancy, and the unemployment rate.

How is the data set up in EViews for analysis?

The data is set up by copying the relevant data into EViews, where it is structured in a panel format with a cross-section (district) and a time variable (year).

What are the key steps to estimate a panel data model in EViews?

The key steps include setting the data type as 'panel data,' specifying the time and cross-section variables, applying a transformation (e.g., log for certain variables), selecting a panel model type (Fixed Effect, Random Effect, etc.), and performing model estimation.

What does the Hausman test do in panel data analysis?

The Hausman test is used to choose between the Fixed Effect and Random Effect models. A significant p-value indicates that the Fixed Effect model is more appropriate.

What does the Fixed Effect model imply in the context of this analysis?

The Fixed Effect model implies that the individual characteristics of each district (cross-section) can affect the dependent variable, and these characteristics are accounted for as constant over time.

What statistical test is used to choose between the Fixed Effect and Random Effect models?

The Hausman test is used to decide between the Fixed Effect and Random Effect models. A p-value less than 0.05 suggests that the Fixed Effect model is more suitable.

What conclusion is drawn from the Hausman test in the script?

The conclusion from the Hausman test is that the Fixed Effect model is the more appropriate choice for this analysis, as indicated by the significant p-value.

How is heteroskedasticity addressed in the analysis?

Heteroskedasticity is mentioned as a potential issue but is not fully addressed in the earlier versions of EViews used in the analysis. Later versions of EViews would have more tools to handle heteroskedasticity.