## (Stata13) Panel Data Descriptive Analysis (Scatterplots) : CrunchEconometrix

With the scatterplot, you can reveal the relationships between the outcome variable and an explanatory within the various categorical variables in your panel data.

In this video, using Stata13, I show the various ways of using the scatterplot to reveal various heterogeneities within and among the groups with the summary statistics of the outcome variable (Gini coefficient) and the different categorical variables such as countries, regions, income groups and inequality categories.

Besides, why do you want to perform panel data analysis? Some of the reasons could be to explore the behaviour of a variable across a sample of groups (e.g. firms, schools, countries, agro produce, individuals, cars, production style, political parties etc.); to explore the behaviour of the groups in the sample with respect to a variable; the groups have some semblance of commonness; the groups have varying heterogeneities (i.e. differences) with respect to a variable; perhaps due to inadequate data over a long period for your sample or maybe you just don’t know why! (lol).

I give 5 tips required for building an engaging panel data structure. They are (1) generate identifiers (watch my video on “Reshape Wide to Longitudinal Data” and “Tips to Building a Panel Data”); (2) reshape the data (watch my video on “Reshape Wide to Longitudinal Data”); (3) group classification (to explore the heterogeneities in the data); (4) categorise the outcome variable (to explore the heterogeneities in the data) and (5) create dummy variables (regional and time dummies).

Remember, graphics convey lots of information than words. So, load your data and let’s get started.

[Watch video clip]

## (Stata13) Panel Data Descriptive Analysis (Histograms) : CrunchEconometrix

In this video, using Stata13, I show the various ways of using the histogram to reveal various heterogeneities within and among the groups with the summary statistics of the outcome variable (Gini coefficient) and the different categorical variables such as countries, regions, income groups and inequality categories.

Besides, why do you want to perform panel data analysis? Some of the reasons could be to explore the behaviour of a variable across a sample of groups (e.g. firms, schools, countries, agro produce, individuals, cars, production style, political parties etc.); to explore the behaviour of the groups in the sample with respect to a variable; the groups have some semblance of commonness; the groups have varying heterogeneities (i.e. differences) with respect to a variable; perhaps due to inadequate data over a long period for your sample or maybe you just don’t know why! (lol).

I give 5 tips required for building an engaging panel data structure. They are (1) generate identifiers (watch my video on “Reshape Wide to Longitudinal Data” and “Tips to Building a Panel Data”); (2) reshape the data (watch my video on “Reshape Wide to Longitudinal Data”); (3) group classification (to explore the heterogeneities in the data); (4) categorise the outcome variable (to explore the heterogeneities in the data) and (5) create dummy variables (regional and time dummies).

Also note that a histogram (1) is not a bar chart, (2) shows the underlying distribution of a variable, (3) gives an accurate representation of the distribution of a numerical data, (4) gives the estimate of the probability distribution of a variable, (5) graphically displays data using bins (bars) of different heights and (6) shows the frequency distribution of a variable. Remember, graphics convey lots of information than words. So, load your data and let’s get started.

[Watch video tutorial]

## (Stata13) Panel Data Descriptive Analysis (Bar Charts) : CrunchEconometrix

Why do you want to perform panel data analysis?

Some of the reasons could be to explore the behaviour of a variable across a sample of groups (e.g. firms, schools, countries, agro produce, individuals, cars, production style, political parties etc.); to explore the behaviour of the groups in the sample with respect to a variable; the groups have some semblance of commonness; the groups have varying heterogeneities (i.e. differences) with respect to a variable; perhaps due to inadequate data over a long period for your sample or maybe you just don’t know why! (lol).

I give 5 tips required for building an engaging panel data structure. They are (1) generate identifiers (watch my video on “Reshape Wide to Longitudinal Data”); (2) reshape the data (watch my video on “Reshape Wide to Longitudinal Data”); (3) group classification (to explore the heterogeneities in the data); (4) categorise the outcome variable (to explore the heterogeneities in the data) and (5) create dummy variables (regional and time dummies).

erogeneities within and among the groups with the summary statistics of the outcome variable (Gini coefficient) and the different categorical variables such as countries, regions, income groups and inequality categories. Remember, graphics convey lots of information than words. So, load your data and let’s get started.

[Watch video tutorial]

## (Stata13) Panel Data Descriptive Analysis (Tables) : CrunchEconometrix

Why do you want to perform panel data analysis?

Some of the reasons could be to explore the behaviour of a variable across a sample of groups (e.g. firms, schools, countries, agro produce, individuals, cars, production style, political parties etc.); to explore the behaviour of the groups in the sample with respect to a variable; the groups have some semblance of commonness; the groups have varying heterogeneities (i.e. differences) with respect to a variable; perhaps due to inadequate data over a long period for your sample or maybe you just don’t know why! (lol).

I give 5 tips required for building an engaging panel data structure. They are (1) generate identifiers (watch my video on “Reshape Wide to Longitudinal Data”); (2) reshape the data (watch my video on “Reshape Wide to Longitudinal Data”); (3) group classification (to explore the heterogeneities in the data); (4) categorise the outcome variable (to explore the heterogeneities in the data) and (5) create dummy variables (regional and time dummies).

In this video, I show the various tabular dynamics that can explored using the summary statistics of the outcome variable (Gini coefficient) and the different categorical variables such as countries, regions, income groups and inequality categories. So, load your data and let’s get started.

[Watch video tutorial]

## Tips to Building Panel Data in Stata : CrunchEconometrix

Why do you want to perform panel data analysis?

Some of the reasons could be to explore the behaviour of a variable across a sample of groups (e.g. firms, schools, countries, agro produce, individuals, cars, production style, political parties etc.); to explore the behaviour of the groups in the sample with respect to a variable; the groups have some semblance of commonness; the groups have varying heterogeneities (i.e. differences) with respect to a variable; perhaps due to inadequate data over a long period for your sample or maybe you just don’t know why! (lol).

I give 5 tips required for building an engaging panel data structure. They are (1) generate identifiers (watch my video on “Reshape Wide to Longitudinal Data”); (2) reshape the data (watch my video on “Reshape Wide to Longitudinal Data”); (3) group classification (to explore the heterogeneities in the data); (4) categorise the outcome variable (to explore the heterogeneities in the data) and (5) create dummy variables (regional and time dummies).

[Watch video tutorial]

## (Stata13): VAR Estimation and Diagnostics : CrunchEconometrix

How can you explain a vector autoregressive (VAR) model?

The word “autoregressive” indicates the presence of the lagged values of the dependent variable on the right-hand side of the equation. The word “vector” implies that the system contains a vector of two or more variables.

A VAR model is constructed only if the variables are integrated of order one. That is, stationary after first difference. If the variables are cointegrated, construct both short-run (VAR) and long-run (VEC) models.

If variables are NOT cointegrated, construct only the short-run (VAR) model. All the variables in a VAR system are endogenous; there are no exogenous variables. The stochastic error terms are often called impulses, or innovations or shocks. All variables in the system have equal lags.

VAR must be specified in levels, hence VAR in differences is a mis-specification!

The VAR model is estimated by ordinary least squares (OLS).

Deciding on the maximum lag length, (an empirical issue). If you use too many lags, you will lose many degrees of freedom, incur statistically insignificant coefficients and multicollinearity. If there are too few lags, there may be specification errors. So, choose optimal lags using the information criterion: AIC, SC, HQIC etc.

Also, the interpretation of the short-run coefficients is as in any other linear model; they are ceteris-paribus effects and inference can be based on the usual OLS standard errors and test statistics.

What are some of the reasons for estimating VAR model? (1) Because there is no cointegration among the variables in the system; (2) to establish causal relationships; (3) to simulate shocks to the system and trace out the effects of shocks on the endogenous variables; (4) for forecasting (decomposing shocks to the VAR system).

[Watch video clip on VAR estimation and diagnostics]

## (Stata13) VAR Estimation and Discussions : CrunchEconometrix

How can you explain a vector autoregressive (VAR) model?

The word “autoregressive” indicates the presence of the lagged values of the dependent variable on the right-hand side of the equation. The word “vector” implies that the system contains a vector of two or more variables.

A VAR model is constructed only if the variables are integrated of order one. That is, stationary after first difference. If the variables are cointegrated, construct both short-run (VAR) and long-run (VEC) models.

If variables are NOT cointegrated, construct only the short-run (VAR) model. All the variables in a VAR system are endogenous; there are no exogenous variables. The stochastic error terms are often called impulses, or innovations or shocks. All variables in the system have equal lags.

VAR must be specified in levels, hence VAR in differences is a mis-specification!

The VAR model is estimated by ordinary least squares (OLS).

Deciding on the maximum lag length, k (an empirical issue). If you use too many lags, you will lose many degrees of freedom, incur statistically insignificant coefficients and multicollinearity. If there are too few lags, there may be specification errors. So, choose optimal lags using the information criterion: AIC, SC, HQIC etc.

Also, the interpretation of the short-run coefficients is as in any other linear model; they are ceteris-paribus effects and inference can be based on the usual OLS standard errors and test statistics.

What are some of the reasons for estimating VAR model? (1) Because there is no cointegration among the variables in the system; (2) to establish causal relationships; (3) to simulate shocks to the system and trace out the effects of shocks on the endogenous variables; (4) for forecasting (decomposing shocks to the VAR system).

[Watch video clip on VAR estimation and discussions]

## (Stata13): VAR and 3-Ways Causality Checks (1) : CrunchEconometrix

So how can you explain causality? A statement such as “J causes Q” will have the following meaning in different scenarios and disciplines such as J leads Q, J is the only cause of Q, J is only one of the possible causes of Q, J must always lead to Q (that is, J determines Q), the occurrence of J makes the occurrence of Q  more probable, J is a probabilistic cause of Q, J must occur either before or simultaneously with Q, but not afterwards, past values of J forecasts future values of Q.

But Regression analysis deals with the dependence of one variable on other variables, it does not necessarily imply causation. In other words, the existence of a relationship between variables does not prove causality or the direction of influence. But in regressions involving time series data, the situation may be somewhat different.

Short-run causal effects: through the F-statistics and the statistical significance of the regressors.

Long-run causal effects: through the statistical significance error-correction term (applicable to VECM only).

Joint causal effects: through the F-statistics and the significance of the independent variables and the statistical significance error-correction term (applicable to VECM only).

Unidirectional causality: occurs from J to Q if the set of estimated coefficients of the lagged J are significantly different from zero and the set of estimated coefficients of lagged Q are not significantly different from zero.

Bi-directional causality: occurs from J to Q if the set of estimated coefficients of the lagged J are significantly different from zero and vice-versa.

Independence: occurs from Q (J) to J (Q) if the set of estimated coefficients of the lagged Q (J) are not significantly different from zero. Using EViews10, this video shows you how to perform causality tests in four different ways within a VAR framework and interpret the results.

[Watch video clip shown below]

## (Stata13): VAR and 3-Ways Causality Checks (2) : CrunchEconometrix

So what is causality? A statement such as “J causes Q” will have the following meaning in different scenarios and disciplines such as J leads Q, J is the only cause of Q, J is only one of the possible causes of Q, J must always lead to Q (that is, J determines Q), the occurrence of J makes the occurrence of Q  more probable, J is a probabilistic cause of Q, J must occur either before or simultaneously with Q, but not afterwards, past values of J forecasts future values of Q.

But Regression analysis deals with the dependence of one variable on other variables, it does not necessarily imply causation. In other words, the existence of a relationship between variables does not prove causality or the direction of influence. But in regressions involving time series data, the situation may be somewhat different.

Short-run causal effects: through the F-statistics and the statistical significance of the regressors.

Long-run causal effects: through the statistical significance error-correction term (applicable to VECM only).

Joint causal effects: through the F-statistics and the significance of the independent variables and the statistical significance error-correction term (applicable to VECM only).

Unidirectional causality: occurs from J to Q if the set of estimated coefficients of the lagged J are significantly different from zero and the set of estimated coefficients of lagged Q are not significantly different from zero.

Bi-directional causality: occurs from J to Q if the set of estimated coefficients of the lagged J are significantly different from zero and vice-versa.

Independence: occurs from Q (J) to J (Q) if the set of estimated coefficients of the lagged Q (J) are not significantly different from zero.

Using EViews10, this video shows you how to perform causality tests in four different ways within a VAR framework and interpret the results.