# Panel dataset example

You can report issue about the content on this page here Want to share your content on R-bloggers? Since I started work on it well over a year ago, it has become essential to my own workflow and I hope it can be useful for others. It is a modified tibblewhich is itself a modified data. This is what it looks like:. The key columns are id and t. They tell you which respondent and which time point the row refers to, respectively. Panel data: 4, x 14 entities: id [] wave variable: t [1, 2, 3, It also allows other panel data functions in the package to know this information without you having to respecify every time.

Note that the wages data are grouped by id and sorted by t within each id. That means when you want to do things like calculate group means and create lagged variables, everything works correctly. A warning, though: this is only true within mutate and transmute from the dplyr package. Panel data: 4, x 7 entities: id [] wave variable: t [1, 2, 3, It works the same way using base R subsetting:.

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Panel data: 4, x 3 entities: id [] wave variable: t [1, 2, 3, By default, it will provide descriptive statistics for each column in each wave. To shorten the output, you can choose columns using dplyr::select style syntax. You can stop getting per-wave statistics by setting by. For panels with many fewer entities, you might also want per-entity statistics. You can achieve this by setting by. Each line is an individual id in the data. The blue line is the mean trend and we can see that nearly everyone increases over time.

Sometimes it is useful to isolate specific entities from your data. These data come from the Penn World Table and contain data about countries, their exchange rates, purchasing power parity, and related data. It is provided by Stata and discussed in its manual. Panel data: 5, x 10 entities: country [] wave variable: year [, Since there are so many, we will want to look at just a subset.

We can see some heterogeneity in the trends. They are great general tools, but my goal was to make a specific tool to make life easier in this particular situation.

Going from long to wide format is fairly straightforward. As a reminder, it looks like this:. Panel data: 4, x 15 entities: id [] wave variable: t [1, 2, 3, Going from wide to long is a bit more complicated because you need to automate the process of knowing how many waves there are, which variables change over time, and how the time-varying variables are labeled to reflect the time of the measurement.

Panel data: 5, x 9 entities: id [] wave variable: wave [1, 2, 3, See the vignette for more details.The key difference between time series and panel data is that time series focuses on a single individual at multiple time intervals while panel data or longitudinal data focuses on multiple individuals at multiple time intervals.

Consider the following two examples to understand the difference between time series and panel data clearly: profit of an individual over a period of ten years is an example of time series data while profit of set of individuals over a period of ten years is an example for panel data.

Fields such as Econometrics and statistics relies on data. Moreover, it is a significant aspect of research and analysis. Usually, there are various methods to obtain data. Government and private organizations, internet, and international organizations such as IMF and World Bank use several methods to gather data. Further, there are various types of data. This article discusses two of them which are the time series and panel data.

Overview and Key Difference 2. What is Panel Data 4. Time series data focuses on observations of a single individual at different times usually at uniform intervals. The time series data has the form of Xt. The t subscript denotes the time. Stock prices, Gross Domestic Product and automobile sales figures data can take time series form. Panel data is also called longitudinal data. This type of data focuses on multiple individuals at multiple time periods.

The i denotes the individual while t denotes the time period. In this scenario, there is a total of 50 observations. Time series data is a dataset consist of observations of one individual at multiple time intervals. Panel data is a dataset consist of observations of multiple individuals obtained at multiple time intervals. Time series data focuses on single individual while panel data focus on multiple individuals. Looking at the application of both types of data, profit of an individual over a period of ten years is an example of time series data while profit of set of individuals over a period of ten years is an example for panel data.

The difference between time series and panel data is that time series focus on a single individual at multiple time intervals while panel data focus on multiple individuals at multiple time intervals.

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## Python Pandas - Panel

Figure Econometrics. Leave a Reply Cancel reply.In statistics and econometricspanel data and longitudinal data [1] [2] are both multi-dimensional data involving measurements over time.

Panel data is a subset of longitudinal data where observations are for the same subjects each time. Time series and cross-sectional data can be thought of as special cases of panel data that are in one dimension only one panel member or individual for the former, one time point for the latter. A study that uses panel data is called a longitudinal study or panel study.

In the example above, two datasets with a panel structure are shown. Individual characteristics income, age, sex are collected for different persons and different years. In the first dataset, two persons 1, 2 are observed every year for three years, In the second dataset, three persons 1, 2, 3 are observed two times person 1three times person 2and one time person 3respectively, over three years, ; in particular, person 1 is not observed in year and person 3 is not observed in or A balanced panel e.

An unbalanced panel e. Both datasets above are structured in the long formatwhich is where one row holds one observation per time. Another way to structure panel data would be the wide format where one row represents one observational unit for all points in time for the example, the wide format would have only two first example or three second example rows of data with additional columns for each time-varying variable income, age.

Two important models are the fixed effects model and the random effects model. However, panel data methods, such as the fixed effects estimator or alternatively, the first-difference estimator can be used to control for it. This means that more efficient estimation techniques are available. Dynamic panel data describes the case where a lag of the dependent variable is used as regressor:. The presence of the lagged dependent variable violates strict exogeneitythat is, endogeneity may occur.

The fixed effect estimator and the first differences estimator both rely on the assumption of strict exogeneity. Instrumental variables or GMM techniques are commonly used in this situation, such as the Arellanoâ€”Bond estimator. From Wikipedia, the free encyclopedia. This article includes a list of general referencesbut it remains largely unverified because it lacks sufficient corresponding inline citations.

Please help to improve this article by introducing more precise citations. June Learn how and when to remove this template message. Main article: Panel analysis. Main article: Multidimensional panel data.

Analysis of Longitudinal Data 2nd ed. Oxford University Press. Applied Longitudinal Analysis. Categories : Panel data Multivariate time series Statistical data types Mathematical and quantitative methods economics. Hidden categories: Articles lacking in-text citations from June All articles lacking in-text citations Wikipedia articles needing clarification from June Namespaces Article Talk.Login or Register Log in with.

Forums FAQ. Search in titles only. Posts Latest Activity. Page of 2. Filtered by:. Niels Meijer. Panel data: testing for serialcorrelation and heteroskedasticity 16 Sep Hello, I've got a panel data set with banks, with data from with varying degrees of data availability.

On average there is about 8. I've got a dependent variable: Default risk. And several explanatory variables: Board Characteristics for each bank.

**The 3 Kinds of Panel Data: Causal Inference Bootcamp**

Plus various control variables. Now I was wondering how I should go about testing for serialcorrelation and heteroskedasticity. I would like to manually run tests for serial correlation and heteroskedasticity. I've done a Breusch-Godfrey test for serial correlation before but not on a panel dataset, just on time series.

I this also suitable for panel data? And how would I perform this test for panel data? Similarly, I've done a Breusch-Pagan test for heteroskedasticity before, but never on panel data, is this suitable for panel data? Some help would be greatly appreciated, as I am new to panel data analysis. Kind regards, Niels.

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Carlo Lazzaro. Niels: you seem to have a large N, small T panel dataset: hence, assuming a continuous dependent variable that is, a score for default riskI would go -xtreg. You can graphically inspect your residual distribution and see whether a heteroskedasticity-suggestive pattern comes alive.

Comment Post Cancel. Carlo, thank you for taking the time to reply I appreciate it. Your assumptions are indeed correct, default risk is a score. I was wondering if a Breusch-Godfrey and Breusch-Pagan test would be suitable?By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I would like to create a panel from a dataset that has one observation for every given time period such that every unit has a new observation for every time period.

Using the following example:. For each unique ID, I would like there to be a unique observation for the year,and the lower and upper time periods on this frameand set the outcome y to 0 for all the times in which there isn't an existing observation, such that the new frame looks like:. Then using the reshape2 package cast frame from long to wide form and then melt it back to long form. Finally rearrange the rows and columns as desired.

The lines ending in one are only to ensure that every year is present so if we knew that were the case those lines could be omitted. The line ending in is only to rearrange the rows and columns so if that did not matter that line could be omitted too. This solution uses no addon packages. This gives the same answer as solution 1 except as noted by a commenter solution 1 above makes id a factor whereas it is not in this solution. Learn more. Create a panel data frame Ask Question.

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### What Is Panel Data?

Active Oldest Votes. Grothendieck G. Grothendieck k 14 14 gold badges silver badges bronze badges. Any particular reason for reshaping here? Wouldn't this suffice? Not if you want to be able to omit the computation of g if it were known that all years were present.

Grothendieck Jan 8 '14 at I don't think I get it, sorry. Where do you eliminate computation of g in your case when it's known all years are present? In the first solution just omit the lines that end in as stated in the answser and you will see it still gives the same result provided all years are present in the input frame. Grothendieck Jan 9 '14 at Note that id will be a factor here. Using data. Arun Arun k 20 20 gold badges silver badges bronze badges. Sign up or log in Sign up using Google.

Sign up using Facebook. Sign up using Email and Password.Panel data, also known as longitudinal data or cross-sectional time series data in some special cases, is data that is derived from a usually small number of observations over time on a usually large number of cross-sectional units like individuals, households, firms, or governments.

In the disciplines of econometrics and statisticspanel data refers to multi-dimensional data that generally involves measurements over some period of time. As such, panel data consists of researcher's observations of numerous phenomena that were collected over several time periods for the same group of units or entities.

For example, a panel data set may be one that follows a given sample of individuals over time and records observations or information on each individual in the sample. The following are very basic examples of two panel data sets for two to three individuals over the course of several years in which the data collected or observed includes income, age, and sex:.

Panel Data Set A.

Panel Data Set B. Both Panel Data Set A and Panel Data Set B above show the data collected the characteristics of income, age, and sex over the course of several years for different people. Panel Data Set A shows the data collected for two people person 1 and person 2 over the course of three years, and Characteristics of person 1 and person 2 were collected in andbut person 3 is only observed innot and There are two distinct sets of information that can be derived from cross-sectional time series data.

The cross-sectional component of the data set reflects the differences observed between the individual subjects or entities whereas the time series component which reflects the differences observed for one subject over time. It is panel data regression methods that permit economists to use these various sets of information provided by panel data. As such, analysis of panel data can become extremely complex.

But this flexibility is precisely the advantage of panel data sets for economic research as opposed to conventional cross-sectional or time series data. Panel data gives researchers a large number of unique data points, which increases the researcher's degree of freedom to explore explanatory variables and relationships.

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Sometimes panel data is also called longitudinal data as it adds a temporal dimension to cross-sectional data. Let us have a look at the dataset Fatalities by checking its structure and listing the first few observations. We find that the dataset consists of observations on 34 variables.

Notice that the variable state is a factor variable with 48 levels one for each of the 48 contiguous federal states of the U. The variable year is also a factor variable that has 7 levels identifying the time period when the observation was made. Since all variables are observed for all entities and over all time periods, the panel is balanced.

If there were missing data for at least one entity in at least one time period we would call the panel unbalanced. We start by reproducing Figure To this end we estimate simple regressions using data for years and that model the relationship between beer tax adjusted for dollars and the traffic fatality rate, measured as the number of fatalities per inhabitants.

Afterwards, we plot the data and add the corresponding estimated regression functions. In both plots, each point represents observations of beer tax and fatality rate for a given state in the respective year. The regression results indicate a positive relationship between the beer tax and the fatality rate for both years. The estimated coefficient on beer tax for the data is almost three times as large as for the dataset. This is contrary to our expectations: alcohol taxes are supposed to lower the rate of traffic fatalities.

As we known from Chapter 6this is possibly due to omitted variable bias, since both models do not include any covariates, e. This could be corrected for using a multiple regression approach. However, this cannot account for omitted unobservable factors that differ from state to state but can be assumed to be constant over the observation span, e.

As shown in the next section, panel data allow us to hold such factors constant. Preface 1 Introduction 1. Computation of Heteroskedasticity-Robust Standard Errors 5. Part I Introduction to Econometrics with R. This book is in Open Review.

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