Time series data, cross-section data, and panel data are all viable options for economics studies.

Time Series Data

This information was gathered at predetermined intervals. It's data that records the values of a variable across time. Daily stock prices, weekly money supply numbers, monthly consumer price indexes, quarterly GDP, yearly government budgets, biennial government spending reports, etc. are all examples of data that may be gathered at regular time intervals. Stock price fluctuations are only one of many occurrences that may be measured by financial data. More frequent collection intervals (daily or even hourly) are used for this data compared to the examples given above. All of these instances are what is known as time series data since the information is presented in chronological sequence.
Notationally, in economics, an observation on variable Y (say, real GDP) at time t is written as Yt. From time t = 1 to t = T, we have a data series. The length of a data collection, denoted by "T," is the total number of time intervals it includes. If we utilize yearly real GDP data from 1980-2017 (a span of 38 years), then t = 1 would represent 1980, t = 38 would represent 2017, and T = 38 would show the total number of years. So, 1980's real GDP would be Y1, 1981's real GDP would be Y2, etc. Typically, time series data is shown in a linear fashion. Information, such as stock price data, may now be retrieved in a manner that is essentially real-time thanks to the development of powerful computers (the so-called real-time quote).
Even though econometricians rely largely on time series data, they pose unique challenges. A common assumption of empirical work with time series data is that the underlying time series is stationary, yet this is not the case for the vast majority of time series data. If the mean and variance of a time series do not change in a predictable way over time, we say that the series is stationary.

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Cross-Section Data

Data on one or more variables gathered all at once constitute cross-sectional data. One frequent kind of population data is pay data for an entire firm or industry, collected via a census. The value of Y for subject I is denoted as Yi. The subjects in a cross-sectional study range from I = 1 to N. Traditional use assigns the value N to the total number of cross-sectional areas (e.g. the number of people surveyed). An N = 100 sample of manufacturing company managers might be polled on their company's monthly earnings for illustrative purposes by a microeconomist. Here, Y1 is the profit recorded by the first firm, Y2 by the second, and so on up to Y100 by the hundredth. In most cases, the order of the data is irrelevant when dealing with cross-sectional data (unlike time series data). The problem of heterogeneity in cross-sectional data is similar to the stationarity issue in time series data.
Panel Data

A time series and a cross-sectional sample may be included in a single data collection. Panel data describes these types of information. Economists studying growth difficulties often use panel data. For example, GDP data for a large number of nations is accessible from 1980 forward. The GDP of each African nation in 1980 (N = 12 observations), 1951 (N = 12 observations), and so on would make up a panel data set on Y = GDP for those 12 countries. Given a time horizon of T years, there would be TN measurements of Y. When we make a note of the value of variable Y for some given unit I at some given time t, we write this as Yit. For the sake of this illustration, let's assume that GDP in Year 1 of Country 1 is Y11, GDP in Year 2 of Country 1 is Y12, etc. When the same cross-sectional (such as a family or business) is polled repeatedly over time, the results are considered longitudinal or micro panel data, a specific kind of pooled data.

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