The analysis of data-sets at a fixed point in time Show
What is Cross-Sectional Data Analysis?Cross-sectional data analysis is when you analyze a data set at a fixed point in time. Surveys and government records are some common sources of cross-sectional data. The datasets record observations of multiple variables at a particular point in time. Financial analysts may, for example, want to compare the financial position of two companies at a specific point in time. To do so, they would compare the two companies’ balance sheets. Below are Amazon’s and Apple’s End of Year Consolidated Balance Sheets. An analyst could use them to look at their 2018 financial position. However, the slight difference in reporting period ending dateas could necessitate making a few adjustments. CFI’s Advanced Financial Modeling & Valuation Course includes an extensive case study on Amazon. Examples of cross-sectional datasets include:
Uses of Cross-Sectional DataCross-sectional datasets are used extensively in economics and other social sciences. Applied microeconomics uses cross-sectional datasets to analyze labor markets, public finance, industrial organization theory, and health economics. Political scientists use cross-sectional data to analyze demography and electoral campaigns. Financial Analysts will typically compare the financial statements of two companies, a cross sectional analysis would be to compare the statements of two companies at the same point in time. Contrast that to time-series data analysis, which would compare the financial statements of the same company across multiple time periods. Sources of Cross-Sectional Data
Random SamplingRandom sampling framework is a statistical framework that is widely used in data analysis. The random sampling method works under the assumption that there exists a close link between the population and a sample taken from that population. Consider the example of orange consumption by Ghanaian households described above. It would take a lot of resources (both time and money) to measure the actual orange consumption of every household in Ghana. It would be much cheaper to only measure the orange consumption of 1,000 households in Ghana. In such a case, the population consists of every household in Ghana, and the sample consists of the 1,000 households whose orange consumption data is known. Econometric analysis of cross-sectional data sets usually assumes that the data is independently generated and that the observations are mutually independent. Such an assumption of independently generated data is violated when the economic unit of analysis is large, relative to the population. Suppose we want to analyze the GDP of all countries in North America. Our population, in this case, consists of 23 countries. Any sample we construct from the population can’t possibly support the construction of a mutually independent random sample. For example, it is extremely likely that the GDP of the United States is correlated with the GDP of Canada. Random Sample in Cross-Sectional Data AnalysisConsider a cross-sectional dataset that measures K characteristics for N different economic entities at time t. An individual observation in the cross-sectional dataset is of the form: Where:
The cross-sectional dataset was created using a random sample drawn from the population (F, X, t), where F is the joint distribution of all (U,X) in the population at time t. Additional ResourcesThank you for reading CFI’s guide to Cross-Sectional Data Analysis. To keep learning and advancing your career, the following CFI resources will be helpful:
Is a data on one or more variables collected at the same point in time?Cross-sectional data: Data of one or more variables, collected at the same point in time. Pooled data: A combination of time series data and cross-sectional data.
What is data recorded at the same point in time called?Time series data, also referred to as time-stamped data, is a sequence of data points indexed in time order. These data points typically consist of successive measurements made from the same source over a fixed time interval and are used to track change over time.
When we collect data on two variables the data is known as DASH data?In statistics, bivariate data is data on each of two variables, where each value of one of the variables is paired with a value of the other variable.
When data are collected at one point in time from different types of people this is called a?Cross-sectional data, or a cross section of a study population, in statistics and econometrics, is a type of data collected by observing many subjects (such as individuals, firms, countries, or regions) at the one point or period of time.
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