Econometrics in absence of data would not exist. All data can be classified into a category and that can be important as the success of good econometric work depends on the nature, sources and limitations of the data used. Econometrics is a branch of economics that make use of mathematical approach to data.
Types of Data[edit | edit source]
There are three types of data: time series, cross-section, and a combination of them is called pooled data.
Time series data of a variable have a set of observations on values at different points of time. They are usually collected at fixed intervals, such as daily, weekly, monthly, annually, quarterly, etc. Time series econometrics has applications in macroeconomics, but mainly in financial economics where it is used for price analysis of stocks, derivatives, currencies, etc.
Cross-section data are collected at the same point of time for several individuals. Examples are opinion polls, income distribution, data on GNP per capita in all European countries, etc.
Pooled data is a mixture of time series data and cross-section data. One example is GNP per capita of all European countries over ten years.
Panel, longitudinal or micropanel data is a type that is pooled data of nature. The difference is that we measure over the same cross-sectional unit for individuals, households, firms, etc. This branch of econometrics is called microeconometrics.
Sources of Data[edit | edit source]
There are many sources of data and it can be very time-consuming to find all the data needed. In fact, finding data can take up more of the time than analysis in a project. Some sources are governmental agencies (Eurostat), international agencies (the International Monetary fund (IMF), the World Bank, the World Health Organisation (WHO), etc.), firms, etc.
The Internet has become the newest source of information over the last decade. There are lots of economic and financial data to obtain.
Data Accuracy[edit | edit source]
Because data in the social sciences are seldom generated under controlled conditions, there will always be unknown influences. This makes it difficult to obtain qualitative data for research. This was mentioned in the previous section.
Measurement of Scale Data fall into four categories which are important to know:
- Ratio scale refers to quantities such as ratios and distances . There can be ordering of the data where comparisons are meaningful, such as . Basically, this can be measure with a parametric approach to statistic.
- Interval scale refers to distances as mentioned above, it can also be measure with a parametric approach to statisticss.
- Ordinal scale refers to an order that is not quantitative but qualitative. We can also say that there is a "natural order" of grouping different categories. For example, there are different income classes (high, medium, low), sizes (large, medium, small), etc. An ordinal scale can be measure with both parametric and non-parametric statistics.
- Nominal scale refers to states but there is no ordering amongst them. For instance, genders (male, female), materials (paper, plastics, wood), etc. Interval scale can only be measure with non-parametric approach to statistics.
Notes[edit | edit source]