Lecture notes
Economic Data Analysis Lecture Notes
- Module
- Economic Data Analysis
- Institution
- The University Of York (UOY)
Economic Data Analysis Lecture Notes 20/21
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Economic Data Analysis1. Introducing Economic Data
1.2 Counting, measuring and collecting data
The rst issues of data analysis start with these three questions:
• How to de ne the variable of interest
• How to measure it
• How to collect it
Figure 1: annual average temperature anomaly in the Northern Hemisphere (1880-2019)
Example: measuring climate change
• What do we want to measure? A change in the variation of temperature over time
• How is it measured? As a yearly average temperature anomaly and we compare this with other
average yearly temperatures in the period
• This method allows for accuracy and comparability
Sampling is the process used in statistical analysis in which a predetermined number of
observations are taken from a larger population. A random sample is where each member of a
population is equally likely to be selected, whereas a sample can be a non-random sample by
design, self-selection or attrition.
Sample attrition is a problem that arises in longitudinal datasets (most have a time dimension and
some follow the same people over time). If the population respond one year but do not respond
the next year, the number of observations decreases; this is sample attrition. This may lead to
sample errors and we also don't know why individuals did not respond.
A sample error is the di erence between a sample statistic used to estimate a population
parameter and the actual but unknown value of the parameter. It can occur even when the sample
is taken randomly, especially for a small sample.
Sample bias is when a sample is collected in such a way that some members of the intended
population are less likely to be included than others; it occurs when the sample is not taken
randomly.
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, 1.3 Making sense of data
Figure 2:
A correlation is a statistical measure that indicates the extent to which two or more variables
uctuate together (covariance, correlation coe cient).
A causal e ect is when one variable (independent variable) causes another variable (dependent
variable) to change. We need to estimate causal e ects to test hypotheses, make predictions and
recommendations.
Dependent variable = β independent variable
Wage = β education
Health = β income
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, A spurious correlation is a mathematical relationship in which two or more variables are
associated but not causally related. It can be due to the fact that number just go up and down,
the selection, an omitted variable or reverse causality.
An omitted variable is a variable, left out of a study, that would explain why the two variables in
the study are correlated. Reverse causality occurs when we mix up the direction of cause and
e ect.
Dependent variable = β independent variable
Wage = β education - skills and abilities?
Health = β income - the other way around?
Pure causal e ect cannot be identi ed because the counterfactual (what has not happened or is
not the case) can never be observed - often concentrate on the average causal/treatment e ect
(ATE).
The correlation coe cient for this climate change data is 0.83 which is accurate considering he
evidence of the relationship between rising CO2 levels and rising temperatures. However, we still
have to consider possible errors with sample selection, omitted variables and reverse causality.
Extrapolation is the process of estimating the value beyond the distinct range of the given
variable, based on its relationship with another variable. Assume that the “trend will continue”.
Internal validity can be used to demonstrate a causal relationship within the context of a study,
whereas external validity can be used to generalise the ndings of a study to other situations.
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, Economic forecasting is the process of making predictions about the economy: at an aggregated
level (GDP, in ation, exchange rates) and at a micro level ( rms’ results, individuals behaviour).
The internal and external validity will depend on the models and assumptions, data quality and
interpretation.
2. Finding economic data
2.1 Dataset design
The 3 most common types of datasets:
• Time series
• Cross section
• Panel
The type of dataset doesn't depend on the nature of the data. It mainly depends on the design
and how observation relate to the two dimensions, time and subject.
Cross section (or cross-sectional data) is a type of data collected by observing many subjects
(individuals, rms, products, countries… ) at one time, or period of time.
Time series is a series of data points indexed in time order. One subject (temperature, nancial
index, GDP… ) is observed at di erent and usually regular (annually, quarterly, daily) moments of
time
Longitudinal data is multidimensional data involving measurements over time. Repeated cross-
sections are repeated in the same population but not necessarily with the same individuals over
di erent time periods. Panel data are the same subjects followed overtime (individuals, rms,
products, countries… ). Cohort data are when a group of people who share de nition
characteristics (born or graduated the same year.. ) are followed over time at regular intervals.
2.2 Multiple sources of economic data
Experimental data is data produced by a controlled method of investigating causal relationships
among variables. Researchers create a control group by randomisation, where they are assigned
by chance rather than by choice. The independent variable (or treatment) is under the control of
the researcher:
• The treatment group receives the treatment, i.e. is a ected by the independent variable
• The control group does not receive the treatment, i.e. is not a ected by the independent
variable.
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