Econometrics Flashcards
Causal Effect
In most tests of economic theory, and certainly for evaluating public policy, the economist’s goal is to infer that one variable (such as education) has a causal effect on another variable (such as worker productivity).
Counterfactual Reasoning
The notion of ceteris paribus also can be described through counterfactual reasoning, which has become an organizing theme in analyzing various interventions, such as policy changes. The idea is to imagine an economic unit, such as an individual or a firm, in two or more different states of the world. For example, consider studying the impact of a job training program on workers’ earnings. For each worker in the relevant population, we can imagine what his or her subsequent earnings would be under two states of the world: having participated in the job training program and having not participated.
ceteris paribus
The notion of ceteris paribus—which means “other (relevant) factors being equal”—plays an important role in causal analysis.
Counterfactual Outcomes
By considering these counterfactual outcomes (also called potential outcomes), we easily “hold other factors fixed” because the counterfactual thought experiment applies to each individual separately.
Cross-sectional Data
A cross-sectional data set consists of a sample of individuals, households, firms, cities, states, countries, or a variety of other units, taken at a given point in time.
Data Frequency
Another feature of time series data that can require special attention is the data frequency at which the data are collected. In economics, the most common frequencies are daily, weekly, monthly, quarterly, and annually.
Economic Modeling
Formal economic modeling is sometimes the starting point for empirical analysis, but it is more common to use economic theory less formally, or even to rely entirely on intuition.
Econometric Model
The form of the function f(.) must be specified before we can undertake an econometric analysis. A second issue is how to deal with variables that cannot reasonablyc4 be observed.
Empirical Analysis
When econometric methods are used to analyze time series data, the data should be stored in chronological order.
Experimental Data
Experimental data are often collected in laboratory environments in the natural sciences, but
they are more difficult to obtain in the social sciences.
Nonexperimental Data
Even though experimental data cannot be obtained for measuring the return to education, we can
certainly collect nonexperimental data on education levels and wages for a large group by sampling
randomly from the population of working people.
Observational Data
(Nonexperimental data are sometimes called observational data, or
retrospective data, to emphasize the fact that the researcher is a passive collector of the data.)
Panel Data
A panel data (or longitudinal data) set consists of a time series for each cross-sectional member in the data set. As an example, suppose we have wage, education, and employment history for a set
of individuals followed over a 10-year period. Or we might collect information, such as investment and financial data, about the same set of firms over a five-year time period. Panel data can also be collected on geographical units. For example, we can collect data for the same set of counties in the United States on immigration flows, tax rates, wage rates, government expenditures, and so on, for
the years 1980, 1985, and 1990.
Pooled Cross Section
Some data sets have both cross-sectional and time series features. For example, suppose that two cross-sectional household surveys are taken in the United States, one in 1985 and one in 1990. In 1985, a random sample of households is surveyed for variables such as income, savings, family
size, and so on.
Random Sampling
An important feature of cross-sectional data is that we can often assume that they have been obtained by random sampling from the underlying population.
