The Nature of Econometrics and Economic Data

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Presentation transcript:

The Nature of Econometrics and Economic Data What is Econometrics? Copyright © 2007 Thomson Asia Pte. Ltd. All rights reserved.

Why study Econometrics? Rare in economics (and many other areas without labs!) to have experimental data Need to use nonexperimental, or observational, data to make inferences Important to be able to apply economic theory to real world data Copyright © 2007 Thomson Asia Pte. Ltd. All rights reserved.

Why study Econometrics? An empirical analysis uses data to test a theory or to estimate a relationship A formal economic model can be tested Theory may be ambiguous as to the effect of some policy change – can use econometrics to evaluate the program Copyright © 2007 Thomson Asia Pte. Ltd. All rights reserved.

Steps in Empirical Economic Analysis Construct a formal Economic Model Turn it into an Econometric Model Copyright © 2007 Thomson Asia Pte. Ltd. All rights reserved.

Types of Data – Cross Sectional Cross-sectional data is a random sample Each observation is a new individual, firm, etc. with information at a point in time If the data is not a random sample, we have a sample-selection problem Copyright © 2007 Thomson Asia Pte. Ltd. All rights reserved.

Types of Data – Pooled Cross Sections and Panel Can pool random cross sections and treat similar to a normal cross section. Will just need to account for time differences. Can follow the same random individual observations over time – known as panel data or longitudinal data Copyright © 2007 Thomson Asia Pte. Ltd. All rights reserved.

Types of Data – Time Series Time series data has a separate observation for each time period – e.g. stock prices Since not a random sample, different problems to consider Trends and seasonality will be important Copyright © 2007 Thomson Asia Pte. Ltd. All rights reserved.

The Question of Causality Simply establishing a relationship between variables is rarely sufficient Want to the effect to be considered causal If we’ve truly controlled for enough other variables, then the estimated ceteris paribus effect can often be considered to be causal Can be difficult to establish causality Copyright © 2007 Thomson Asia Pte. Ltd. All rights reserved.

Example: Returns to Education A model of human capital investment implies getting more education should lead to higher earnings In the simplest case, this implies an equation like Copyright © 2007 Thomson Asia Pte. Ltd. All rights reserved.

Example: (continued) The estimate of b1, is the return to education, but can it be considered causal? While the error term, u, includes other factors affecting earnings, want to control for as much as possible Some things are still unobserved, which can be problematic Use nlsy.dta to estimate a simple earnings function Copyright © 2007 Thomson Asia Pte. Ltd. All rights reserved.