Abstract
Econometric identification is essential to distinguish cause, effect and correlation in econometric studies. This chapter discusses some of the most common econometric techniques used in economic history today, including a series of examples, areas of application, advantages and caveats. Techniques discussed include ordinary least squares regression, time series analysis, regression discontinuity designs, placebo regressions and instrumental variable approaches.
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Notes
- 1.
This test can be run in Stata by downloading the command “psacalc”. Oster (2017) also considers it important to look at movements in the R2, given that some controls may not explain a lot of the variance in the dependent variable, and therefore are unlikely to have an impact on the coefficient of interest.
- 2.
The most important test in a difference-in-differences approach is to evaluate whether the treated and the untreated group follows a common trend.
- 3.
External validity is often considered more difficult to comply with since the empirical test must be applicable to the general population. Obviously, large and representative samples should be given preference . Ideally, any statistical test is applied to the full population. That said, in econometric day-to-day operations, often a trade-off between internal and external validity is observed. The harder we try to isolate an effect from confounds, the more unlikely it is that the results can be generalised since the outside world is complex, and there are many effects interacting with each other.
- 4.
See Morck and Yeung (2011) for a broader discussion of identification in historical settings.
- 5.
Also see Albouy (2012), who argues that the lack of accuracy of historical mortality rates and other data uncertainties caused a weak instruments problem.
- 6.
See Colvin and McCracken (2017) for a recent example of a successful replication study, and the Replication Wiki, hosted by the University of Göttingen, for advice on how to start a replication project (http://replication.uni-goettingen.de).
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Blum, M., Dimico, A. (2018). Econometric Identification. In: Blum, M., Colvin, C. (eds) An Economist’s Guide to Economic History. Palgrave Studies in Economic History. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-96568-0_45
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DOI: https://doi.org/10.1007/978-3-319-96568-0_45
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