Review of Difference-in-Difference Analyses in Social Sciences: Application in Policy Test Research

William H. Greene and Min (Shirley) Liu

Chapter 124 in Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning:(In 4 Volumes), 2020, pp 4255-4280 from World Scientific Publishing Co. Pte. Ltd.

Abstract: In this chapter, we review the difference-in-difference (DID) method and first-difference method, which have been widely used in quantitative research designs in the social sciences (e.g., economics, finance, accounting, etc.). First, we define the DID and first-difference methods. Then, we explain the models that may be used in the DID and first-difference methods and briefly discuss the critical assumptions when researchers make a casual inference from the results. Next, we use some examples documented in previous studies to illustrate how to apply the DID and first-difference methods in the research related to policy implementations. Finally, we compare the DID method to the comparative interrupted time series (CITS) design and briefly introduce two popular methods that researchers have used to create a control sample in order to reduce sample selection bias in a quasi-experimental design: propensity score matching (PSM) and regression discontinuity design (RDD).

Keywords: Financial Econometrics; Financial Mathematics; Financial Statistics; Financial Technology; Machine Learning; Covariance Regression; Cluster Effect; Option Bound; Dynamic Capital Budgeting; Big Data (search for similar items in EconPapers)
JEL-codes: C01 C1 G32 (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (5)

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