Structural Econometric Modeling: Rationales and Examples from Industrial Organization

By Peter C. Reiss, F. Wolak. 2004

356 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/52eee16318b9477b96622111da57bd0470f21670

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Citations in the corpus

Understanding the paradigm shift to computational social science in the presence of big data (2014, unknown): “Reiss and Wolak[74,p.4281]havepointedoutthatwhen “ thereislittleeconomictheoryonwhich tobuild,theempiricistmayinsteadprefertousenonstructuralordescriptiveeconometric models.”
Bank Credit Cycles (2008, unknown): “See Reiss and Wolak (2007) and Bresnahan (1989) for surveys of the literature.”
Estimating the Benefits of Targeted R&D Subsidies (2013, methodology): “Following the standard methods of structural industrial organization (see Reiss & Wolak, 2007, for a survey), we identify a number of objects by combining theory, functional form, and distributional assumption.”
Pass-Through of Emissions Costs in Electricity Markets (2014, unknown): “More generally, the dependence of mark-up estimates on the assumed functional forms has been acknowledged by the empirical IO literature (see e.g. Bresnahan (1982, 1989), Reiss and Wolak (2007), and Kim and Knittel (2006), among others).”
Estimation of Random-Coefficient Demand Models: Two Empiricists' Perspective (2014, unknown): “This is very often the case in the so-called structural approach, which attempts to infer relationships between observable endogenous variables, and observable explanatory or unobservable variables based on optimizing behavior dictated by economic theory (Reiss & Wolak, 2007).”
A New Data Set On Competition In National Banking Markets (2015, unknown): “In their survey paper, Reiss and Wolak (2007) are very skeptical about using a specific”
A New Way to Measure Competition (2004, methodology): “The other way to estimate price cost margins is to use a structural approach (see Reiss and Wolak (2002) for a survey).”
On the Use of Instrumental Variables in Accounting Research (2010, background): “Similarly, Reiss and Wolak (2001) discuss the “magic” of finding an instrumental variable and cynically suggest that the best instrumental variable is developed by simply adding random error to the endogenous variable (which will be correlated with the original variable by construction).”