Abstract
The ceteris paribus condition in economic theory assumes that the world outside the environment described by the theoretical model does not change, so that it has no impact on the economic phenomena under review. In this paper, we examine the econometric consequences of the ceteris paribus assumption by introducing a 'state of the world' variable into a well specified stochastic economic theory, and we show that the difference between the conditional distribution implied by the theoretical model and the actual conditional distribution of the data is due to different ways of conditioning on the state of the world. We allow the 'state of the world' variable to be, alternatively and equivalently, an index variable representing omitted variables, or a discrete random parameter representing a sequence of models. We construct a probability that can be interpreted as the upperbound of the probability that the ceteris paribus condition is correct. The estimated upperbound can in turn be interpreted as a measure of the information about the data-generating process that is provided by a theoretical model which is constrained by a set of ceteris paribus assumptions. In order to illustrate our findings from both a theoretical and an empirical perspective, we examine a linearized version of the real business cycle model proposed by King Plosser, and Rebello (1988b. Journal of Monetary Economics 21, 309-341).
| Original language | American English |
|---|---|
| Pages (from-to) | 223-253 |
| Number of pages | 31 |
| Journal | Journal of Econometrics |
| Volume | 95 |
| Issue number | 2 |
| DOIs | |
| State | Published - Apr 2000 |
| Externally published | Yes |
ASJC Scopus subject areas
- Economics and Econometrics
- Applied Mathematics
Keywords
- Bayesian prior
- Ceteris paribus
- Fit
- Information measure
- Missing variables
- Real business cycle models
- Reality bound
- Stochastic general equilibrium models