Jason Smith today in a blog post:
$$y = f(x,z,...)$$
where $y$ is the effect, and $x,z,...$ are the causes. How do you go about determining whether or not $x$ causes $y$? You create a closed system in which only $x$ and $y$ can change and then change $x$. If you then observe $y$, you may then (and only then) conclude that $x$ causes $y$. If, however, you cannot create a closed system, you cannot conclude whether or not your hypothesis that $x$ causes $y$ is true.
Of course, this is all really basic, but what does it have to do with New Keynesian economics (or, more generally, mainstream economics). I'll start with a general example. Consider the minimum wage. Basic partial equilibrium analysis suggests that increasing minimum wage will cause an increase in unemployment, but empirical research seems to not confirm this prediction. Case closed, Econ 101 is wrong and Info Econ 101 is right!
No. The only way for any empirical research to invalidate the partial equilibrium analysis with the minimum wage is if it were done in a closed system; i.e., the only two variables were employment and the minimum wage, which has not been the case in any study that I have seen referenced. In this sense, the causal relationship between the minimum wage and employment has not been falsified -- the data have only proven that the partial equilibrium model of labor markets is incomplete (i.e., it doesn't capture all of the causes of changes in unemployment), which we all already knew.
This same problem is captured by the failure of NK DSGE models to explain the Great Recession; the only conclusion that can be drawn from the empirical evidence is that the model is incomplete, which everyone already knew anyway, and therefore failed to capture all the possible causes of the Great Recession -- this has no bearing on the correctness of the rest of the model.
Since it is obvious that closed systems can't be dealt with very often in social science, especially if the hypothesis being tested (e.g., NK DSGE) is rather complex, it should be obvious that empirical evidence is not very useful for social science. That said, the appropriate conclusion is probably that all economic models are doomed to be quantitatively unsuccessful (and even if they are, that is not necessarily an indication that the model in question is correct in the sense that it correctly matches causes with effects), even if they do capture some of the correct cause and effect relationships, and we should only judge models based on their qualitative predictions, not their quantitative ones.
New Keynesian economics = Ignore empirical data
...
Information transfer economics = Use empirical dataI'd like to venture to explain why exactly this might be true. Start with basic Humean epistemology in which knowledge is the relationship between cause and effect and, for a given effect, there might be multiple causes. This can be expressed as
$$y = f(x,z,...)$$
where $y$ is the effect, and $x,z,...$ are the causes. How do you go about determining whether or not $x$ causes $y$? You create a closed system in which only $x$ and $y$ can change and then change $x$. If you then observe $y$, you may then (and only then) conclude that $x$ causes $y$. If, however, you cannot create a closed system, you cannot conclude whether or not your hypothesis that $x$ causes $y$ is true.
Of course, this is all really basic, but what does it have to do with New Keynesian economics (or, more generally, mainstream economics). I'll start with a general example. Consider the minimum wage. Basic partial equilibrium analysis suggests that increasing minimum wage will cause an increase in unemployment, but empirical research seems to not confirm this prediction. Case closed, Econ 101 is wrong and Info Econ 101 is right!
No. The only way for any empirical research to invalidate the partial equilibrium analysis with the minimum wage is if it were done in a closed system; i.e., the only two variables were employment and the minimum wage, which has not been the case in any study that I have seen referenced. In this sense, the causal relationship between the minimum wage and employment has not been falsified -- the data have only proven that the partial equilibrium model of labor markets is incomplete (i.e., it doesn't capture all of the causes of changes in unemployment), which we all already knew.
This same problem is captured by the failure of NK DSGE models to explain the Great Recession; the only conclusion that can be drawn from the empirical evidence is that the model is incomplete, which everyone already knew anyway, and therefore failed to capture all the possible causes of the Great Recession -- this has no bearing on the correctness of the rest of the model.
Since it is obvious that closed systems can't be dealt with very often in social science, especially if the hypothesis being tested (e.g., NK DSGE) is rather complex, it should be obvious that empirical evidence is not very useful for social science. That said, the appropriate conclusion is probably that all economic models are doomed to be quantitatively unsuccessful (and even if they are, that is not necessarily an indication that the model in question is correct in the sense that it correctly matches causes with effects), even if they do capture some of the correct cause and effect relationships, and we should only judge models based on their qualitative predictions, not their quantitative ones.
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