Noah Smith’s article, The Most Damning Critique of DSGE, questions the lack of use of DSGE models in financial sectors. Smith is perplexed that the financial sector does not utilize DSGE models to forecast economic fluctuations or the effects of government policy for large monetary gains. He claims this siuation to be “currently the most damning critique of the whole DSGE paradigm” (Smith 2014).
Smith critiques the mainstream notion that because agents of the private-sector can not set economic policy, there is no need for Lucas Critique-robust models. Models that satisfy the Lucas Critique and are policy-conditional can be used by firms to make larger returns on their investments.
The blog post includes a simple and brief example of what the advantages of using DSGE models can look like:
“A policy-conditional forecast is when you say “If the Fed tapers, inflation will fall by 0.5% next year.” To get these forecasts as good as possible, you need to know how policy affects the economy. and if your model is not Lucas-robust, then you will not be able to know how policy affects the economy, so you will react sub-optimally to a policy change.”
“For example, suppose the Fed suddenly lowers interest rates substantially. Most people, using their silly spreadsheets with their 70s-vintage Phillips Curves, will forecast a rise in GDP growth, so they will pay a lot for stocks, expecting higher profits from the increased growth. But wise DSGE modelers, using the Nobel-winning and ostensibly Lucas-robust Kydland-Prescott 1982 model, know that the Phillips Curve is not structural. They know that the promised growth will not occur, so as soon as stocks become overpriced, they short the S&P. When the hoped-for growth does not materialize and stocks fall, the DSGE modelers reap a huge profit at the expense of the spreadsheet modelers” (Smith 2014).
I believe that macroeconomic events, such as the 2008 global financial crisis, have dramatic effects on investors. The lack of Lucas-robust DSGE models used in the private sectors is ironic to me, as it seems almost intuitive that these particular models would be used as the basis of most financial decisions.
However, Smith does bring up a valid point in the article, in regards to involving DSGE models more in the finance sector. There are numerous different variations of DSGE models that can be used, so it is difficult for firms to choose the “correct” one. The potential benefits of using DSGE models for forecasting the effects of government policies have been overshadowed by the costs of using them and their downfalls.
Finally, something that resonated with me that Smith said in his blog post is that “being Lucas-robust is necessary for making optimal policy-contingent forecasts, but it is not sufficient. You also need the model to be a good model of the economy. If your parameters are all structural, but you’ve assumed the wrong microfoundations, then your model will make bad predictions even though it’s Lucas-robust.” I found this to be one the most important points made in the post. Throughout the whole semester, I have continuously contemplated many of the foundations in which almost all economic models conduct under. I think that accurate parameters are the necessary driving force of all models, whether macro or micro, but have been taken for granted in the economics community. I have noticed a trend or fixation on answering complex questions, while disregarding “basic” economic principles in the process. A wise man by the name of Voltaire once said “Judge a man by his questions rather than his answers.”
Here is the link to Noah Smith’s (Noahpinon) blog post http://noahpinionblog.blogspot.co.uk/2014/01/the-most-damning-critique-of-dsge.html