Noah Smith’s Conclusion on DSGE Models

In my last blog post I summarized and reflected on Noah Smith’s article The Most Damning Critique of DSGE. The blog post expanded on Smith’s opinion of utilizing DSGE models in the financial sector but I neglected to note one of the most important points made. Noah claims that 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, the main one being that they satisfy the Lucas-critique, have been overshadowed by the costs of using them or determining the most appropriate one to use.

Smith determines that the lack of use of  DSGE models, specifically Lucas-robust  DSGE models, in finance markets are an indicator that “DSGE fail  the market test” (Smith 2014). This means that financial modelers who would benefit directly from superior market returns uniformly do not use DSGE models, thus strongly suggesting that DSGE models are not useful for macroeconomic predictions. I found this extrapolation to be interesting, but not necessarily supported firmly in the article.

Noah Smith’s blog post can be accessed through the following link http://noahpinionblog.blogspot.co.uk/2014/01/the-most-damning-critique-of-dsge.html

RE: The Most Damning Critique of DSGE

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

Roger Farmer’s Main Points

Hey everyone I hope you all had a nice spring break! Real quick here are some notes that I took on Roger Farmer’s Prosperity for All book. There is a chance I might have missed some important ideas and topics but I wanted to keep it relatively concise so that it would fit in a blog post. Because we have presented and learned about the the major schools of thoughts, I figured I would write this post as a refresher and to serve as a reference for when we contrast what Farmer has proposed.

Roger Farmer’s Prosperity for All: How to Prevent a Financial Crises

Farmer’s 5 Main Points:

  1. Stock market and financial assets are important for economic growth
  2. Stock market fluctuations influence the unemployment rate
  3. Central Bank and Fed should target the inflation rate under monetary policy

Government should target the unemployment rate under fiscal/financial policy

  1. Unemployment can be permanently above natural rate
  2. Self-fulfilling prophecies and expectations are important

Confidence in markets are key to market efficiency

“Animal Spirits” influence markets

  • Central Bank should buy & sell shares each month depending on the unemployment rate (Farmer, Page 18)
  • Great Recession was a result of a deviation in the Natural Rate of Unemployment

Dismisses the Phillips Curve and Natural Rate Hypothesis

  • Neoclassical + Keynesian Economics = basis of Roger Farmer’s model and theories
  • Economics should be changed over time

With the introduction of new data and random anomalies comes the response of fixing models and modifying theories

  1. There is a continuum of possible equilibrium unemployment rates
  2. The unemployment rate that prevails is determined by the “Animal Spirits” of investors

The stock market influences the unemployment rate and expectations influence decisions

  • Natural Rate of Unemployment: the sum of structural and frictional unemployment is referred Is the average level of unemployment that is expected to prevail in an equilibrium economy, with the absence of cyclical unemployment.

Dismisses wage and price stickiness in terms of prevailing unemployment

  • Aggregate Demand depends on wealth not income
  • Keynes Search Theory + Belief Function = Explanation for GDP, Price Level, and Employment
  • Beliefs should be fundamental in models
  • Deviations in “Animal Spirits” cause recessions and business-cycles

Restore confidence in consumers and investors through government asset purchases to prevent market crashes causing recessions

US government can buy & sell shares in the stock market, which are paid by issuing short-term Treasury securities, to cushion economic crises.

  • Notes that DSGE models are partially wrong and should be modified rather than fully dismissed out of economics
  • Proposes the US should invest in domestic infrastructure

Investment in infrastructure will help lower the unemployment rate and stimulate economic growth