Rogoff’s three challenges for macroeconomic research

In this white paper, Rogoff points to three challenges for macroeconomic researchers:

There are three great challenges facing researchers in modern macroeconomics today, all brought into sharp relief by the recent financial crisis. The first is to find more realistic, and yet tractable, ways to incorporate financial market frictions into our canonical models for analyzing monetary policy. The second is to rethink the role of countercyclical fiscal policy, particularly in the response to a financial crisis where credit markets seize. A third great challenge is to achieve a better cost-benefit analysis of financial market regulation.

All this is discussed elsewhere as well. Towards the end, he defends the mathematical approach of economics:

My basic contention is that although macroeconomists should certainly give more attention to historical analysis and empirics, the profession still very much needs to continue deepening its mathematical and analytical frameworks,
certainly along the lines of the three challenges outline above. A central thrust of modern economics, especially since World War II, has been to introduce greater mathematical rigor and discipline into analysis. Although this approach has been much criticized, mathematical rigor serves two essential roles. First, it makes it far easier to make the field cumulative, so that researchers can generalize, refine, advance and refute existing theories. Secondly, in conjunction with modern statistical methods, it has made possible to formally parameterize and test specific theoretical models, greatly expanding their applicability.

As noted, the recent financial crisis has raised huge criticism and discontent with the canonical approach to macroeconomics, some justified, some not. A fair criticism is that because academic researchers place great emphasis on internal consistency, there is tendency to give far less rigorous attention to external consistency. As noted, the small number of economists who looked at long-term historical data on the history of financial crises were far better able to analyze and predict the economy’s vulnerability to the financial crisis, as well to project its likely aftermath.

But the current limitations of sophisticated mathematical and statistical models for real world macroeconomic applications should not be viewed as a reason to reject modern technical economics. Over the very long‐term, as economics advances as a science, frameworks that are amenable to concrete mathematical and statistical methods are likely to continue to improve dramatically, especially as computational methods expand and databases become deeper and easier to manipulate. One can imagine that future developments will allow much more nuanced models of how large‐scale markets work, and of the interconnection between financial variables, political and regulatory constraints and macroeconomic outcomes. Ultimately, success in meeting the three challenges detailed here must involve a deepening of research in technical economic methods, not abandonment.

Hmm. Well said. The problem has never been math really. It is overdoing of math and models.

As Krugman says you are a fool to believe in your own model. It just helps you understand an economic situation better. That is what it should be. The thrust should be to explain what your analysis shows and not how beautiful your model is.


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