DSGEs, Detrending, and Forecasting

Posted by Wendy Conklin at Jul 19, 2011 | No Comments »

With some implications for the debate over assessing fiscal and monetary policies

Reader Brian writes:


DSGE’s aren’t the answer to everything, but I still find the microfoundations, careful treatment of expectations, etc. still attractive and, in my opinion, the best we have at the moment.

As somebody who has served on many dissertation committees where the dissertation involves cutting edge DSGEs (dynamic stochastic general equilibrium models), I can attest to the fact that such models can be very useful in providing insights into the workings of the macroeconomy, as well as the welfare implications associated differing policy regimes.

However, I think Brian’s observations highlight several misconceptions, and one important drawback of DSGEs. (An excellent review of the use of DSGEs in policy is provided by C. Tovar)

Misconceptions

Regarding the treatment of expectations, DSGEs usually incorporate model consistent expectations. However, ever since John Taylor’s pathbreaking work in the early 1990s [0], we have had model-consistent expectations imbedded in certain structural macroeconometric models. Hence, a DSGE is not necessary for operationalizing rational expectations.

Regarding microfoundations, if one examines the guts of the standard New Keynesian versions (including the one recently used by John Taylor [1]), one usually finds lots of ad hoc additions. Consumption is definitely not described by a simple Euler equation as implied by the pure rational expectations-life cycle hypothesis; usually there are some hand-to-mouth consumers floating around. Prices are not freely flexible; rather Calvo pricing is often assumed for tractability. Capital adjustment costs, and other frictions are often included as well. Why not leave these frictions out? Because, without them, it is well nigh impossible to replicate the impulse response functions of real world data. In other words, the bright line of microfoundations versus ad hoc functions is in fact pretty fuzzy.

(And from an international finance perspective, it’s troubling that the real exchange rate is usually linked one-for-one with the ratio of the marginal utilities of consumption, something that is as counterfactual as one can get. And don’t get me started on the risk premium gets introduced into these models, if indeed there is one.)

The Big Drawback

DSGEs (and their predecessors, RBCs) are models of the business cycle. As such, they focus on the deviations from trend. However, in order to predict where the economy will be in one year, given current conditions and policies, one needs to know what the trend is. In other words, extracting the cycle from the trend is critically important. This is a point that James Morley made in his “Emperor has no clothes” paper.

This issue has long been recognized in the policy community. From Camilo Tovar:


Econometricians often fail to be able to observe the theoretical concepts modeled (eg the output gap). So a first question is: how to match the theoretical concepts in DSGE models with those of the observed data? This is not trivial (and certainly the problem is not exclusive to these models). In the DSGE literature the theoretical concepts have been captured not against specific data figures (say GDP levels or inflation) but against filtered data (eg by using the Hodrick-Prescott filter). Filtering decomposes the data into a cyclical component and a trend component. The cyclical component is what is frequently fed into the model. By doing so the analysis focuses on the business cycle frequencies, mainly because it is considered that DSGE models are better suited to explain short-run rather than long-run cycles. However, filtering has important implications (see discussion in Del Negro and Schorfheide (2003)). One is that, forecasting stays out of the reach of DSGE models since the goal is to forecast actual rather than filtered data. The second is that the dynamics obtained do not match those required by policy makers, weakening the usefulness of DSGE models as policy tools. Alternatives often used are the linear detrending and demeaning of variables, as well, as transforming variables so that they are stationary around a balanced growth path. In this last case, the problem is that the trend is often assumed to follow an exogenous stochastic process, which is imposed by the researcher.

In order to highlight the real-world complications involved in this issue, consider two popular cycle-trend extraction methods used in the “business”: the Hodrick-Prescott (HP) filter, and band pass (BP) filter. I apply the HP and BP filters over the 1967Q1-11Q1 sample, and HP filter to the 1967Q1-09Q1 sample, and plot the resulting cycle components in Figure 1.

Figure 1 demonstrates how the inferences regarding the output gap differ wildly depending on the filter used; and the sensitivity of the results — even for a given filter — to the endpoints (notice how the HP filter gives different output gaps for 2009Q1 depending on the sample).

Appealing to simple theories does not necessarily yield uncontroversial results. Figure 1 also includes the trend implied if one thinks GDP and consumption (here services only) are cointegrated. That approach, outlined by Cogley and Schaan (1995) following Cochrane (1994), implies that the economy was 2.7% above trend in 2011Q1.

The large scale macroeconometric models usually rely upon some sort of production function approach to calculating the trend. The implied output gaps from the statistical filter approach and the CBO’s version of the production function approach (described here) are displayed in Figure 2.

For more on the scary things the HP filter can do, see T. Cogley, and J. Nason, 1995, “Effects of the Hodrick-Prescott filter on trend and difference stationary time series Implications for business cycle research,” Journal of Economic Dynamics and Control 19(1-2): 253-278. See also Simon van Norden on the use of these types of filters in current analysis.

Concluding Thoughts

Much current macro research that I am familiar with is conducted using DSGE’s of one type or another. Very little research is conducted using large scale macroeconometric models. That doesn’t mean the results of those large scale macro models is useless, and that DSGE’s are inherently superior; one has to keep in mind the imperatives of academia are different from those pertaining to the policy world. And there are many different sets of models that have differing strengths vis a vis the two categories. Time series models might do better in forecasting. Small macroeconometric models can provide insights into the way the economy works, without necessarily being better at prediction. In general, the best type of model depends upon the question at hand; this depends not only on the model characteristics, but also the constraints imposed when faced with noisy and limited real-world (real-time) data.

Or, as my parents used to tell me ad nauseum, “new is not necessarily better” (roughly translated).


Similar Posts:

Share

Leave a Reply