DSGE models and forecasting

Putting the New Keynesian DSGE model to the real-time forecasting test

by Marcin Kolasa, Michał Rubaszek and Paweł Skrzypczyński


Dynamic stochastic general equilibrium models have recently become standard tools for policy-oriented analyses. Nevertheless, their forecasting properties are still barely explored. We fill this gap by comparing the quality of real-time forecasts from a richly-specified DSGE model to those from the Survey of Professional Forecasters, Bayesian VARs and VARs using priors from a DSGE model. We show that the analyzed DSGE model is relatively successful in forecasting the US economy in the period of 1994-2008. Except for short-term forecasts of inflation and interest rates, it is as good as or clearly outperforms BVARs and DSGE-VARs. Compared to the SPF, the DSGE model generates better output forecasts at longer horizons, but less accurate short-term forecasts for interest rates. Conditional on experts’ now casts, however, the forecasting power of the DSGE turns out to be similar or better than that of the SPF for all the variables and horizons.

Combining VAR and DSGE forecast densities

by Ida Wolden Bache, Anne Sofie Jore, James Mitchell and Shaun Vahey


A popular macroeconomic forecasting strategy takes combinations across many models to hedge against instabilities of unknown timing; see (among others) Stock and Watson (2004), Clark and McCracken (2010), and Jore et al. (2010). Existing studies of this forecasting strategy exclude Dynamic Stochastic General Equilibrium (DSGE) models, despite the widespread use of these models by monetary policymakers. In this paper, we combine inflation forecast densities utilizing an ensemble system comprising many Vector Autoregressions (VARs), and a policymaking DSGE model. The DSGE receives substantial weight (for short horizons) provided the VAR components exclude structural breaks. In this case, the inflation forecast densities exhibit calibration failure. Allowing for structural breaks in the VARs reduces the weight on the DSGE considerably, and produces well-calibrated forecast densities for inflation.

Forecasting the US Real House Price Index: Structural and Non-Structural Models with and without Fundamentals

by Rangan Gupta, Alain Kabundi and Stephen Miller


We employ a 10-variable dynamic structural general equilibrium model to forecast the US real house price index as well as its turning point in 2006:Q2. We also examine various Bayesian and classical time-series models in our forecasting exercise to compare to the dynamic stochastic general equilibrium model, estimated using Bayesian methods. In addition to standard vector-autoregressive and Bayesian vector autoregressive models, we also include the information content of either 10 or 120 quarterly series in some models to capture the influence of fundamentals. We consider two approaches for including information from large data sets – extracting common factors (principle components) in a Factor-Augmented Vector Autoregressive or Factor-Augmented Bayesian Vector Autoregressive models or Bayesian shrinkage in a large-scale Bayesian Vector Autoregressive models. We compare the out-of-sample forecast performance of the alternative models, using the average root mean squared error for the forecasts. We find that the small-scale Bayesian-shrinkage model (10 variables) outperforms the other models, including the large-scale Bayesian-shrinkage model (120 variables). Finally, we use each model to forecast the turning point in 2006:Q2, using the estimated model through 2005:Q2. Only the dynamic stochastic general equilibrium model actually forecasts a turning point with any accuracy, suggesting that attention to developing forward-looking microfounded dynamic stochastic general equilibrium models of the housing market, over and above fundamentals, proves crucial in forecasting turning points.

Perhaps by coincidence, three new papers in this week’s issue of the NEP-DGE report deal with forecasting. Kolasa, Rubaszek and Skrzypczyński says that DSGE models perform remarkably well. Bache, Jore, Mitchell and Vahey claim that VAR models with structural breaks do better, but of course structural breaks cannot be predicted with a VAR. Gupta, Kabundi and Miller show that DSGE models of real estate markets are better with turning points, which are the most difficult statistic to forecast.

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2 Responses to DSGE models and forecasting

  1. M.H. says:

    So, if I understand right, a DSGE model would have seen the real estate crisis coming. So why exactly has this way of modeling lost favor with the crisis? The other two paper show also that DSGE models are better at forecasting!

  2. Ken Houghton says:

    Maybe because there wasn’t a DSGE model that predicted the FIRE crisis?

    This is roughly along the lines of the prominent economist (Feldstein?) who last week declared that housing price increases really don’t help with economic growth. Now he says this.

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