Would DSGE Models Have Predicted the Great Recession in Austria?
 113 Downloads
Abstract
Dynamic stochastic general equilibrium (DSGE) models are the common workhorse of modern macroeconomic theory. Whereas storytelling and policy analysis were in the forefront of applications since its inception, the forecasting perspective of DSGE models is only recently topical. In this study, we perform a postmortem analysis of the predictive power of DSGE models in the case of Austria’s Great Recession in 2009. For this purpose, eight DSGE models with different characteristics (small and large models; closed and open economy models; one and twocountry models) were used. The initial hypothesis was that DSGE models are inferior in exante forecasting a crisis. Surprisingly however, it turned out that not all but those models which implemented features of the causes of the global financial crisis (like financial frictions or interbank credit flows) could not only detect the turning point of the Austrian business cycle early in 2008 but they also succeeded in forecasting the following severe recession in 2009. In comparison, nonDSGE methods like the exante forecast with the Global Economic (Macro) Model of Oxford Economics and WIFO’s expert forecasts performed comparable or better than most DSGE models in the crisis.
Keywords
DSGE models Business cycles Forecasting Openeconomy macroeconomicsJEL Classification
C11 C32 C53 E32 E371 Introduction
It is common knowledge that the economic community was not able to forecast the Great Recession in 2009. The crisis evolved in a sequence of crises (see Breuss 2016): it started with the US subprime crises, followed by a banking crisis triggered by the Lehman Brothers’ crash on 15 September 2008 which induced a collapse of the interbank market. Then the stock market plunged and caused the Great Recession in 2009. Starting in the United States it spread to most industrial countries. Europe, in particular the Euro area generated its unique “Euro (debt) crisis”. As an excuse, one argued that because of the specificity of the crisis, the economic models then used were not able to forecast it.
In the forecasting business, a variety of models are used, but primarily traditional macro econometric models. The now common workhorse of modern macroeconomic theory, however, are DSGE (Dynamic stochastic general equilibrium) models.^{1} They are used to predict (forecast) and explain (storytelling) comovements of aggregate time series over the business cycle (real business cycle theory) and to perform policy analysis (policy experiments^{2}: IRF implications of shocks of fiscal and monetary policy and of technical change^{3}). Whereas the two latter applications were in the forefront of applications since its inception, based on the work by Kydland and Prescott (1982),^{4} the forecasting perspective is only recently topical.
Most forecasting evaluations with DSGE models so far were executed for the US economy and for the Euro area (at the ECB). In the following we perform a postmortem of DSGE model forecasts of the Great Recession (2009) in Austria. For this purpose, we use eight DSGE models with different characteristics (closed and open economy models; one and twocountry models). Primarily, the development of the Austrian real GDP during the Great Recession of 2009 and thereafter is evaluated exante with outofsample forecasts.
The paper is structured as follows. Chapter 2 gives a brief overview of the literature on forecasting with DSGE models. Chapter 3 describes the eight DSGE models used for this forecasting exercise. In chapter 4 the forecasting performance of the different models for Austria during the Great Recession is evaluated. Additionally, in chapter 5 we check the forecasting performance of nonDSGE methods (Global Economic (Macro) Model of Oxford Economics and WIFO’s expert forecasts). Conclusions are drawn in the last chapter.
2 Review of Literature on Forecasting with DSGE Models

Real business cycle (RBC) theory of neoclassical growth models with flexible prices. Real shocks cause business cycle fluctuations.^{5} The fathers of RBC models are Kydland and Prescott (1982).

New Keynesian DSGE models (NK) build on a structure similar to RBC models, but assume that prices and wages are set by monopolistically competitive firms, adjusting not instantaneously and costlessly (price and wage rigidity). The first who introduced this framework were Rotemberg and Woodford (1997).

New Keynesian Synthesis (NKS) models.^{6} Goodfriend and King (1997) and Clarida et al. (1999) introduced a framework mixing RBC features with nominal and real rigidities.
DSGE models are widely applied in academic research but also in international institutions (European Commission, IMF, ECB), in particular in central banks. More and more DSGE models are also used for forecasting purposes.^{9} The literature so far dealt firstly with general aspects of the forecasting performance of DSGE models and with comparisons with other times series techniques (mostly VARs and BVARs), in recent attempts the predictive power of DSGE models were applied to understand the GFC 2008/09.^{10} The hitherto forecasting exercises were concentrated on the USA and the Euro area.
2.1 USA
The forecasting exercise of Del Negro and Schorfheide (2007) is an early attempt to evaluate the forecasting quality of DSGE models. First, they develop a set of tools that is useful for assessing the time series fit of a DSGE model. They systematically relax the implied cross coefficient restrictions of the DSGE model to obtain a VAR specification that is guaranteed to fit better than the DSGE model. Then they use this specification as a benchmark to characterize and understand the degree of misspecification of the DSGE model. Second, they apply these tools to a variant of the model of Smets and Wouters (2007) and document its fit and forecasting performance based on postwar U.S. data over the period 2Q1974 to 1Q2004.
The first comprehensive analysis of the forecasting capability of DSGE models during the Great Recession 2009 in the USA is done by Del Negro and Schorfheide (2013). They demonstrate the forecasting performance of the Smets and Wouters (2007) DSGE model with data up to 2011, compare it with professional forecasts published in the “Blue Chip” survey and the forecasts by the Federal Reserve Board of Governors (the socalled “Greenbook”). Firstly, the DSGE models used to exante forecast the Great Recession 2009 do not perform better than the Blue Chip and the Greenbook forecasts. Secondly, the examination of DSGE model’s forecasts during the 2008–2009 US recession suggests that the DSGE models with financial frictions are preferable to the original Smets and Wouters model.
Kolasa and Rubaszek (2014) compare the quality of forecasts from DSGE models with and without financial frictions. The exercise is done for the US economy with data, covering the period 1Q1970 to 4Q2010. They find that accounting for financial market imperfections does not result in a uniform improvement in the accuracy of point forecasts during noncrisis times while the average quality of density forecast even deteriorates. In contrast, adding frictions in the housing market proves very helpful during the times of financial turmoil, over performing both the frictionless benchmark and the alternative that incorporates financial frictions in the corporate sector.
Merola (2014) analyses expost the relevant factors for the recent banking crisis of the US economy in 2008. The analysis is done by comparing the original Smets and Wouters model (2007) with an alternative version augmented with the financial accelerator mechanism à la Bernanke et al. (1999). Both versions are estimated using Bayesian techniques over the sample period: 1967 to 2012. The Smets and Wouters model, augmented with the financial accelerator mechanism, is suitable to capture much of the historical developments in U.S. financial markets that led to the financial crisis. The model can account for the output contraction in 2008, as well as the widening in corporate spreads and supports the argument that financial conditions have amplified the U.S. business cycle and the intensity of the recession.
2.2 Euro Area
Christoffel et al. (2011) make nstep ahead and outofsample forecasts for the Euro area with the “The New AreaWide Model (NAWM) of the Euro Area” and compared its performance with vector autoregressions (VARs), Bayesian vector autoregressions (BVARs), a random walk, and a location parameter, namely the mean. The outofsample forecast evaluation exercise covers the period after the introduction of the euro up to the precrisis year 2006. Overall, the results suggest that the NAWM performs quite well when compared with the reducedform forecasting tools. In particular, the model compares favourably when forecasting real GDP growth, the trade variables, employment, the real exchange rate, and the shortterm nominal interest rate. However, the NAWM is less successful when forecasting certain nominal variables, e.g. nominal wage growth.
Smets et al. (2013) analyze the realtime forecasting performance of the New Keynesian DSGE model of Galí et al. (2012) estimated on Euro area data. They investigate to what extent forecasts of inflation, GDP growth and unemployment by professional forecasters improve the forecasting performance over the period 1Q1999 to 4Q2010. The authors consider two approaches for conditioning on such information. Under the “noise” approach, the mean professional forecasts are assumed to be noisy indicators of the rational expectations forecasts implied by the DSGE model. Under the “news” approach, it is assumed that the forecasts reveal the presence of expected future structural shocks in line with those estimated over the past. The forecasts of the DSGE model are compared with those from a Bayesian VAR model and a random walk. Overall, the authors find that the GSW model outperforms the randomwalk model and has similar performance as the nonstructural BVAR model. Adding one to twoyearahead professional forecasts of real GDP growth, inflation, and the unemployment rate does not significantly improve the overall performance of the GSW model, although it does help to reduce some of the bias in the forecasts of wage growth in the news models.
3 DSGE Models Applied to the Austrian Business Cycle
Austria is a small open economy and since its EU accession in 1995 deeply integrated into EU’s Single Market. Therefore, DSGE models with an international nexus should be more suitable to describe the Austrian business cycle.
On the other hand, under the basic assumption that theoretical DSGE models are micro founded, they should describe any market economy, not only that for which the model was originally designed. Because this implies an uncertainty concerning the model selection, we make a compromise. We use DSGE models originally applied for other countries (USA, Portugal, Euro Area etc.) as well as such designed for Austria in order to track the development of Austrian macroeconomic data. We have selected eight different types of models: closed and open economy DSGE models, models for small and mediumsized models as well as large open economy models; some are one, others are twocountry models.
In the application of the eight models we used the original calibration and adjusted it to the Austrian case where necessary (e.g. in the open economy and twocountry models) we used the respective parameters for Austria’s export and import shares).
3.1 Small and MediumSized Closed and Open Economy Models
DSGE models estimated with Austrian data
Authors  Characteristics  

Countries no  Originally applied in  Model size Small/medium/large  Economy Closed/open  Endogenous variables  Special features  
An and Schorfheide (2007)  1  USA  Small  Closed  3  NK 
Lubik and Schorfheide (2007)  1  USA  Small  Open  5  NK + EX 
Smets and Wouters (2007)  1  USA  Medium  Closed  7  NK 
Del Negro and Schorfheide (2013)  1  USA  Medium  Closed  8  SW + FF 
Christoffel et al. (2008)  1  NAWM (EA)  Large  Open  18  NK 
Poutineau and Vermandel (2015)  2  Core + Periphery EA  Large  Open  15  EA + IBF 
Almeida (2009)  1  Portugal  Large  Open  13  NK 
Breuss and Rabitsch (2009)  2  Austria + EA  Large  Open  17  NK 
3.1.1 A Small Closed Economy 3Equation DSGE Model
The simplest possible example of a DSGE model^{11} is the “Baby” DSGE model of An and Schorfheide (2007).^{12} With this small benchmark monetary policy analysis model, the authors studied monetary policy aspects of the USA. The theoretical economy in the An and Schorfheide model consists of a final goods producing firm, a continuum of intermediate goods producing firms, a representative household, and a monetary and a fiscal authority. It has six equations describing the behaviour of output, consumption, government spending, technology, inflation, and a shortterm nominal interest rate (Taylor rule). When substituting consumption and government spending into the output equation and technology into the Euler consumption equation the model reduces to three endogenous variables (GDP, inflation and interest rate). Three shocks (fiscal, monetary and productivity) are applied. Except of the interest rate, all variables are detrended.^{13} The measurement equation linking the data on quartertoquarter GDP growth (differences of the natural logarithm, annual quartertoquarter inflation rates, and annual nominal interest rates. The model is estimated in YADA^{14} and Austrian data over the period 1Q1992 to 4Q2016. The primary database for this and the following models is those of Oxford Economics which are mainly based on Eurostat data.
3.1.2 A Small Open Economy DSGE Model
Lubik and Schorfheide (2007)^{15} extended the closed economy An and Schorfheide model to a small open economy DSGE model. It consists of a forwardlooking ISequation and a Phillips curve. Monetary policy is also given by a Taylortype interest rate rule, where the exchange rate is introduced via the definition of consumer prices and under the assumption of PPP. The model uses five shocks (three shocks of the An and Schorfheide model) plus two external shocks (foreign GDP and foreign inflation). In loglinearized form the model is estimated in YADA and with Austrian data over the period 1Q1999 to 4Q2016.
3.1.3 The Most Cited DSGE Model of a Closed Economy
A wellknown example of a mediumsized DSGE model is that of Smets and Wouters (2007).^{16} Although the authors study shocks and frictions in US business cycles the model is designed for a closed economy. The Smets and Wouters (SW) model uses basically a sticky price and wage system, followed by a flexibleprice based output gap measure in the monetary policy rule.
The SW model is consistent with a balanced steadystate growth path driven by deterministic labour augmenting technological progress. The observed variables are given by quarterly data of the log of real GDP per capita, the log of real consumption per capita, the log of real investment per capita, the log of hours per capita, the log of quarterly GDP deflator inflation, the log of real wages, and the federal funds rate (in the application for Austria, ECB’s Main Financing Operations (MFO) interest rate). All observed variables except hours, inflation, and the MFO rate measured in first differences of the natural logs. Consistent with the number of endogenous variables, the SW model uses seven shocks to describe the business cycle development: shock to fiscal, monetary, consumption, investment, technology, inflation and wages. The model is estimated in YADA with Austrian data over the period 1Q1995 to 4Q2016.
3.1.4 The SW DSGE Model with Financial Frictions
Del Negro and Schorfheide (2013)^{17} presents a smallscale version of the Smets and Wouters model by removing several features, such as capital accumulation. It is also assumed that there is no wage stickiness in the smallscale model. Consequently, the marginal cost is equal to the real wage, and the latter is equal to the marginal rate of substitution between consumption and leisure. In addition, Del Negro and Schorfheide (2013) introduce financial frictions into their variant of the SW model based on the financial accelerator approach of Bernanke et al. (1999). The set of measurement equations is augmented by an equation explaining the spread of Moody’s seasoned Baa corporate bond yield spread over the 10year treasury note yield at constant maturity by the interest rate difference (real interest rate minus real rent on capital). In the Del Negro and Schorfheide model the eight endogenous variables are driven by eight shocks: In addition to the seven shocks in the SW model, one financial shock is introduced. In the estimation for Austria, the spread is measured by the 10year Austrian government bond yield over those of Germany. The model is estimated in YADA with Austrian data over the period 1Q1992 to 4Q2016.
3.2 Large Open Economy Models
3.2.1 The NAWM DSGE Model for the Euro Area
In the following we describe largesized open economy DSGE models, used to estimate the Austrian business cycle. These models should be able to better track the development of the Austrian economy than small and often closed economy DSGE models. The reason is that Austria since its EU accession in 1995 became more and more integrated into the Single Market of the EU. The economic development of the EU neighbours (in particular that of the major trading partner Germany) primarily determine the path of the business cycle in Austria.
Christoffel et al. (2008), authors at the European Central Bank (ECB) designed the “The New AreaWide Model (NAWM) of the Euro Area”, a microfounded openeconomy DSGE model. The NAWM is for use in the (Broad) Macroeconomic Projection Exercises regularly undertaken by ECB/Eurosystem staff and for policy analysis. The NAWM is neoclassical in nature and centred around intertemporal decisions of households and firms which are maximising expected lifetime utility and the expected stream of profits, respectively.
The NAWM models the domestic economy (the Euro area) existing of four types of economic agents: households, firms, a fiscal authority, and a monetary authority (Taylor rule). Firms are distinguished between producers of tradable differentiated intermediate goods and producers of three nontradable final goods: a private consumption good, a private investment good, and a public consumption good. In addition, there are foreign intermediategood producers that sell their differentiated goods in domestic markets, and a foreign retail firm that combines the exported domestic intermediate goods. International linkages arise from the trade of intermediate goods and international assets, allowing for limited exchangerate passthrough on the import side and imperfect risk sharing.
The NAWM consist of 18 endogenously explained macroeconomic variables (GDP, private consumption, government consumption, investment, employment, wages, interest rate, effective exchange rate, exports, imports, foreign demand, foreign prices, inflation (GDP and consumption deflator), foreign interest rate, export prices of competitors, import deflator, oil prices). These 18 variables are driven by the same number of shocks. For our exercise the model is estimated with Austrian data in YADA over the period 1Q1995 to 4Q2016.
3.2.2 A TwoRegions Euro Area DSGE Model with Banking
Poutineau and Vermandel (2015) develop a twocountry DSGE model to document how the transmission of asymmetric shocks in the Eurozone has been affected with a banking system that provides crossborder interbank and corporate lending facilities. This solution is original with respect to the existing literature of monetary policy issues in a monetary union. The authors try to overcome with their specification missing elements in precrisis models by considering phenomena which have contributed to the GFC 2008, the Great Recession 2009 and the following Euro crisis. The twocountry model considers EMU (the Euro area) as consisting of two regions: the periphery and the core. The number of shocks is higher (or equal) to observable variables (15) to avoid stochastic singularity issues.
We estimate this DSGE model with 15 endogenous variables for a twocountry setting (Austria and Euro Area) in Dynare^{18} over the (Euro area) period 1Q1999 to 4Q2016.
3.2.3 A NK DSGE Model for Portugal
Almeida (2009) developed a NewKeynesian DSGE model for a small open economy integrated in the Economic and Monetary Union (EMU of the EU), estimated for the Portuguese economy, using a Bayesian approach. The model features five types of economic agents namely households, firms, aggregators, the rest of the world and the government. It is assumed that monetary policy is decided by the ECB and that the domestic economy’s size is negligible, relative to those of the EMU, and therefore Portugal cannot influence EMU’s economy but the EMU is determining Portugal’s business cycle. The model contains 13 endogenous variables with the same number of shocks.
This prototype model for a small member of the Euro area should also fit well for the Austrian economy. The estimation of this DSGE model with Austrian data (13 endogenous variables) is executed in Dynare over the period 1Q1995 to 4Q2015.
3.2.4 A TwoCountry DSGE Model of Austria and the Euro Area
Breuss and Rabitsch (2009) were the first to model a DSGE model for Austria. Although the approach is theoretically similarly to those of the SW model, its novel feature consists in modelling a twocountry DSGE model. It is a DSGE model in the style of New Keynesian/New Open Economy Macroeconomics for the small open economy of Austria as a member of the European Economic and Monetary Union (EMU).^{19} The model was originally estimated using Bayesian methods on quarterly data covering the period of 1Q19751Q2005. Because Austria entered the EMU on 1 January 1999 we considered this regime switch by partitioning into two periods: a preEMU and a postEMU period. For our purpose, the evaluation of the forecast quality in the Great Recession 2009 we reestimated the model (with 17 endogenous variables and an equal number of shocks) from 1Q1995 to 4Q2015. The estimation is executed in Dynare with Austrian and Euro area data.
4 Which Model Would Have Best Predicted the Recession 2009?
 (i)
Before the default of Lehman Brothers (bLB). The models are estimated until 1Q2008 (the peak of the business cycle) and the outofsample forecasts run from 2Q2008 to 4Q2016.
 (ii)
After the default of Lehman Brothers (aLB). The models are estimated until 3Q2008 (the peak of the business cycle) and the outofsample forecasts run from 4Q2008 to 4Q2016.
Outofsample forecast can be executed conditional^{22} on specific knowledge at the inception of a crisis or unconditional (without side knowledge). In the following we evaluate the models with unconditional forecasting methods. In our analysis, we take the mean forecasts.^{23}
Forecasting performance of DSGE models compared with macro models in the Great Recession 2009
Model type  NRMSE  

Great Recession 2009*  Great Recession 2009–2016*  
Before LB  TP  After LB  TP  Before LB  After LB  
DSGE models  
An and Schorfheide (2007)  0.4207  Yes  0.6210  No  0.3146  0.3856 
Lubik and Schorfheide (2007)  0.4969  No  0.3743  Yes  0.2596  0.2101 
Smets and Wouters (2007)  0.2883  Yes  0.2681  Yes  0.1897  0.1811 
Del Negro and Schorfheide (2013)  0.3267  No  0.2931  Yes  0.1659  0.1659 
Christoffel et al. (2008)  0.6528  No  0.3205  Yes  0.3423  0.2472 
Poutineau and Vermandel (2015)  0.2792  Yes  0.2102  Yes  0.2298  0.1668 
Almeida (2009)  0.3000  Yes  0.3342  Yes  1.8176  0.6096 
Breuss and Rabitsch (2009)  0.8811  Yes  0.3986  Yes  1.1101  0.4669 
Global Economic (Macro) Model  
Oxford Economics (OEF)  0.4537  No  0.2920  Yes  0.2946  0.2001 
The common interpretation of the causes of the past crisis is that it started with a subprime housing crisis, leading to a financial (banking) crisis in the USA (default of Lehman Brothers on 15 September 2008) and spread then globally to a global financial crisis and a Great Recession in 2009 (see Breuss 2016). The Austrian economy was hit primarily by external forces via trade and capital movements. Of course, the shock of Lehman Brothers led also to a freeze of interbank lending. However, in contrast to the USA as well as Ireland and Spain Austria had no housing crisis.
Austria is a prototype of a small open economy. Therefore, models designed for closed economies should a priori not fit very well when it comes to reproduce and forecast its business cycle. Interestingly, in turned out that this first presumption is not quite true. A further conjecture is, that a twocountry DSGE model approach (Austria and Euro area) should be better suitable to reproduce Austria’s business cycle because Austria—as a member of EU and the Euro area—is economically heavily integrated into the Euro area. Therefore, shocks in the Euro area determine heavily Austria’s business cycle.
4.1 Turning Point of the Austrian Business Cycle in 2008
4.2 Exante Forecasting the Great Recession in 2009
4.2.1 Before Lehman Brothers (1Q2008)
At the beginning of 2008 (before the default of Lehman Brothers) one could already have known the burst of the subprime sector in the USA evolving already in 2007.^{25} At that point in time only a few DSGE models could realize that the Austrian business cycle passed the peak and began to turn into a recession. At the beginning of 2008, the closed economy models of Smets and Wouters and that of Del Negro and Schorfheide (see Fig. 1) as well as the open economy model of Almeida and the twocountry models of Poutineau and Vermandel as well as those of Breuss and Rabitsch realized that a recession is under way (see Fig. 2).
Measured by NRMSE, Poutineau and Vermandel (the tworegion Euro area model with banking) wins the trophy with the best score (see Table 2). The secondbest performer in exante forecasting the recession was the SW model. Although catching the turning point early in 2008 the twocountry model of Breuss and Rabitsch did not get the upswing following the Great Recession in 2009 (see Fig. 2). Similarly, the Almeida model performed badly after the Great Recession (see Fig. 2). Quite bad was the NAWM model in realizing the turning point and catching the recession at the beginning of 2008.
4.2.2 After Lehman Brothers (3Q2008)
During 2008 the financial crisis broadened and reached its climax with the default of Lehman Brothers on 15 September 2008. After that unique event the interbank market crashed, a credit squeeze stopped new real investments, the financial crisis turned into a real recession, called the Great Recession. By hindsight we understand the mechanics of the emergence of the Great Recession in 2009. At the end of 2008, after the Lehman Brothers crash only few forecasters realized which consequences this event might have. Therefore, they were not able to forecast properly the recession.
Nevertheless, all eight DSGE models were able to detect the turning point of the Austrian business cycle after Lehman Brothers (see Table 2; Figs. 1, 2). Again, the best performer with the least forecasting errors was the twocountry DSGE model with banking of Poutineau and Vermandel. Closely at the second place comes the SW model. The SW is a surprise in the case of Austria, because it was designed for a closed economy and estimated with US data (although the USA are also a closed economy). The explanation may be due to the sticky price and wage system, followed by a flexibleprice based output gap measure in the monetary policy rule which can describe quite properly the Austrian institutional wage bargaining process with its strong trade unions. The worst performer at the end of 2008 was the simplest model of An and Schorfheide.
4.3 Exante Forecasting the Great Recession in 2009 and the Recovery Thereafter
Measured by the normalized RMSE before and after Lehman Brothers, the best performer over the period 2008–2016 was the smallscale SW DSGE model with financial frictions by Del Negro and Schorfheide. However, also the SM model and those of Poutineau and Vermandel performed quite well. The worst score (highest NRMSE) produced the DSGE model of Almeida (see Table 2; Figs. 1, 2).
4.4 Inflation
Forecasting performance of DSGE models compared with macro models in the Great Recession 2009
Model type  NRMSE  

Great Recession 2009*  Great Recession 2009–2016*  
Before LB  TP  After LB  TP  Before LB  After LB  
DSGE models  
An and Schorfheide (2007)  0.3965  Yes  0.4809  No  0.2757  0.2998 
Lubik and Schorfheide (2007)  0.3560  Yes  0.1749  Yes  0.2954  0.2542 
Smets and Wouters (2007)  0.2830  No  0.2681  Yes  0.4408  0.5184 
Del Negro and Schorfheide (2013)  0.6471  No  0.6914  Yes  0.4938  0.5572 
Christoffel et al. (2008)  1.3747  Yes  1.0315  Yes  1.0161  1.0430 
Poutineau and Vermandel (2015)  0.2280  Yes  0.2650  Yes  0.3937  0.4437 
Almeida (2009)  0.3524  Yes  0.3070  Yes  0.2475  0.2216 
Breuss and Rabitsch (2009)  0.3215  No  0.5785  Yes  0.2809  0.3696 
Global Economic (Macro) Model  
Oxford Economics (OEF)  0.2519  No  0.1861  Yes  0.2642  0.2052 

The best inflation performance (the lowest NRMSE figures) exhibits the Pouteneau and Vermandel model during the Great Recession 2008–2010; the best inflation performer in the postrecession period (2010–2016) was the Almeida model. The worst performance delivered the NAWM model.
 Comparing the predictive power of real GDP with those for inflation the results were as follows:

During the Great Recession 2009 the models of Del Negro and Schorfheide and that of NAWM forecasted GDP better than inflation before and after Lehman Brothers. Poutineau and Vermandel as well as Breuss and Rabitsch forecasted inflation better than GDP after Lehman Brothers. The small (An and Schorfheide, Lubik and Schorfheide) and mediumsized DSGE models (Smets and Wouters) were better in forecasting exante the inflation than real GDP.

In the postrecession period 2009–2016 five out of the eight DSGE models forecasted exante real GDP better than inflation. Only the models An and Schorfheide, Almeida, as well as Breuss and Rabitsch forecasted inflation better than real GDP.

5 How Performed NonDSGE Methods?
The primary goal of this paper was to test the exante forecasting quality of DSGE models in case of a severe recession. For purposes of comparison, we take two NonDSGE methods to evaluate postmortem the Austrian Great Recession. One is a global macro model of Oxford Economics, the other is the expert forecasting of WIFO.
5.1 OEF Global Economic Model
Oxford Economics forecasts monthly the economic development of 80 countries in its Global Economic (Macro) Model. The OEF Global Economic Model^{26} is the only macroeconomic model that fully integrates 80 global economies plus the Eurozone. The Oxford model is an eclectic model designed to capture the key relationships in the global economy: (i) Keynesian in the short run; (2) Monetarist in the long run. In the short run, shocks to demand will generate economic cycles that can be influenced by fiscal and monetary policy. But over the long run, output is determined by supply side factors: investment, demographics, labour participation and productivity.
 (i)
Before the default of Lehman Brothers (bLB). We take the OEF database as of 3M2008 and forecast the further development of the Austrian GDP in the coming years up to the end of 2012.
 (ii)
After the default of Lehman Brothers (aLB). Here we take the OEF database as of 11M2008 and execute with it a forecast of the following years up to the end of 2013.
5.2 WIFO’s Expert Forecasts
The Austrian Institute of Economic Research (WIF0) makes quarterly forecasts (or revisions of previous forecasts), however, it does not forecast quarterly variables but only yearly macro variables, like real GDP. WIFO always forecasts only 1 year in advance. The forecast is done by a team of experts. Only afterwards the forecast is translated into the WIFO macro model for policy simulation purposes.
Before Lehman Brothers, in March 2008 WIFO already anticipated a turning of the business cycle due to gloomy news about the development (subprime and banking crises) in the USA. After Lehman Brothers in December 2008, WIFO already forecasted a slight decline of real GDP for 2009 (− 0.5%). Then the GDP growth was corrected downwards step by step: March 2009 (− 2.2%). In June 2009 (− 3.4%), WIFO forecasted nearly correctly the final decline of real GDP (− 3.7%) in the year 2009 (see Fig. 3).^{27}
6 Conclusions
DSGE (Dynamic stochastic general equilibrium) models are the common workhorse of modern macroeconomic theory. They are widely applied in academic research but also in international institutions (IMF, European Commission, ECB), in particular in central banks. DSGE models serve three purposes: They are used to predict (forecast) and explain (storytelling) comovements of aggregate time series over the business cycle (real business cycle theory) and to perform policy analysis (policy experiments: IRF implications of shocks of fiscal and monetary policy and of technical change). Whereas the two latter applications were in the forefront of applications since its inception, the forecasting perspective is only recently topical.
Most forecasting evaluations with DSGE models so far were executed for the US economy and for the Euro area (at the ECB). In this study, we performed a postmortem of DSGE model forecasts of the Great Recession (2009) in Austria. For this purpose, eight DSGE models with different characteristics (closed and open economy models; one and twocountry models) were used.
The initial hypothesis that DSGE models might be less suitable to forecast a severe recession than macro models could be partly falsified. The forecasts of real GDP growth for Austria are obtained at two different junctures of the crisis that led to the recession: At the beginning of 2008 and at the end of this year (after the default of Lehman Brothers). Whereas early in 2008 only five of eight models detected the turn into recession, after Lehman Brothers seven out of eight DSGE models correctly saw the Austrian business cycle turning into recession. With respect to the predictive power measured by RMSE values those models which already included factors which led to the Great Recession, namely financial frictions and interbank features performed the best. This is true for the Poutinau and Vermandel model. Surprisingly, the most cited Smets and Wouters model, a closed economy DSGE model also performed for Austria—a prototype open economy—quite well in exante forecasting the crisis.
With exception of the best performing DSGE models (Poutineau and Vermandel as well as Smets and Wouters) exante forecast of real GDP with nonDSGE models like the Global Macro Model of Oxford economics and WIFO’s expert forecasts performed comparable or better than the other DSGE models in the crisis. Inflation during the crisis was better predicted by the Oxford model than by most DSGE models.
Footnotes
 1.
Although DSGE modelling is mainstream in modern macroeconomics, there are many critics of DSGE modelling. Blanchard (2016) questions the future of DSGE models as a proper instrument of modern macroeconomics. Romer (2016) fundamentally criticises the flaws of DSGE models in properly explaining the fluctuations of economic development. Also, Stiglitz (2017) attacked DSGE modelling because of the wrong microfoundations, which—from his point of view—failed to incorporate key aspects of economic behaviour.
 2.
A recent example is the analysis of the implication of the EUBanking Union with the DSGE model QUEST of the European Commission (2 tworegions Euro area model) by Breuss et al. (2015).
 3.
Volker Wieland (see Wieland et al. 2012) heads an EUsponsored project of DSGE model comparison to analyse fiscal and monetary policy shocks under different rules, executed with a common algorithm. The models used are collected in “The Macroeconomic Model Database (MMB)” (see the MMBWebsite: http://www.macromodelbase.com/download/).
 4.
A short history of DSGE modelling can be found in FernándezVillaverde (2010).
 5.
A basic RBC DSGE model with monopolistic competition can be found in Griffoli (2013), pp. 11–14.
 6.
Poutineau et al. (2015) demonstrate the working of a NKS DSGE model using the benchmark “New Keynesian 3equation Model” consisting of a New Keynesian Phillips curve (inflation), a dynamic IS curve (output) and a monetary policy (Taylor) rule (interest rate).
 7.
FernándezVillaverde (2010) calls the research of formal estimation of DSGE models (the cornerstone of modern macroeconomics)—the combination of rich structural models, novel solution algorithms, and powerful simulation techniques—which allows researchers to transform the quantitative implementation of equilibrium models from ad hoc procedures to a systematic discipline, the New Macroeconometrics.
 8.
 9.
For a literature review, see Del Negro and Schorfheide (2013), p. 35.
 10.
A critical assessment of the usefulness of theorybase forecasts with estimated DSGE models can be found in Giacomini (2015).
 11.
Another similarly simple model would be the “New Keynesian 3equation Model” presented by Poutineau et al. (2015).
 12.
A short description of the An and Schorfheide model can be found in Warne (2015).
 13.
The method of detrending varies in this study. In the Smets and Wouters model we use their method, in the other models we use HodrickPrescott filtering to detrend the variables.
 14.
 15.
A short description of the Lubik and Schorfheide model can be found in Warne (2015).
 16.
A short description of the Smets and Wouters model can be found in Warne (2015).
 17.
A short description of the Del Negro and Schorfheide model can be found in Warne (2015).
 18.
Dynare is used together with Matlab. See Griffoli (2013) and the DYNARE website: http://www.dynare.org/.
 19.
A threecountry version (Austria, Euro area and USA) of this NK DSGE model was developed in Breuss and Fornero (2009).
 20.
The expost or withinsample forecast of the eight DSGE models used to capture the business cycle in Austria can be found in the Annex.
 21.
Six out of the eight DSGE models are estimating the business cycle in quarterly growth rates (differences of the logs of the respective variables). The Almeida and the BreussRabitsch models are estimated in gaps of real GDP.
 22.
Conditional forecasting concerns forecasts of endogenous variables conditional on a certain path and length of path for some other endogenous variables. This is important when one uses realtime data vintages. The values for all observed variables for period T, the last “historical” time period, have often not been released by the statistical authority yet and are therefore missing from the relevant data vintage, i.e., the data set is unbalanced. Accordingly, some of the time T values need to be forecasted and the forecasts of these variables need to take into account that values for other variables are available for the same time period (see Warne, 2015, p. 173).
 23.
The exante forecasts of the eight models also show the confidence intervals (from 50% to 90%). In some cases, where the mean forecast results bLB and aLB are very close, there may be no significant difference in both forecast. Candidates for this conjecture would be the outcome of the well performing models of Smets and Wouters, Del Negro and Schorfheide as well as Poutineau and Vermandel.
 24.
The RMSE of a model prediction with respect to the estimated variable \( X_{model} \) is defined as the square root of the mean squared error: \( RMSE = \sqrt {\frac{{\mathop \sum \nolimits_{i = 1}^{n} \left( {X_{obs,i}  X_{model,i} } \right)^{2} }}{n}} \). Nondimensional forms of the RMSE are useful because we want to compare RMSE of DSGE models with different GDP metrics. There are two approaches: normalize the RMSE to the mean of the observed data or normalize to the range (maximums minus minimum) of the observed data. The latter is used in Table 2: \( NRMSE = \frac{RMSE}{{X_{obs,max}  X_{obs,min} }} \).
 25.
After it reached the peak in 2006, since early 2007 the CaseShiller Home Price Index began to decline dramatically.
 26.
See the website of Oxford Economics: http://www.oxfordeconomics.com/.
 27.
A numerical comparison of the goodness of WIFO’s forecast with the other models (DSGE and OEF model) in Table 2 is difficult, because WIFO makes only annual forecasts. If however, one calculates the normalized RMSE (NRMSE) values for the WIFO forecast of real GDP growth made before LB (March 2008) and after LB (December 2008) over the forecast period 2007–2010, one gets the following NRMSE values: before LB 0.4304, after LB 0.2361. Hence, WIFO forecasted the Great Recession 2009 nearly as good as the OEF model.
Notes
Acknowledgements
Open access funding provided by Vienna University of Economics and Business (WU).
References
 Almeida, V. (2009). Bayesian estimation of a DSGE model for the Portuguese economy. Master Thesis, Universidade Técnica de Lisboa, June 2008.Google Scholar
 An, S., & Schorfheide, F. (2007). Bayesian analysis of DSGE models. Econometric Reviews, 26(2–4), 113–172. (with discussion, 173–219).CrossRefGoogle Scholar
 Bernanke, B. S., Gertler, M., & Gilchrist, S. (1999). The financial accelerator in a quantitative business cycle framework. In J. B. Taylor & M. Woodford (Eds.), Handbook of macroeconomics (Vol. 1C, pp. 1341–1393). Amsterdam: North Holland.CrossRefGoogle Scholar
 Blanchard, O. (2016). Do DSGE models have a future? Policy brief PB 1611, Peterson Institute for International Economics (PIIE), August 2016.Google Scholar
 Breuss, F. (2016). The crisis in retrospect: Causes, effects and policy responses. In Harald Badinger & Volker Nitsch (Eds.), Routledge handbook of the economics of european integration (pp. 331–350). London and New York: Routledge.Google Scholar
 Breuss, F., & Fornero, J. A. (2009). An estimated DSGE model of Austria, the Euro area and the U.S.: Some welfare implications of EMU, FIW working paper, no. 34, August 2009.Google Scholar
 Breuss, F., & Rabitsch, K. (2009) An estimated twocountry DSGE model of Austria nd the Euro area. Empirica—Journal of European Economics, 36(1), 123–158 (based on EI working paper, no. 78, June 2008; Europainstitut WUWien).Google Scholar
 Breuss, F., Roeger, W., & in’t Veld, J. (2015). The stabilising properties of a European Banking Union in case of financial shocks in the Euro Area. European Economy—Economic Papers, no. 543, February 2015.Google Scholar
 Canova, F. (2007). Methods for applied macroeconomic research. Princeton: Princeton University Press.Google Scholar
 Christoffel, K., Coenen, G., & Warne, A. (2008). The new areawide model of the Euro Area: A microfounded openeconomy model for forecasting and policy analysis. ECB working paper series no. 944, October 2008.Google Scholar
 Christoffel, K., Coenen, G., & Warne, A. (2011). Forecasting with DSGE models. In M. P. Clements & D. F. Hendry (Eds.), The oxford handbook of economic forecasting (pp. 89–127). New York: Oxford University Press.Google Scholar
 Clarida, R., Galí, J., & Gertler, M. (1999). The science of monetary policy: A new Keynesian perspective. Journal of Economic Literature, 37(4), 1661–1707.CrossRefGoogle Scholar
 DeJong, D. N., Ingram, B. F., & Whiteman, C. H. (2000). A Bayesian approach to dynamic macroeconomics. Journal of Econometrics, 98(2), 203–223.CrossRefGoogle Scholar
 Del Negro, M., & Schorfheide, F. (2013). DSGE modelbased forecasting. In G. Elliott & A. Timmermann (Eds.), Handbook of economic forecasting (Vol. 2, pp. 57–140). Amsterdam: North Holland.Google Scholar
 Del Negro, M., Schorfheide, F., Smets, F., & Wouters, R. (2007). On the fit of new Keynesian models. Journal of Business & Economic Statistics, 25(2), 123–143.CrossRefGoogle Scholar
 FernándezVillaverde, J. (2010). The econometrics of DSGE models, SERIEs, Journal of the Spanish Economic Association, 1(1–2), 3–49 (CEPR discussion paper, no. 7157, February 2009).Google Scholar
 Galí, J., Smets, F., & Wouters, R. (2012). Unemployment in an estimated new Keynesian model. In D. Acemoglu & M. Woodford (Eds.), NBER macroeconomics annual 2011 (Vol. 26, pp. 329–360). Chicago: University of Chicago Press.Google Scholar
 Giacomini, R. (2015). Economic theory and forecasting: lessons from the literature. Econometric Journal, 18(2), C22–C41.CrossRefGoogle Scholar
 Goodfriend, M., & King, R. (1997). The new neoclassical synthesis and the role of monetary policy. In B. S. Bernanke & J. Rotemberg (Eds.), NBER macroeconomics annual 1997 (Vol. 12, pp. 231–296). MIT Press.Google Scholar
 Griffoli, T. M. (2013). DYNARE user guide: An Introduction to the solution and estimation of DSGE models. DYNARE Website http://www.dynare.org/. Accessed 23 Feb 2018.
 Kolasa, M., & Rubaszek, M. (2014). Forecasting with DSGE models with financial frictions. Dynare working paper series, working paper, no. 40, June 2014.Google Scholar
 Kydland, F. E., & Prescott, E. C. (1982). Time to build and aggregate fluctuations. Econometrica, 50(6), 1345–1370.CrossRefGoogle Scholar
 Lubik, T. A., & Schorfheide, F. (2007). Do central banks respond to exchange rate movements? A structural investigation. Journal of Monetary Economics, 54(4), 1069–1087.CrossRefGoogle Scholar
 Merola, R. (2014). The role of financial frictions during the crisis: An estimated DSGE model. Dynare working paper series, working paper, no. 33, January 2014.Google Scholar
 Poutineau, JCh., & Vermandel, G. (2015). Crossborder banking flows spillovers in the Eurozone: Evidence from an estimated DSGE model. Journal of Economic Dynamics & Control, 51, 378–403.CrossRefGoogle Scholar
 Poutineau, J.Ch., Sobczak, K., & Vermandel, G. (2015). The analytics of the new Keynesian 3equation model. In HAL01194642, 7 September 2015.Google Scholar
 Romer, P. (2016). The trouble with macroeconomics. https://paulromer.net/wpcontent/uploads/2016/09/WPTrouble.pdf. Accessed 23 Feb 2018.
 Rotemberg, J. J., & Woodford, M. (1997). An optimizationbased econometric framework for the evaluation of monetary policy. In B. S. Bernanke & J. Rotemberg (Eds.), NBER macroeconomics annual (Vol. 12, pp. 297–346). MIT Press.Google Scholar
 Smets, F., Warne, A., & Wouters, R. (2013). Professional forecasters and the realtime forecasting performance of an estimated new Keynesian model for the Euro Area. ECB working papers series, no. 1571, August 2013.Google Scholar
 Smets, F., & Wouters, R. (2007). Shocks and frictions in US business cycles: A Bayesian DSGE approach. The American Economic Review, 97(3), 586–606.CrossRefGoogle Scholar
 Stiglitz, J. E. (2017). Where modern macroeconomics went wrong. NBER working paper, no. 23795, September 2017.Google Scholar
 Warne, A. (2015). YADA manual—Computational details. http://www.texlips.net/download/yada.pdf. Accessed 23 Feb 2018.
 Wieland, V., Cwik, T., Mueller, G. J., Schmidt, S., & Wolters, M. (2012). A new comparative approach to macroeconomic modeling and policy analysis. Journal of Economic Behavior & Organization, 83(3), 523–541.CrossRefGoogle Scholar
Copyright information
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.