Monetary Policy Modelling in Times of Financial Turmoil: The Case of Sveriges Riksbank. Jens Iversen Stefan Laséen Henrik Lundvall Ulf Söderström

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1 Monetary Policy Modelling in Times of Financial Turmoil: The Case of Sveriges Riksbank Jens Iversen Stefan Laséen Henrik Lundvall Ulf Söderström Preliminary and incomplete February, Abstract We evaluate the usefulness of DSGE models for practical forecasting and monetary policy analysis, with a focus on the recent nancial crisis. We do this in two steps. First, we evaluate real-time forecasts from Sveriges Riksbank s DSGE model Ramses and a BVAR model since 7, and compare these with the judgemental forecasts published by the Riksbank. Second, we compare model forecasts during the nancial crisis using models with and without nancial frictions to evaluate the usefulness of such frictions to capture nancial turmoil. Our results suggest that nancial frictions are potentially useful for forecasting and monetary policy analysis, especially during nancial crises. However, in the case of Sweden, careful modelling of the international outlook seems more important for forecast precision. Keywords: DSGE model, Forecasting, Financial crisis, Monetary policy. JEL Classi cation: E7, E, E. Modelling Division, Monetary Policy Department, Sveriges Riksbank, SE- 7 Stockholm, Sweden. Corresponding author: Ulf Söderström, ulf.soderstrom@riksbank.se. This paper was prepared for the conference Macroeconomic Modeling in Times of Crisis organized by the Banque de France, the Federal Reserve Bank of Atlanta, CEPREMAP and CAS on October 6,. We would like to thank Christopher Erceg and seminar participants at the above mentioned conference for helpful comments and suggestions. We are grateful to Henrik Siverbo for research assistance. The views expressed in this paper are solely the responsibility of the authors and should not be interpreted as re ecting the views of the Executive Board of Sveriges Riksbank.

2 Introduction Dynamic Stochastic General Equilibrium (DSGE) models are used at many central banks for forecasting and monetary policy analysis. Sveriges Riksbank (the central bank of Sweden) has routinely used DSGE models since, and the DSGE model Ramses is now the core macroeconomic model at the Monetary Policy Department. This paper describes how Ramses has assisted forecasting and monetary policy analysis at the Riksbank since early 7. The paper focuses on the period since 7, a period that has been characterized by a deep nancial crisis and a global recession, but a relatively strong recovery in Sweden. This period puts DSGE models to a di cult test. Most econometric models have di culties making forecasts in periods of large volatility and enduring recession. But DSGE models have also been heavily criticized for failing to capture the sources and e ects of nancial market volatility in recent years. Our study evaluates the use of DSGE models for forecasting in a policy context, as well as the importance of including nancial frictions in a DSGE model. The paper is divided into two parts. In the rst part, we evaluate the extent to which Ramses has been useful for forecasting purposes. We use a dataset of real-time forecasts of consumer price in ation, GDP growth, the nominal exchange rate, and the repo rate (the Riksbank s interest rate instrument) produced by Ramses and a Bayesian VAR (BVAR) model, along with the forecasts published by the Riksbank. We show that the DSGE model forecasts are competitive with forecasts from the BVAR model, and have provided important input into the Riksbank s nal judgemental forecasts. We also show that conditioning information (such as a short-term forecast and a forecast for international variables) are often useful to improve on the model forecasts, in particular in times of turmoil. Furthermore, using extra-model information to provide additional judgement is particularly useful in volatile times. In the second part of the paper we revisit the peak of the nancial crisis, the fall of 8, and analyze the extent to which nancial frictions are useful to capture the e ects on the economy of nancial variables and shocks. The version of Ramses in use in 8 did not explicitly model nancial frictions and shocks, but a version of the model with such features was introduced in. Using real-time data vintages from the fall of 8 we compare forecasts from the more recent version of Ramses with those from a version of the model where nancial frictions were eliminated. We show that nancial frictions improve on model forecasts when nancial shocks are large. However, these improvements are modest. For a small open economy like Sweden, information on international developments are far more important for forecast precision, also during the

3 nancial crisis. This is consistent with the view that the nancial turmoil was largely generated by international factors leading to a collapse in export demand. At the same time, our results suggest that a model with nancial frictions could have been helpful when forming the short-term forecast, as high interest rate spreads may have contributed to low investment in late 8. Overall, our results demonstrate that DSGE models can provide policymakers with useful information about the outlook for the economy, also in a period of extreme nancial stress. The fact that DSGE models are competitive with BVAR models in terms of forecast precision is encouraging for DSGE model users. But DSGE models have the additional advantage of providing a structural interpretation of incoming data that can be helpful for monetary policy analysis and when developing a narrative used in central bank communication. While our study is largely silent on the bene ts of these features, our own experience suggests that this is at least as important as forecast precision. The paper is organized as follows. In Section we provide some background about the monetary policy framework in Sweden, the process to produce a forecast and monetary policy analysis, and a brief history of DSGE modelling at the Riksbank. In Section we evaluate model forecasts since 7 and compare with the Riksbank s published forecasts. In Section we revisit the fall of 8 and compare the performance of two versions of the DSGE model, with and without nancial frictions. Finally, we sum up and draw some conclusions for future work in Section. Monetary policy and DSGE modelling at the Riksbank. Monetary policy framework Monetary Policy at Sveriges Riksbank is guided by a mandate for price stability that was originally introduced in 99. The Riksbank has speci ed this mandate as a target for in ation of percent, measured in terms of the annual change in the consumer price index (CPI). As the CPI in Sweden measures the cost of housing using mortgage rates, monetary policy decisions are normally guided by in ation measured by the index CPIF, CPI with a xed mortgage rate. In addition to stabilizing in ation around the in ation target, monetary policy also strives to stabilize production and employment around longterm sustainable paths. The Riksbank therefore conducts what is sometimes referred to Until April 8, the Riksbank instead used the index CPIX, de ned as CPI excluding household mortgage interest expenditure adjusted for the direct e ects of changes in indirect taxes and subsidies.

4 as exible in ation targeting. Since 999 decisions on monetary policy are made by an Executive Board with six members, each of whom is individually responsible for his or her decisions. Regular monetary policy meetings are scheduled six times a year. After each meeting, the Riksbank publishes -quarter forecasts for a large number of variables to motivate the monetary policy decision. These forecasts are published in three Monetary Policy Reports after the meetings in February, July, and October, and three Monetary Policy Updates, after the meetings in April, September, and December. Since 7, the Riksbank also publishes a forecast for the main interest rate instrument, the repo rate. Thus, at the monetary policy meeting, the Executive Board votes for the level of the repo rate as well as for the repo rate forecast, or repo rate path, along with the full set of forecasts. Ahead of each monetary policy meeting, sta at the Monetary Policy Department prepares the set of forecasts in a process that is six to eight weeks long. The forecasting process consists of several steps: First, a forecast is produced for the international (trade-weighted) economy: GDP growth, CPI in ation, and a short-term interest rate. Second, short-term forecasts (or nowcasts ) are constructed for a large number of variables, using indicator models and high-frequency data. Third, a set of medium-term forecasts is produced for the main variables (GDP growth, hours worked, CPI and CPIF in ation, the real and nominal exchange rate, and the repo rate). These forecasts are conditioned on the international forecast and the nowcast. Fourth, the small set of forecasts is disaggregated into forecasts for a large number of variables, including the components of GDP, various labor market variables, and various measures of in ation and resource utilization. The forecasts are presented to the Executive Board one to two weeks ahead of the monetary policy meeting, along with the sta s view (or, more recently, a recommendation) on the appropriate level and path for the repo rate. The sta view is informed by various experiments and simulations of Ramses, as well as other models. Subsequently, the Executive Board takes ownership of the forecasts and the Monetary Policy Report or Update, which are published on the day before the monetary policy meeting. The forecasts are supported by a suite of empirical macroeconomic models: the DSGE model Ramses, a Bayesian vector autoregressive (BVAR) model, and the dynamic error-correction model Moses. The macro models are mainly used for the medium-term forecast, but also as input into the nowcast. The nal published fore- See Sveriges Riksbank () for details on the monetary policy framwork in Sweden. See Hallsten and Tägtström (9) for a detailed description of the decision-making process at the Riksbank. See Bårdsen, den Reijer, Jonasson, and Nymoen () for a description of Moses.

5 casts are judgemental, and are produced through an informal combination of the model forecasts and outside judgement. This judgement can be informed by other auxiliary models or by various rules of thumb.. DSGE modelling at Sveriges Riksbank In the early s, the Monetary Policy Department decided to develop an empirical DSGE model to assist forecasting and monetary policy department. The rst version of the DSGE model Ramses was developed in by Adolfson, Laséen, Lindé, and Villani (8), and has been in use at the Monetary Policy Department since. The model was an extension of Christiano, Eichenbaum, and Evans () and Smets and Wouters () to a small open economy, and included a unit-root technology shock, as in Altig, Christiano, Eichenbaum, and Lindé (). The model was estimated with Bayesian techniques on quarterly data series from 98 to, with a break in the monetary policy rule in 99Q, to capture the shift from a xed exchange rate regime to an in ation targeting regime. The second version of Ramses (Ramses II) was developed in 8 9 by Christiano, Trabandt, and Walentin (), and is documented in Adolfson, Laséen, Christiano, Trabandt, and Walentin (). This version of the model is in use since early. Ramses II extended Ramses I in three important respects. First, nancial frictions and a nancial accelerator mechanism were introduced following Bernanke, Gertler, and Gilchrist (999) and Christiano, Motto, and Rostagno (, 8). Second, the model includes equilibrium unemployment using a speci cation with search and matching frictions in the labor market following the version of Gertler, Sala, and Trigari (8) implemented in Christiano, Ilut, Motto, and Rostagno (7). Finally, the model allows imports to enter export production as well as in the aggregate consumption and investment baskets. The model was estimated using data from 99Q to 8Q on 8 series, including the rate of unemployment and the spread between the average corporate loan rate and the 6-month government bond rate. For monetary policy analysis at the Riksbank, the Ramses model is occasionally complemented by other smaller DSGE models, for instance, a model with housing as in Iacoviello and Neri () (see Walentin ()), a model with an explicit banking sector as in Meh and Moran (), and a smaller open economy model building on Galí and Monacelli () and Monacelli (). The acronym Ramses stands for the Riksbank Aggregate Macromodel for Studies of the Economy of Sweden.

6 . Macroeconomic developments in Sweden To summarize the macroeconomic developments in Sweden in recent years, Figure plots annual CPIF in ation, annual GDP growth, the rate of unemployment, the nominal and real exchange rate, and the repo rate since. In 7 the Swedish economy was doing well. Average GDP growth in these years was.7% and unemployment was falling, while in ation was below target, between and.%. Monetary policy was in a tightening phase after a slowdown in. In 8 the economy started slowing down, GDP growth fell and unemployment started to increase. At the same time in ation had increased and was above the % target, and in ation expectations were also increasing. Monetary policy was therefore tightened further, and at the monetary policy meeting of September the repo rate was raised from.% to.7%. When the nancial crisis escalated in September 8, export demand plummeted, and exports fell ve quarters in a row, by at most % in 8Q and.8% in 8Q. GDP therefore fell dramatically and unemployment increased from 6% to close to 9% at the end of 9. In ation kept fairly stable, partly due to a large exchange rate depreciation (the exchange rate weakened by more than % from July 8 to March 9). 6 The repo rate was therefore reduced in a series of steps. On October 8, in between monetary policy meetings, the repo rate was cut to.% in a move coordinated with the Bank of Canada, the Bank of England, the European Central Bank, the Federal Reserve, and the Swiss National Bank. At the meeting of October the repo rate was cut to.7%, and at the meeting of December the repo rate was cut by 7 basis points to %. Eventually the repo rate reached.% in September 9. The acute crisis and the deep recession in 8 9 was followed by a sharp rebound in, with high GDP growth and falling unemployment. This rapid recovery led the Riksbank to start tightening monetary policy, and the repo rate was increased gradually to %. In ation kept falling, however, as the exchange rate strengthened. In the economy has entered a weaker phase. Annual GDP growth was.% in Q, unemployment is slowly increasing, and in ation is low (annual CPIF in ation was.9% in September ). The repo rate has therefore been reduced in three steps from % to.% in September. 6 The trade-weighted exchange rate is measured in terms of the domestic currency price of foreign currency, and is indexed to on November 8, 99, the day before Sweden moved from a xed exchange rate against the ECU to a oating exchange rate regime.

7 Model forecasts and monetary policy since 7 We now investigate the use and usefulness of DSGE models, by comparing forecasts from Ramses and the BVAR model with actual outcomes and with the judgemental forecasts published by the Riksbank. We focus on forecasts of annual consumer price in ation, annual GDP growth, the trade-weighted nominal exchange rate, and the repo rate. We compare the forecasts over the period from February 7 to February, and we evaluate them against data from 7Q to Q. In this period, the Riksbank has published forecasts at 9 occasions. 7 We also divide the period into three sub-periods: before, during, and after the nancial crisis in 8 9, in order to study the role of models and judgement in normal times and in volatile times. We begin by a more traditional test of forecasting performance, by reporting root mean squared errors (RMSE) for model forecasts as well as for the published forecasts. Next, we study the relationship between the model forecasts and the published forecasts, to illustrate the impact models have had in forming the judgemental forecast. Hence, the rst aspect relates model forecast to actual data outcomes whereas the second aspect relates model forecast to published forecasts. In the forecast evaluation exercise, we place special emphasis on the incorporation of external information into the model-based forecasts. Del Negro and Schorfheide () show that external information can improve on forecasting ability of DSGE models. As external information is routinely used at the Riksbank in terms of a short-term forecast (or nowcast) and an international forecast, it is straightforward to evaluate whether this information has improved the forecasting performance of the two models (Ramses and BVAR). We can also study if incorporating external information was more or less important during the nancial crisis. To answer these questions we make use of a unique dataset consisting of real-time forecasts from the two models, with and without the incorporation of external information into the model-based forecasts, and the published forecasts over the period 7Q Q. Thus, in addition to evaluating the usefulness of external information, we are able to evaluate the judgement applied in each forecasting round.. Are DSGE models useful for forecasting? Figures 9 show forecasts from the two models and the published forecasts for annual consumer price in ation, annual GDP growth, the nominal exchange rate and the repo 7 The Riksbank published four forecasts in 7 (in February, June, October, and December), and six forecasts in 8 (in February, April, July, September, October, and December). 6

8 rate. 8 Figures,, 6, and 8 report the actual forecasts on each occasion along with the outcomes, while Figures,, 7, and 9 report RMSEs of each of the ve forecasts. 9 As the conditional forecasts incorporate the nowcast for the current and next quarter, RMSE numbers are reported for horizons from three to twelve quarters. We begin by studying forecasts of annual consumer price in ation in Figures and. The Ramses forecasts tend to be more dispersed than the BVAR forecasts. This is mainly due to the exchange rate forecasts which are rather volatile in Ramses (see Figure 8) and have a signi cant impact on in ation. The Ramses forecasts tend to approach the in ation target of % over time, whereas the BVAR forecasts do not. The published forecasts are always close to % after two years, as monetary policy has focused on returning in ation towards the target after two to three years. Figure reports the RMSE of the in ation forecasts over the full period and three subperiods: prior to the nancial crisis (7Q 8Q), during the nancial crisis (8Q 9Q) and after the crisis (Q Q). Overall, the published judgemental forecasts tend to have lower RMSE than the model forecasts, suggesting that judgement has been useful for the in ation forecasts. Comparing the two models, the conditional Ramses forecasts tend to have lower RMSE than the BVAR forecasts at short horizons, while the BVAR performs better at long horizons. At short horizons, the conditioning information is particularly useful for the Ramses forecasts. Thus, extra-model judgement and conditioning information has shown to be quite useful for the Riksbank s in ation forecasts. This was particularly true during the nancial crisis, but seems less important after the crisis (although there are very few forecasts since ). Figures and analyze forecasts of annual GDP growth. It is immediately clear that all forecasts missed the deep recession in 8 9 but also underestimated the strong recovery in. There are no large di erences in precision across the di erent forecasts. The published forecasts have marginally lower RMSE, so judgement has improved the forecasts to some extent (except during the recovery, where the unconditional BVAR forecasts perform better). Also, the conditioning information is not as important for GDP growth forecasts as for in ation. Figures 6 and 7 show forecasts of the nominal exchange rate. Again, no forecast 8 Consumer price in ation is measured as CPIX (CPI excluding household mortgage interest expenditure adjusted for the direct e ects of changes in indirect taxes and subsidies, previously called UNDX) until February 9, and CPIF (CPI with a xed mortgage rate) from April 9 onwards. 9 The model forecasts that incorporate external information are labelled conditional, while the unconditional forecasts do not incorporate any such information. Until September the Ramses forecasts were conditioned with the international forecasts using unanticipated shocks, but from October the forecasts were introduced using anticipated shocks. 7

9 captured the strong weakening of the exchange rate in 8 9, and the BVAR model was more successful in capturing the subsequent strengthening. The BVAR forecasts have a lower RMSE than the Ramses forecasts and the published forecasts at longer horizons, especially during the nancial crisis, when the Ramses forecasts were often very imprecise. (The exchange rate forecasts in Ramses are also conditioned on an estimate of the long-run real exchange rate, and this estimate increased somewhat during 8 9.) Conditioning has been important for both Ramses and the BVAR model, especially since 8, while it is less clear that judgement was useful for the exchange rate forecasts. Finally, Figures 8 and 9 evalute forecasts of the repo rate. Evaluating the published repo rate forecasts is complicated by the fact that the Riksbank sets the repo rate itself. Thus, the repo rate path can seen as both a forecast and as an instrument for monetary policy. For the model forecasts, those produced by Ramses tend to return towards the steady-state level (which is close to %) more quickly than the BVAR forecasts. Instead, the BVAR forecasts are more closely related to international interest rates, which have been depressed since mid-9. All forecasts perform similarly in terms of RMSE. On average, the BVAR forecasts are marginally more precise than the published forecasts and those from Ramses, but since 8 the published forecasts have a lower RMSE than the model forecasts. Again, the extra-model conditioning information has been important to improve the precision of the model forecasts, especially for Ramses after 8. The unconditional forecasts from Ramses seem to overpredict the outcomes to a larger degree than the conditional forecasts. The accuracy of the BVAR forecast, on the other hand, has not been much a ected by incorporating external information. Overall, this exercise suggests that DSGE model forecasts are competitive with forecasts from a BVAR model, a result that is encouraging for DSGE model users. The results also show that incorporating external information can substantially improve on the model forecasts, typically more so for the DSGE model than for the BVAR model. This is particularly important in turbulent times, when (as in this case) the external information may suggest that the return to normal levels will take longer than usual. Finally, judgemental adjustments of model-based forecasts can sometimes improve forecast precision substantially. Again, these gains seem larger during the nancial crisis and the subsequent recovery than during more normal times. This nding con rms the results in Del Negro and Schorfheide (). 8

10 . Are DSGE model forecasts useful for monetary policy? Having discussed whether DSGE models are useful for forecasting, we now move to a second potential task of DSGE models: their role in providing monetary policy advice and assist in story-telling. We evaluate the usefulness of model-based forecasts for monetary policy analysis by studying how closely related these are to the published judgemental forecasts. The idea is that if policymakers nd a model forecast useful, they will tend to publish a judgemental forecast that is more similar to the model forecast. Figures report root mean squared deviations (RMSD) of the four model forecasts from the published forecasts for in ation, GDP growth, the nominal exchange rate, and the repo rate. To capture the persistence in the published forecasts we also include the previous published forecast in the comparison. For the in ation forecasts it is not clear which model has been more in uential for the published judgemental forecast. Especially at longer horizons, the previous forecast has been more closely related to the published forecasts, re ecting the fact that in ation forecasts at the - to -year horizon tend to be close to the % in ation target. During 8 9 the incorporation of external information into the Ramses forecasts has increased their correlation with the published forecasts, but since the external information has not left any impression on the published forecasts, which are more closely related to the unconditional Ramses forecasts. For the GDP growth forecasts in Figure a similar picture emerges: all model forecasts have a similar impact on the published forecasts, and there is substantial persistence in the forecasts. However, for the exchange rate forecasts in Figure the model-based forecasts were quite useful, especially the Ramses forecasts that incorporate external information. Overall, the Ramses forecasts have been more closely related to the published forecasts than the BVAR model forecasts, and the conditioning information has also had an important impact on the published forecasts. The same is also true for the repo rate forecasts in Figure. Again, the conditional forecasts made by Ramses are closely related to the published judgemental forecasts. This evidence suggests that the DSGE model forecasts have been seen as useful when deciding on the forecast for the repo rate. 9

11 Are models with nancial frictions helpful in times of nancial turmoil? During the fall of 8, the core model used at the Riksbank (Ramses I) did not explicitly model nancial frictions or shocks. The current version of the model (Ramses II) in use since instead does include nancial frictions. In this section, we therefore revisit the fall of 8 to study whether a model with nancial frictions could have improved forecasts and policy analysis during the crisis. We reconstruct model forecasts for the monetary policy meetings in September, October, and December 8. We also study in more detail the importance for model forecasts of external conditioning information, in terms of the nowcast for the current (and next) quarter(s) and a forecast for the international (trade-weighted) economy. Comparing forecasts from a version of the model with and without nancial frictions, we show that explicitly including nancial frictions marginally improves on forecast precision, although neither model is able to predict the sharp slowdown in economic activity in late 8 and early 9. However, a model with nancial frictions could have been useful to support the construction of the nowcast for investment in late 8. This, in turn, could have improved forecast precision and monetary policy analysis during the nancial crisis. To some extent, it is perhaps not surprising that a model with domestic nancial frictions and shocks does not capture the depth of the nancial crisis in Sweden. The crisis largely hit Sweden through the external sector, leading to a large drop in exports and therefore investment. Domestic nancial shocks were probably less important for the Swedish experience. We therefore move on to quantify the impact of conditioning information on the forecasts during the fall of 8. We con rm results from Section that accurate conditioning information is crucial for forecast precision. This is particularly important concerning information about the international outlook.. The importance of nancial frictions We begin by examining the importance of nancial frictions and shocks for model forecasts during the fall of 8. We compare forecasts and policy advice from two versions of Ramses II. The rst version is the current version that includes nancial frictions and a shock to the nancial sector (to entrepreneur net worth). The second version removes the nancial frictions block from the model. Both models are estimated on This second version is thus not identical to the model in use before, as the two versions of Ramses di er also in other regards.

12 data from 99Q to 7Q, using the data vintage available in April 8. For the model with nancial frictions, we use the same 8 data series used when estimating the o cial version of Ramses II. For the model without nancial frictions we exclude data on the corporate loan spread. With these models we compare three sets of forecasts:. Forecasts based on observed data only (unconditional forecasts).. Forecasts conditional on a nowcast for the current (and sometimes the next) quarter.. Forecasts conditional on observed data, but where the nancial shock (to entrepreneur net worth) is calibrated to obtain the nowcast for the corporate spread. We need to be careful about what nancial information we assume is available at each forecasting date. The data on the corporate loan spread used for estimation are published monthly, with a delay of around one month. Thus, it does not capture highfrequency movements in nancial market data that is potentially useful for forecasting and monetary policy advice. But in real time, policymakers had access to such highfrequency data, and could use it to construct the short-term forecast for the corporate loan spread. To capture this, we use daily observations of the three-month TED spread, the spread between the three-month interbank rate and the three-month treasury bill, until the day before the monetary policy meeting. Assuming that the TED spread remains at this level during the nowcast quarter(s), the nowcast for the corporate loan is set equal to the value in the previous quarter plus the quarterly increase in the TED spread. The second set of forecasts will incorporate the nowcast for all observable variables in the model, including the corporate spread. When observing an increase in the corporate spread, the model will assign this to a combination of shocks, not only the nancial shock. To more precisely capture the impact of the nancial shock, the third set of forecasts will assign the entire increase in the spread to the nancial shock. To set the stage, Figure shows the development of a few nancial market variables from to. All interest rate spreads were very low until mid-7, when the nancial crisis rst erupted. Mortgage spreads started to increase and the stock market fell over the summer of 7, while money market spreads did not increase until August 7. In September 8 all spreads increased dramatically and stayed elevated until early 9. At the same time the stock market fell further and bottomed out in early

13 9. (This behavior is similar to that of the nominal exchange rate in Figure.) The corporate loan spread increased slowly in the rst two quarters of 8 and then further in the third and forth quarter. Most of the movements in interest rate spreads and nancial market variables were closely related to movements in international nancial markets. Thus, much of the volatility was not directly related to Swedish factors... September 8 Ahead of the monetary policy meeting on September, 8, the Riksbank forecast high in ation going forward primarily because of elevated oil and international food prices. At the same time, GDP growth had started to slow down. Even if tension was high in nancial markets, it had eased a bit over the summer and several interest rate spreads had started to come down. To counteract high in ation and rising in ation expectations, the Executive Board voted to increase the repo rate from. to.7 percent. Figures 7 show three sets of forecasts from the models with nancial frictions (denoted FF ) and without nancial frictions (denoted NoFF ). The unconditional forecasts in Figure are based on information up until 8Q, and are close to identical for the two models. The s slightly lower hours worked and investment growth but slightly higher in ation. The repo rate forecast is practically the same in the two models. (The monetary policy rule assumes that the repo rate is set as a function of the level and rst di erence of CPIF in ation and hours worked.) The small di erence between the forecasts is partly due to the fact that the corporate loan spread was fairly low through the second quarter. It also suggests that the nancial accelerator mechanism in the FF model is weak, so that non- nancial shocks propagate in roughly the same way in the two models. Next, Figure 6 shows forecasts conditional on the nowcast for the third and fourth quarters. The nowcast for the corporate spread is updated using daily observations of the TED spread until September. As the TED spread had fallen over the summer, the nowcast for the corporate spread that we use is also falling slightly relative to the second quarter. There is therefore a very small di erence between the two model forecasts. The third forecast (shown in Figure 7) is based on data until 8Q, but we The stock market index is closely correlated with the various interest rate spreads (and with the exchange rate). Matching the model to the behavior of stock prices is therefore likely to give similar results to those below. See Hopkins, Lindé, and Söderström (9) for an overview of nancial market volatility in Sweden during the crisis. This decision was highly debated at the time, also within the Executive Board. The Executive Board vote was split against, and the Governor cast the deciding vote. The minutes from all monetary policy meetings are available on the Riksbank website,

14 calibrate the nancial shock (to entrepreneurial net worth) to obtain the corporate spread in the nowcast quarters. Again, there are small di erences between the model forecasts. The s slightly higher in ation and investment and lower hours worked. It therefore forecasts a higher repo rate than the model without nancial frictions. Thus, although there are some di erences between the forecasts from the two models in September 8, these di erences are small, and both models make large forecast errors for investment and GDP in late 8 and early 9. It is therefore unlikely that a model with nancial frictions would have given very di erent advice ahead of the monetary policy meeting in September 8... October 8 After the collapse of Lehman Brothers on September, 8, interest rate spreads increased dramatically all over the world, and also in Sweden. At the same time, the krona exchange rate weakened abruptly and the stock market fell. From early September to early October the TED spread doubled from 7 to basis points, the krona weakened by percent and the stock market fell by percent. On October 8, in a coordinated move with several other central banks, the Riksbank lowered the repo rate by basis points to. percent. The Riksbank was also in the process of conducting various measures to stabilize domestic nancial markets. Ahead of the regular monetary policy meeting on October, interest rate spreads had increased further, the krona had continued to weaken and the stock market was still depressed. The Executive Board therefore decided to lower the rate by another basis points to.7 percent. The unconditional model forecasts shown in Figure 8 are again based on data only up to 8Q. As in September the forecasts from the FF and NoFF models are therefore similar. The di erence between the unconditional forecasts in September and October 8 is only due to data revisions between these two points in time. The forecasts conditional on the nowcast for the third and fourth quarters in Figure 9 show that the s lower in ation, investment, and hours worked compared with the NoFF model, implying a lower repo rate. In contrast to the September forecasts there is now a material di erence between the two model forecasts, and the repo rate forecast is close to basis points lower in the FF model towards the end of the forecast period. This is because the nowcast for the corporate loan spread has increased dramatically due to the elevated TED spread in the third quarter and the rst part of October. However, neither model predicts the collapse in investment, GDP, and hours worked in the fourth quarter, and the eventual drop in the repo rate to basis

15 points in mid-9. One reason is that the is constrained by the nowcast for investment. To account for the increase in the spread in the fourth quarter, the FF model lters out a substantial negative innovation to the entrepreneurial net worth shock, which implies a fall in investment growth that is much larger than in the nowcast. The relatively high nowcast is then interpreted as a positive investment-speci c technology shock, which raises the investment forecast. One interpretation is that if the Riksbank had used a model with an explicit link between interest rate spreads and economic activity in the fall of 8, the nowcast for investment might have been reduced, and the forecast more in line with the actual economic development. To let the nancial shock speak more freely, Figure shows the forecasts of the FF model based on data until 8Q but where the net worth shock is chosen to match the increase in the corporate spread in the nowcast quarters. This implies a substantial decrease in investment of between four and ve percent in the fourth quarter, well in line with the realized value. The model also captures the slow recovery of investment from 9Q and onwards. However, the fall in GDP growth and hours worked is modest. While a large negative shock to entrepreneurs net worth reduces investment, it has mildly positive e ects on consumption, small e ects on exports, and it reduces imports substantially (due to the high import content of investment spending)... December 8 Because of a substantial worsening of the economic situation during the late fall of 8, resource utilization and in ationary pressure were low ahead of the meeting on December, and were forecast to be low going forward. Financial tension had eased somewhat. The TED spread had peaked just before the October meeting, and had by late November fallen by some basis points, and the krona had continued to weaken. There were clear signs of a slowdown in economic activity, however, and the repo rate was reduced by 7 basis points to percent. The unconditional forecast is now based on data through the third quarter, so the FF model sees the actual increase in the corporate loan spread that occurred in that quarter. This pushes down the investment forecast relative to the NoFF model, see Figure. The FF model also forecasts a more negative development of hours worked and in ation compared with the NoFF model, and a lower repo rate path. However, the di erences are again small: the largest di erence between the repo rate forecasts is less than basis points. The nowcast includes data only on the fourth quarter. Comparing Figures 9 and shows that the nowcast for in ation and GDP growth were reduced between October

16 and December. Nevertheless, the two model forecasts are not much a ected, and the is if anything more optimistic in December than in October. A likely reason for this is that our nowcast for the corporate spread is actually lower in December, as the TED spread had fallen. The weaker exchange rate also acts to stimulate the economy and raise in ation. Also for the forecast conditional on the calibrated net worth shock in Figure, the di erence between the two model forecasts is roughly the same as in October, and the forecasts are slightly more optimistic in December than in October... Summary The results so far in this Section con rm those from the previous section that having the appropriate conditioning information is important. Using a model with nancial frictions could have helped understand better the link between nancial market developments and the real economy. This could have improved the investment forecast at a crucial moment. However, even with an appropriate view on nancial market developments, the model with nancial frictions model would not have predicted the substantial collapse in economic activity that occurred. Partly, this could be because a contraction in entrepreneurial net worth provokes a drop in investment but also an increase in net exports, as a large fraction of the lower investment spending is a fall in imports. In practice, the contraction during the nancial crisis was largely due to international factors and a drop in export demand. This suggest that shocks to the external sector were important to explain the recession in Sweden. In the next subsection we therefore investigate the importance of conditioning on international factors.. The impact of the international outlook In this section we elaborate on the nding in Section that conditioning information is important for forecast precision. For small open economies it might be just as important to obtain an appropriate view of foreign developments as having a correct understanding of the domestic economy. We use the model with nancial frictions to study the importance of extra-model conditioning information, much like in Section but focusing on the forecasts made during the fall of 8. Again, we compare three di erent forecasts:. Forecasts based on observed data only (the same unconditional forecasts as in the rst part of the section).. Forecasts conditional on the nowcast and the Riksbank forecast for the international economy (using unanticipated shocks)

17 . Forecasts conditional on the nowcast and the realized paths for the international economy (using unanticipated shocks). The unconditional forecasts based on information available in September 8 as well as the forecast conditional on the nowcast and the international forecast in Figure do not predict the substantial drop in economic activity that occurred over the end of 8 and beginning of 9. However, conditioning on the true economic development in the international (trade-weighted) economy goes some way towards capturing this drop. For instance, conditioning on the developments in the foreign economy implies a substantial fall in both in ation and hours worked. The in ation forecast is lower than the realized values for most of 9 and into. Hence, the repo rate is also forecast to fall considerably and reaches a minimum of percent towards the end of 9 (the realized value was. percent). Also the forecasts of export growth and domestic in ation (not shown) are quite close to the realized values. However, the forecast of investment (as well as private consumption) seem worse when conditioning on the foreign economic development. For October 8, the forecast are more depressed than in September, see Figure. The repo rate forecast is reduced, especially those conditioned on the international forecast. In December, the forecasts are similar to October, see Figure 6. Overall, comparing the three forecasts in Figures 6 shows that getting an accurate view on international developments is crucial for a small open economy like Sweden. This is particularly true to capture the drop in hours worked and the reduction in the repo rate. This low in ation forecast is due to a forecast for a strong exchange rate appreciaton. In practice, the exchange rate depreciated substantially. 6

18 Conclusions and nal remarks Overall, the results in this paper demonstrate that DSGE models can provide policymakers with useful information about the outlook for the economy, also in a period of extreme nancial stress. Comparing real-time forecasts from a DSGE model with a BVAR model, we show that the DSGE model forecasts are competitive with the BVAR forecasts in terms of forecast precision, also during the nancial crisis. This is encouraging for DSGE model users. We also show that the DSGE model forecasts have provided important input into the Riksbank s nal judgemental forecasts, and that conditioning information (such as a short-term forecast and a forecast for international variables) are often useful to improve on the model forecasts, in particular in times of turmoil. Comparing forecasts from DSGE models with and without nancial frictions, we show that nancial frictions improve on model forecasts when nancial shocks are large, but these improvements are often modest. At the same time, our results suggest that a model with nancial frictions could have been helpful when forming the short-term forecast, as high interest rate spreads may have contributed to low investment in late 8. Finally, we nd that information on international developments seems more important for forecast precision than nancial frictions and shocks, at least for the Swedish economy during the nancial crisis. This is consistent with the view that the nancial turmoil was largely generated by international factors leading to a large drop in export demand. In addition to forecast precision, DSGE models have the advantage of providing a structural interpretation of incoming data that can be helpful for monetary policy analysis and when developing a narrative used in central bank communication. While our study is largely silent on the bene ts of these features, our own experience suggests that this is at least as important as forecast precision. 7

19 References Adolfson, Malin, Stefan Laséen, Lawrence J. Christiano, Matthias Trabandt and Karl Walentin (), Ramses II Model description, Occasional Paper No. XX, Sveriges Riksbank. Adolfson, Malin, Stefan Laséen, Jesper Lindé, and Mattias Villani (8), Evaluating an estimated new Keynesian small open economy model, Journal of Economic Dynamics and Control (8), Altig, David E., Lawrence J. Christiano, Martin Eichenbaum, and Jesper Lindé (), Firm-speci c capital, nominal rigidities and the business cycle, Review of Economic Dynamics (), 7. Bårdsen, Gunnar, Ard den Reijer, Patrik Jonasson, and Ragnar Nymoen (), MOSES: Model for studying the economy of Sweden, Economic Modelling 9 (6), Bernanke, Ben, Mark Gertler, and Simon Gilchrist (999), The nancial accelerator in a quantitative business cycle framework, in Handbook of Macroeconomics, edited by John B. Taylor and Michael Woodford, Elsevier Science. Christiano, Lawrence J., Martin Eichenbaum, and Charles L. Evans (), Nominal rigidities and the dynamic e ects of a shock to monetary policy, Journal of Political Economy (),. Christiano, Lawrence J., Cosmin Ilut, Roberto Motto, and Massimo Rostagno (7), Monetary policy and stock market boom-bust cycles, Unpublished manuscript, Northwestern University. Christiano, Lawrence J., Roberto Motto, and Massimo Rostagno (), The great depression and the Friedman-Schwartz hypothesis, Journal of Money, Credit, and Banking (6, Part ), Christiano, Lawrence J., Roberto Motto, and Massimo Rostagno (8), Shocks, structures or monetary policies? The euro area and US after, Journal of Economic Dynamics and Control (8), Christiano, Lawrence J., Matthias Trabandt and Karl Walentin (), Introducing nancial frictions and unemployment into a small open economy model, Journal of Economic Dynamics and Control (),

20 Del Negro, Marco and Frank Schorfheide (), DSGE model-based forecasting, Sta Report No., Federal Reserve Bank of New York. Forthcoming, Handbook of Economic Forecasting, Volume. Galí, Jordi and Tommaso Monacelli (), Monetary policy and exchange rate volatility in a small open economy, Review of Economic Studies 7 (), Gertler, Mark, Luca Sala, and Antonella Trigari (8), An estimated monetary DSGE model with unemployment and staggered nominal wage bargaining, Journal of Money, Credit, and Banking (8), Hallsten, Kerstin and Sara Tägtström (9) The decision making process How the Executive Board of the Riksbank decides on the repo rate, Sveriges Riksbank Economic Review 9:, Hopkins, Elisabeth, Jesper Lindé, and Ulf Söderström (9) The transmission mechanism and the nancial crisis, Sveriges Riksbank Economic Review 9:, 7. Iacoviello, Matteo and Stefano Neri (), Housing market spillovers: Evidence from an estimated DSGE model, American Economic Journal: Macroeconomics (), 6. Meh, Césaire and Kevin Moran (), The role of bank capital in the propagation of shocks, Journal of Economic Dynamics and Control (), 76. Monacelli, Tommaso (), Monetary policy in a low pass-through environment, Journal of Money, Credit and Banking 7 (6), Rudebusch, Glenn D. (6), Monetary policy inertia: Fact or ction?, International Journal of Central Banking (), 8. Smets, Frank and Raf Wouters (), An estimated dynamic stochastic general equilibrium model of the euro area, Journal of the European Economic Association (), 7. Smets, Frank and Raf Wouters (7), Shocks and frictions in U.S. business cycles: A Bayesian DSGE approach, American Economic Review 97 (), Sveriges Riksbank (), Monetary Policy in Sweden. Walentin, Karl (), Housing collateral and the monetary transmission mechanism, Unpublished manuscript, Sveriges Riksbank. 9

21 Figure. Macroeconomic developments in Sweden, - CPIF inflation Annual percentage change 8 GDP growth Annual percentage change, seasonally adjusted data 9 Unemployment Percent of the labor force, aged -7, seasonally adjusted data Repo rate Percent 6 Trade-weighted nominal exchange rate (TCW) Index, 8 November 99 = Trade-weighted real exchange rate (TCW) Index, 8 November 99 = Source: National sources, Statistics Sweden and Sveriges Riksbank

22 Figure. Annual CPIX/CPIF inflation Q-Q and forecasts 7Q-Q Riksbank Note: BVAR Endo and Ramses Endo denote unconditional forecasts from the Bayesian VAR model and the DSGE model Ramses. BVAR Cond and Ramses Cond denote forecasts from the Bayesian VAR model and the DSGE model Ramses conditional on a two-quarter nowcast for all variables and a forecast of trade-weighted foreign interest rates, GDP growth, and inflation. Source: Statistics Sweden and Sveriges Riksbank.