Volume 66, Issue 1

This paper examines five possible explanations for the Great Recession of 2008 and 2009, using data for the United States and the eurozone. Of these five hypotheses, four are not supported by the data, while the fifth appears reasonable.

This paper provides novel evidence on the time varying impact of government spending shocks on output in Germany over the years 1970 to 2013. In a first step, I use an expectations-augmented vector autoregressive model with time varying parameters (TVP-VAR) to show that fiscal multipliers are not stable over time but exhibit a ushaped pattern. While multipliers fluctuate around 2 at the beginning and end of the sample, they are much smaller in between. In a second step, I discuss which factors determine the magnitude of German multipliers and hence explain the observed variation. It turns out that fiscal policy is more effective when business uncertainty is high but less in periods of financial market stress, while the state of the business cycle is of minor importance. Moreover, I find that fiscal sustainability is a crucial determinant of the multipliers. I conclude that policy recommendations based on average multipliers are misleading.

This paper analyzes the effect of planned fiscal consolidation on GDP growth forecast errors from the years 2010-2013 using cross section analyses and fixed effects estimations. Our main findings are that fiscal multipliers have been underestimated in most instances for the year 2011 while we find little to no evidence for the years 2010 and especially the latter years 2012/13. Since the underestimation of fiscal multipliers seems to have decreased over time, it may indicate learning effects of forecasters. However, the implications for fiscal policy should be considered with caution as a false forecast of fiscal multipliers does not confirm that austerity is the wrong fiscal approach but only suggests a too optimistic assessment of fiscal multipliers for the year 2011.

We tackle the nowcasting problem at the regional level, using a large set of indicators (regional, national and international) for the years 1998 to 2013. We explicitly take into account the ragged-edge data structure and consider the different information sets faced by a regional forecaster within each quarter. It appears that regional survey results in particular improve forecasting accuracy. Among the 10% best performing models for the short forecasting horizon, one fourth contain regional indicators. Hard indicators from the German manufacturing sector and the Composite Leading Indicator for Europe also deliver useful information for the prediction of regional GDP in Saxony. Unlike national GDP forecasts, the performance of regional GDP is similar across different information sets within a quarter.

There is a broad literature on determinants of house price dynamics, which received increasing attention in the aftermath of the subprime crisis. Additional to macroeconomic standard variables, there might be other hard to measure or even unobservable factors influencing real estate prices. Using quarterly data, we try to increase the informational input of conventional models and capture such effects by including Google search engine query information into a set of standard fundamental variables explaining house prices. We use the house price index (HPI) published by Eurostat to perform fixed-effects regressions for a panel of 14 EU-countries comprising the years 2005-2013. We find that Google data as a single aggregate measure plays a prominent role in explaining house price developments.