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Recently, much attention has been devoted to the measurement of macroeconomic (expectation) uncertainty and its impact on aggregate economic fluctuations. This paper presents a new qualitative measure of macroeconomic expectation uncertainty based on data from a German online survey of consumer expectations. I document that the survey design works well. Elicited expectation uncertainty is related to data volatility and conventional measures of uncertainty as expected. Its dependency on socioeconomic factors is in line with previous evidence based on quantitative uncertainty measures. The new measure offers a very efficient way of eliciting expectation uncertainty and can be used to obtain uncertainty measures on many different expectations at low cost.

We propose to treat survey-based density expectations as compositional data when testing either for heterogeneity in density forecasts across different groups of agents or for changes over time. Monte Carlo simulations show that the proposed test has more power relative to both a bootstrap approach based on the KLIC and an approach which involves multiple testing for differences of individual parts of the density. In addition, the test is computationally much faster than the KLIC-based one, which relies on simulations, and allows for comparisons across multiple groups. Using density expectations from the ECB Survey of Professional Forecasters and the U.S. Survey of Consumer Expectations, we show the usefulness of the test in detecting possible changes in density expectations over time and across different types of forecasters.

Using new survey data on quantitative growth expectations of firms in Germany, we show that firms resort to local information when forming expectations about aggregate growth. Firms extrapolate from the economic situation in their county, industry growth and their individual business situation. The effect is particularly strong for small firms and explains part of the high expectation dispersion across firms. Furthermore, we show that growth expectations are correlated with employment and investment decisions of firms, highlighting that differences in expectations do indeed seem to lead to differences in actual firm decisions. Our results confirm predictions of theoretical models with rational inattention.

We analyze the effectiveness of the German tax reduction on fuel prices ('Tankrabatt') that was introduced for three months, starting on 1 June 2022. Using the synthetic control method to compare actual prices of gasoline and diesel to those in a counterfactual situation without the tax reduction, we find that the tax reduction has been completely passed on to consumers for most of the three months. In early June, it took approximately two weeks for the full pass-through to take effect. Moreover, pass-through rates started to decline in August while the tax reduction was still in place. We observe an upward price jump smaller than the size of the expiring tax reduction at the start of September. Our results are robust to different approaches of constructing the synthetic control group.

Established tests for proper calibration of multivariate density forecasts based on Rosenblatt probability integral transforms can be manipulated by changing the order of variables in the forecasting model. We derive order-invariant tests. The new tests are applicable to densities of arbitrary dimensions and can deal with parameter estimation uncertainty and dynamic misspecification. Monte Carlo simulations show that they often have superior power relative to established approaches. We use the tests to evaluate generalized autoregressive conditional heteroskedasticity-based multivariate density forecasts for a vector of stock market returns and macroeconomic forecasts from a Bayesian vector autoregression with time-varying parameters. (VLID)5657739 Version of record

Expectations are highly relevant for macroeconomic dynamics. Yet, the empirical evidence about properties of corporate macroeconomic expectations is scarce. Using new survey data on quantitative growth expectations of firms in Germany, we show that expectations are highly dispersed. The degree of dispersion depends on firm size and on how important the general economy is for the business of firms, supporting theories of rational inattention. Firms seem to extrapolate from local economic conditions and business experiences to aggregate growth expectations. Differences in growth expectations are associated with di erences in firms' Investment and labor demand. Not Reviewed

Releases of key macroeconomic indicators are closely watched by financial markets. We investigate the role of expectation dispersion and economic uncertainty for the stock-market reaction to indicator releases. We find that the strength of the financial market response to news decreases with the preceding dispersion in expectations about the indicator value. Uncertainty, in contrast, increases the response. We rationalize our findings in a model of imperfect information. In the model, dispersion results from a perceived weak link between macroeconomic indicators and fundamentals that reduces the informational content of indicators, while higher fundamental uncertainty makes this informational content more valuable. Not Reviewed

This paper studies the determinants of firms' business outlook and managerial mitigation strategies in the wake of the COVID-19 crisis using a representative panel of German firms. We first demonstrate that the crisis amplifies pre-crisis weaknesses: Firms that appear relatively weak before the crisis are harder hit initially, and, on top of the initial impact, expect more difficulties for their businesses going forward. Consequently, such firms are first to cut employment and investment. Second, our results highlight that expectations regarding the duration of the shutdown—which, at this point of the crisis, exhibit plausibly random variation—are an important determinant of the chosen mitigation strategies: Firms that expect the shutdown to last longer are more likely to lay off workers and to cancel or postpone investment projects.

Using European Commission real-time data, we show that potential output (PO) estimates were substantially and persistently revised downwards after the Great Recession. We decompose PO revisions into revisions of the capital stock, trend labor, and trend total-factor productivity (TFP). Initially, trend TFP revisions contribute most to the overall PO revisions while all three components are almost equally important in the longer run. Revisions of the capital stock happen quickly while revisions of trend labor, mainly driven by revisions of the non-accelerating wage rate of unemployment (NAWRU), are made gradually. The relative contributions of the components to overall PO revisions differ systematically across countries. This suggests that heterogeneous policies are needed to push different countries back to their previous growth paths.

Established tests for proper calibration of multivariate density forecasts based on Rosenblatt probability integral transforms can be manipulated by changing the order of variables in the forecasting model. We derive order invariant tests. The new tests are applicable to densities of arbitrary dimensions and can deal with parameter estimation uncertainty and dynamic misspecification. Monte Carlo simulations show that they often have superior power relative to established approaches. We use the tests to evaluate GARCH-based multivariate density forecasts for a vector of stock market returns.

This paper analyses the distribution of long-term inflation expectations in the euro area using individual density forecasts from the ECB Survey of Professional Forecasters. We exploit the panel dimension in this dataset to examine whether this distribution became less stable following the Great Recession, subsequent sovereign debt crisis and period when the lower bound on nominal interest rates became binding. Our results suggest that the distribution did change along several dimensions. We document a small downward shift in mean long-run expectations toward the end of our sample although they remain aligned with the ECB definition of price stability. More notably, however, we identify a trend toward a more uncertain and negatively skewed distribution with higher tail risk. Another main finding is that key features of the distribution are influenced by macroeconomic news, including the ex post historical track record of the central bank.

We analyze when and how much OECD estimates of potential output are revised after recessions and which factors explain the size of these revisions. Inter alia, we find that following a recession, the OECD substantially revises downwards potential output, those revisions are larger than what is to be expected under the assumption of no hysteresis, and the recession depth, the primary balance, and the current account balance are predictors of post-recession revisions of potential output.

This paper analyzes how the OECD revises potential output (PO) estimates after recessions. We show that downward revisions are substantial and mostly driven by supply shocks while PO estimates do not significantly react to demand shocks. In addition, revisions are partly caused by avoidable mismeasurement of PO before recessions. In particular, we show that the length of the preceding boom and pre-recession values of the current account balance and credit volumes are predictors of post-recession PO revisions. Our results call for improved methods for estimating PO and provide evidence against the existence of substantial hysteresis effects of demand shocks.

We derive new tests for proper calibration of multivariate density forecasts based on Rosenblatt probability integral transforms. These tests have the advantage that they i) do not depend on the ordering of variables in the forecasting model, ii) are applicable to densities of arbitrary dimensions, and iii) have superior power relative to existing approaches. We furthermore develop adjusted tests that allow for estimated parameters and, consequently, can be used as in-sample specification tests. We demonstrate the problems of existing tests and how our new approaches can overcome those using Monte Carlo Simulation as well as two applications based on multivariate GARCH-based models for stock market returns and on a macroeconomic Bayesian vectorautoregressive model.

The 2030 Agenda for Sustainable Development includes a set of 17 sustainable development goals (SDG) with 169 specific targets. As such, it could be a step forward in achieving efficient governance and policies for global sustainable development. However, the current indicator framework with its broad set of individual indicators prevents straightforward assessment of synergies and trade-offs between the various indicators, targets, and goals, thus, heightening the significance of policy guidance in achieving sustainable development. With our detailed analysis of SDG 14 (Ocean) for European Union (EU) coastal states, we demonstrate how the (complementary) inclusion of composite indicators that aggregate the individual indicators by applying a generalized mean can provide important additional information and facilitate the assessment of sustainable development in general and in the SDG context in particular. Embedded in the context of social choice theory, the generalized mean varies the specification of substitution elasticity and thus allows: (a) for a straightforward distinction between a concept of weak and strong sustainability and (b) for straightforward sensitivity analysis. We show that while in general the EU coastal states have a fairly balanced record at the SDG 14 level, certain countries like Slovenia and Portugal with a fairly balanced and a fairly unbalanced showing, respectively, rank very differently in terms of the two concepts of strong sustainability.

We derive new tests for proper calibration of multivariate density forecasts based on Rosenblatt probability integral transforms. These tests have the advantage that they i) do not depend on the ordering of variables in the forecasting model, ii) are applicable to densities of arbitrary dimensions, and iii) have superior power relative to existing approaches. We furthermore develop adjusted tests that allow for estimated parameters and, consequently, can be used as in-sample specification tests. We demonstrate the problems of existing tests and how our new approaches can overcome those using two applications based on multivariate GARCH-based models for stock market returns and on a macroeconomic Bayesian vectorautoregressive model.

We analyze how modeling international dependencies improves forecasts for the global economy based on a Bayesian GVAR with SSVS prior and stochastic volatility. To analyze the source of performance gains, we decompose the predictive joint density into its marginals and a copula term capturing the dependence structure across countries. The GVAR outperforms forecasts based on country-specific models. This performance is solely driven by superior predictions for the dependence structure across countries, whereas the GVAR does not yield better predictive marginal densities. The relative performance gains of the GVAR model are particularly pronounced during volatile periods and for emerging economies.

The 2030 Agenda for Sustainable Development that includes a set of 17 Sustainable Development Goals (SDG) with 169 specific targets could be a step forward in achieving efficient governance and policies for global sustainable development. An essential element will be the global indicator framework for monitoring and assessing progress over and against both the overall goals and the specific targets and to guide policy towards sustainable solutions. In the debate over the current indicator framework, little attention is devoted to conceptual issues. Here, we argue that the inclusion of composite indicators, which can be used to aggregate individual indicators, as complements to the single indicators could support the overall assessment process without necessitating any significant changes to the currently proposed indicator base. While the individual indicators remain the backbone of the indicator framework, serving the purpose for detailed assessment of specific policy measures, the composite indicators allow for an explicit assessment of trade-offs between policies. Our illustrative investigation of the sustainable oceanic development of EU coastal states highlights how much a comprehensive assessment can benefit from the additional inclusion of composite indicators.

To assess the performance of multivariate density forecasts for the world economy based on a Bayesian global vector autoregressive (GVAR) model, we decompose the predictive joint density into its marginals and a copula term that captures the dependence structure among variables and countries. Moreover, we use the stochastic search variable selection prior (SSVS) on the coefficients in its conjugate form to account for model uncertainty at the national level and augment the GVAR framework to allow for stochastic volatility. Our results are as follows: First, the GVAR systematically outperforms forecasts based on country-specific models in terms of predictive joint density. Second, the good GVAR performance is driven by superior predictions for the dependence structure across variables, whereas the GVAR model does not yield better predictive marginal densities. Third, the relative performance gains of the GVAR model are particularly pronounced during the Great Recession. Finally, our results imply that for some countries a more parsimonious GVAR model can further improve the forecast quality.

Using real-time data, we analyze how the systematic expectation errors of professional forecasters in 19 advanced economies depend on the state of the business cycle. Our results indicate that the general result that forecasters systematically overestimate output growth (across all countries) masks considerable differences across different business-cycle states. We show that forecasts for recessions are subject to a large negative systematic forecast error (forecasters overestimate growth), while forecasts for recoveries are subject to a positive systematic forecast error. Forecasts made for expansions have, if anything, a small systematic forecast error for large forecast horizons. When we link information about the business-cycle state in the target year with quarterly information about its state in the forecasting period, we find that forecasters realize business-cycle turning points somewhat late. Using cross-country evidence, we demonstrate that the positive relationship between a change in trend growth rates and forecast bias, as suggested in the literature, breaks down when only focusing on forecasts made for expansions.

This paper documents multivariate forecast disagreement among professional forecasters of the Euro area economy and discusses implications for models of heterogeneous expectation formation. Disagreement varies over time and is strongly counter-cyclical. Disagreement is positively correlated with general (economic) uncertainty. Aggregate supply shocks drive disagreement about the long-run state of the economy while aggregate demand shocks have an impact on the level of disagreement about the short-run outlook for the economy. Forecasters disagree about the structure of the economy and the degree to which individual forecasters disagree with the average forecast tends to persist over time. This suggests that models of heterogeneous expectation formation, which are currently not able to generate those last two features, need to be modified. Introducing learning mechanisms and heterogeneous signal-to-noise ratios could reconcile the benchmark model for disagreement with the observed facts.

We analyze the international transmission of financial stress and its effects on economic activity. We construct country specific monthly financial stress indexes (FSI) using dynamic factor models from 1970 until 2012 for 20 countries. We show that there is a strong co-movement of the FSI during financial crises and that the FSI of financially open countries are relatively more correlated to FSI in other countries. Subsequently, we investigate the international transmission of financial stress and its impact on economic activity in a Global VAR (GVAR) model. We show that i) financial stress is quickly transmitted internationally, ii) financial stress has a lagged but persistent negative effect on economic activity, and iii) that economic slowdowns induce only limited financial stress.

We develop a comprehensive wealth index for cities that measures their endowment with environmental, energy, social, human, and economic capital stocks. We apply this index to the 100 largest autonomous cities in Germany. We find that (i) a good economic performance does not need to come at the cost of environmental degradation; (ii) clear regional differences exist between West and East Germany and between North and South Germany; and (iii) social preferences reflected in the comprehensive wealth index account for roughly half of the variation in housing rents, which reflect individual willingness to pay for living in a certain city.

This thesis is a collection of papers that use survey data to analyze expectations about macroeconomic variables and the way these expectations are formed. Using a new approach for modeling forecast errors in a structural way, we show that most of the individual forecasts in the Consensus survey data set have fairly good properties with respect to unbiasedness and efficiency. We provide empirical evidence in favor of partial equilibrium models of the sticky information type that explain the development of inflation dynamics and the formation process of inflation expectations of households respectively. We show that there is a dichotomy between disagreement about real variables, which is more strongly affected by real factors, and disagreement about nominal variables, which is related to the institutional setting of monetary policy. Mit einem neuen Ansatz zur Modellierung von Prognosefehlern für "fixed event"-Prognosen wird in dieser Dissertation gezeigt, dass der Großteil der untersuchten makroökonomischen Prognosen unverzerrt und (schwach) effizient sind. Es wird außerdem gezeigt, dass Modelle mit der Annahme von "sticky information" die Entwicklung der Inflationsdynamik und die Bildung von Inflationserwartungen in den großen europäischen Ländern gut erklären. Darüber hinaus wird gezeigt, dass die Streuung von Prognosen für reale Größen vor allem durch realwirtschaftliche Faktoren determiniert wird, während jene für Prognosen für nominale Größen vor allem von den geldpolitischen Rahmenbedingungen beeinflusst werden.

In this paper, we use survey data to analyze the accuracy, unbiasedness, and the efficiency of professional macroeconomic forecasts. We analyze a large panel of individual forecasts that has not been analyzed in the literature so far. We provide evidence on the properties of forecasts for all G7 counties and for four diffierent macroeconomic variables. Our results show a high degree of dispersion of forecast accuracy across forecasters. We also find that there are large diffierences in the performance of forecasters not only across countries but also across diffierent macroeconomic variables. In general, forecasts tend to be biased in situations where forecasters have to respond to large structural shocks or gradual changes in the trend of a variable. Furthermore, while a sizable fraction of forecasters seem to smooth their GDP forecasts significantly, this does not apply to forecasts made for other macroeconomic variables.

Using the Consensus Economics dataset with individual expert forecasts from G7 countries we investigate determinants of disagreement (crosssectional dispersion of forecasts) about six key economic indicators. Disagreement about real variables (GDP, consumption, investment and unemployment) has a distinct dynamic from disagreement about nominal variables (inflation and interest rate). Disagreement about real variables intensifies strongly during recessions, including the current one (by about 40 percent in terms of the interquartile range). Disagreement about nominal variables rises with their level, has fallen after 1998 or so (by 30 percent), and is considerably lower under independent central banks (by 35 percent). Cross-sectional dispersion for both groups increases with uncertainty about the underlying actual indicators, though to a lesser extent for nominal series. Country-by-country regressions for inflation and interest rates reveal that both the level of disagreement and its sensitivity to macroeconomic variables tend to be larger in Italy, Japan and the United Kingdom, where central banks became independent only around the mid-1990s. These findings suggest that more credible monetary policy can substantially contribute to anchoring of expectations about nominal variables; its effects on disagreement about real variables are moderate.

We analyze what macroeconomic shocks affect the soundness of the German banking system and how this, in turn, feeds back into the macroeconomic environment. Recent turmoils on the international financial markets have shown very clearly that assessing the degree to which banks are vulnerable to macroeconomic shocks is of utmost importance to investors and policy makers. We propose to use a VAR framework that takes feedback effects between the financial sector and the macroeconomic environment into account. We identify responses of a distress indicator for the German banking system to a battery of different structural shocks. We find that monetary policy shocks, fiscal policy shocks, and real estate price shocks have a significant impact on the probability of distress in the banking system. We identify some differences across type of banks and different distress categories, though these differences are often small and do not show any systematic patterns.

During the ongoing financial crisis the analysis of similar historical crises has gained more and more attention among economic researchers and forecasters. Existing studies, however, do not tackle the immense heterogeneity that is present in cross-country samples in a formal and consistent way. In this paper, we propose a standardization approach to estimate the typical impact of economic crises. We show that our approach leads to estimates that are much less dependent on the sample used to estimate the typical shape and, hence, should give more reliable information about the typical macroeconomic impact of economic crises.

Macro-stress testing studies often rely on rather short sample periods due to the limited availability of banking data. They may fail to appropriately account for the cyclicality in the interaction between the banking system and macroeconomic developments. In this paper we use a newly constructed data set on German banks' income and loss statements over the past 36 years to model the interaction between the banking sector and the macroeconomy. Our identified-VAR analysis indicates that the level of stress in the banking sector is strongly affected by monetary policy shocks. The results rationalize the active behavior of central banks observed during periods of financial market crises.

A couple of recent papers have shifted the focus towards disagreement of professional forecasters. When dealing with survey data that is sampled at a frequency higher than annual and that includes only fixed event forecasts, e.g. expectation of average annual growth rates measures of disagreement across forecasters naturally are distorted by a component that mainly reflects the time varying forecast horizon. We use data from the Survey of Professional Forecasters, which reports both fixed event and fixed horizon forecasts, to evaluate different methods for extracting the ``fundamental'' component of disagreement. Based on the paper's results we suggest two methods to estimate dispersion measures from panels of fixed event forecasts: a moving average transformation of the underlying forecasts and estimation with constant forecast-horizon-effects. Both models are easy to handle and deliver equally well performing results, which show a surprisingly high correlation (up to 0.94) with the true dispersion.

In this paper, we use survey data to analyze the rationality of professional macroeconomic forecasts. We analyze both individual forecasts and average forecasts. We provide evidence on the properties of forecasts for all the G7-counties and four different macroeconomic variables. Furthermore, we present a modification to the structural model which is commonly used to model the forecast errors of fixed event forecasts in the literature. Our results confirm that average forecasts should be used with caution, since even if all individual forecasts are rational the hypothesis of rationality is often rejected by the aggregate forecasts. We find that there are not only large differences in the performance of forecasters across countries but also across different macroeconomic variables; in general, forecasts tend to be biased in situations where forecasters have to learn about large structural shocks or gradual changes in the trend of a variable.

In this paper we analyze the power of various indicators to predict growth rates of aggregate production using real-time data. In addition, we assess their ability to predict turning points of the economy. We consider four groups of indicators: survey data, composite indicators, real economic indicators, and financial data. Almost all indicators are found to improve short-run growth forecasts whereas the results for four-quarter-ahead growth forecasts and the prediction of recession probabilities in general are mixed. We can confirm the result that an indicator suited to improve growth forecasts does not necessarily help to produce more accurate recession forecasts. Only composite leading indicators perform generally well in both forecasting exercises.

We use the concept of predictability as presented in Diebold and Kilian (2001) to assess how well the growth rates of various components of German GDP can be forecasted. In particular, it is analyzed how well different commonly used leading indicators can increase predictability of these time series. To this end, we propose an algorithm to select an optimal information set from a full set of possible leading indicators. In the univariate set up, we find very small degrees of predictability for all quarterly growth rates whereas yearly growth rates seem to be more predictable at short forecast horizons. According to the algorithm proposed, from a set of financial leading indicators the short term interest rate is included in the highest number of information sets and from a set of survey indicators the ifo-business expectation index is included in most cases. Conditioning on the optimal sets of leading indicators improves the predictability of most of the quarterly growth rates substantially while the predictabilities of the yearly growth rates cannot be increased significantly further. The results indicate that there is clearly evidence that complicated forecasting models are usually superior to simple AR univariate models.

We investigate the relevance of the Carroll’s sticky information model of inflation expectations for four major European economies (France, Germany, Italy and the United Kingdom). Using survey data on household and expert inflation expectations we argue that the model adequately captures the dynamics of household inflation expectations. We estimate two alternative parametrizations of the sticky information model which differ in the stationarity assumptions about the underlying series. Our baseline stationary estimation suggests that the average frequency of information updating for the European households is roughly once in 18 months. The vector error-correction model implies households update information about once a year.

Using a newly constructed macroeconometric model for Germany and the rest of the Euro area, we investigate the macroeconomic effects of structural labor market reforms in Germany. We find that neither the fact that Germany can no longer pursue an independent monetary policy nor the possibility that other countries in the Euro area might react to reforms in Germany by implementing labor market reforms themselves constitute impediments to successful reforms. Reforms would relative quickly bring down unemployment and increase GDP significantly. Even former labor market "insiders" would gain as net wages increase due to falling unemployment insurance contributions.

It continues to be heavily disputed whether foreign aid promotes economic growth in developing countries. In most cross-country regressions, aid is considered effective only if it shifts recipient countries to a significantly higher and sustainable growth path. We apply an alternative approach which is less demanding, based on the concept of temporary growth accelerations suggested by Hausmann, Pritchett and Rodrik. In assessing what can reasonably be expected from the donors' modest aid efforts, we do not only employ aggregate aid data but we also differentiate between major aid categories, including grants, loans and so-called short-impact aid. It turns out that aid flows have a small but significantly positive effect on the conditional probability of growth accelerations. This result holds across different estimation methods. Short-impact aid is found to be more effective in this respect, while we reject the view that grants are superior to loans. To the contrary, we find a stronger effect of loans. Furthermore, aid has become more effective during the second half of our sample. Typically, however, the significance of results crucially depends on the criteria applied to identify growth accelerations.

This paper investigates the relevance of the sticky information model of Mankiw and Reis (2002) and Carroll (2003) for four major European economies (France, Germany, Italy and the United Kingdom). As opposed to the benchmark rational expectation models, households in the sticky information environment update their expectations sporadically rather than instantaneously owing to the costs of acquiring and processing information. We estimate two alternative parametrizations of the sticky information model which differ in the stationarity assumptions about the underlying series. Using survey data on households? and experts? inflation expectations, we find that the model adequately captures the dynamics of household inflation expectations. Both parametrizations imply comparable speeds of information updating for the European households as was previously found in the US, on average roughly once a year.