Estimating fundamental cross-section dispersion from fixed event forecasts

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.survey data, dispersion, disagreement, fixed event forecasts

What macroeconomic shocks affect the German banking system? Analysis in an integrated micro-macro model

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. --VAR,banking sector stability,sign restriction approach

Estimating Fundamental Cross-Section Dispersion from Fixed Event Forecasts

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.Survey data, dispersion, disagreement, fixed event forecasts

Accuracy, Unbiasedness and Efficiency of Professional Macroeconomic Forecasts: An empirical Comparison for the G7

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.Evaluating forecasts, Macroeconomic Forecasting, Rationality, Survey Data, Fixed-Event Forecasts

A Multivariate Analysis of Forecast Disagreement: Confronting Models of Disagreement with SPF Data

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

Recessions and Instable Estimates 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

Eliciting Expectation Uncertainty from Private Households

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

Disagreement among Forecasters in G7 Countries

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 (in ation and interest rate). Disagreement about real variables intensifes 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. Countryby- 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 eects on disagreement about real variables are moderate.disagreement, survey expectations, monetary policy, forecasting

Disagreement among forecasters in G7 countries

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. JEL Classification: E31, E32, E37, E52, C53disagreement, forecasting, monetary policy, survey expectations

Order Invariant Evaluation of Multivariate Density Forecasts

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