This paper analyzes how firm-specific uncertainty affects firms’ propensity to invest. We measure firm-specific uncertainty as firms’ absolute forecast errors derived from survey data of German manufacturing firms over 2007–2011. In line with the literature, our empirical findings reveal a negative impact of firm-specific uncertainty on investment. However, further results show that the investment response is asymmetric, depending on the size and direction of the forecast error. The investment propensity declines significantly if the realized situation is worse than expected. However, firms do not adjust their investment if the realized situation is better than expected, which suggests that the uncertainty effect counteracts the positive effect due to unexpectedly favorable business conditions. This can be one explanation behind the phenomenon of slow recovery in the aftermath of financial crises. Additional results show that the forecast error is highly concurrent with an ex-ante measure of firm-specific uncertainty we obtain from the survey data. Furthermore, the effect of firm-specific uncertainty is enforced for firms that face a tighter financing situation.

Am 7. und 8. Dezember 2015 fand am Leibniz-Institut für Wirtschaftsforschung Halle (IWH) zum 16. Mal der IWH-CIREQ Macroeconometric Workshop statt. Die in Kooperation mit dem Centre interuniversitaire de recherche en économie quantitative (CIREQ), Montréal, durchgeführte Veranstaltung beschäftigte sich dieses Mal mit zentralen Herausforderungen, denen sich die ökonomische Prognose zu stellen hat: Strukturbrüche in den Daten, statistische Revisionen und Fehler bei der Messung wichtiger Indikatoren.

Early-warning models most commonly optimize signaling thresholds on crisis probabilities. The ex-post threshold optimization is based upon a loss function accounting for preferences between forecast errors, but comes with two crucial drawbacks: unstable thresholds in recursive estimations and an in-sample overfit at the expense of out-of-sample performance. We propose two alternatives for threshold setting: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. We provide simulated and real-world evidence that this simplification results in stable thresholds and improves out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.

This paper analyses the recession in 2008/2009 in Germany. This recession is very different from previous recessions in particular regarding their causes and magnitude. We show to what extent forecasters and forecasts based on leading indicators fail to detect the timing and the magnitude of the recession. This study shows that large forecast errors for both expert forecasts and forecasts based on leading indicators resulted during this recession which implies that the recession was very difficult to forecast. However, some leading indicators (survey data, risk spreads, stock prices) have indicated an economic downturn and hence, beat univariate time series models. Although the combination of individual forecasts provides an improvement compared to the benchmark model, the combined forecasts are worse than several individual models. A comparison of expert forecasts withthe best forecasts based on leading indicators shows only minor deviations. Overall, the range for an improvement of expert forecasts in the crisis compared to indicator forecasts is small.

Using a new dataset on capital market regulation, we analyze whether capital controls helped protect emerging markets from the real economic consequences of the 2009 financial and economic crisis. The impact of the crisis is measured by the 2009 forecast error of a panel state space model, which analyzes the business cycle dynamics of 63 middle-income countries. We find that neither capital controls in general nor controls that were specifically targeted to derivatives (that played a crucial role during the crisis) helped shield economies. However, banking regulation that limits the exposure of banks to global risks has been highly successful.

The present paper compares expected inflation to (econometric) inflation forecasts based on a number of forecasting techniques from the literature using a panel of ten industrialized countries during the period of 1988 to 2007. To capture expected inflation, we develop a recursive filtering algorithm which extracts unexpected inflation from real interest rate data, even in the presence of diverse risks and a potential Mundell-Tobin-effect.

The extracted unexpected inflation is compared to the forecasting errors of ten

econometric forecasts. Beside the standard AR(p) and ARMA(1,1) models, which

are known to perform best on average, we also employ several Phillips curve based approaches, VAR, dynamic factor models and two simple model avering approaches.

This paper analyses the recession in 2008/2009 in Germany, which is very different from previous recessions, in particular regarding its cause and magnitude. We show to what extent forecasters and forecasts based on leading indicators fail to detect the timing and the magnitude of the recession. This study shows that large forecast errors for both expert forecasts and forecasts based on leading indicators resulted during this recession which implies that the recession was very difficult to forecast. However, some leading indicators (survey data, risk spreads, stock prices) have indicated an economic downturn and hence, beat univariate time series models. Although the combination of individual forecasts provides an improvement compared to the benchmark model, the combined forecasts are worse than several individual models. A comparison of expert forecasts with the best forecasts based on leading indicators shows only minor deviations. Overall, the range for an improvement of expert forecasts during the crisis compared to indicator forecasts is relatively small.

The present paper compares expected inflation to (econometric) inflation forecasts

based on a number of forecasting techniques from the literature using a panel of

ten industrialized countries during the period of 1988 to 2007. To capture expected

inflation we develop a recursive filtering algorithm which extracts unexpected inflation from real interest rate data, even in the presence of diverse risks and a potential Mundell-Tobin-effect.

The extracted unexpected inflation is compared to the forecasting errors of ten

econometric forecasts. Beside the standard AR(p) and ARMA(1,1) models, which

are known to perform best on average, we also employ several Phillips curve based approaches, VAR, dynamic factor models and two simple model avering approaches.

In this paper a structural macroeconometric model for the Eurozone is presented. In opposite to the multi country modelling approach, the model relies on aggregate data on the supra-national level. Due to nonstationarity, all equations are estimated in an error correction form. The cointegrating relations are derived jointly with the short-run dynamics, avoiding the finite sample bias of the two step Engle Granger procedure. The validity of the aggregated approach is confirmed by out-of-sample forecasts and two simulation exercises. In particular the implications of a lower economic recovery in the US and a shock in the nominal Euro area interest rate are discussed.