Forecasting German GDP using alternative factor models based on large datasets

This paper discusses the forecasting performance of alternative factor models based on a large panel of quarterly time series for the german economy. One model extracts factors by static principals components analysis, the other is based on dynamic principal components obtained using frequency domain methods. The third model is based on subspace algorithm for state space models. Out-of-sample forecasts show that the prediction errors of the factor models are generally smaller than the errors of simple autoregressive benchmark models. Among the factors models, either the dynamic principal component model or the subspace factor model rank highest in terms of forecast accuracy in most cases. However, neither of the dynamic factor models can provide better forecasts than the static model over all forecast horizons and different specifications of the simulation design. Therefore, the application of the dynamic factor models seems to provide only small forecasting improvements over the static factor model for forecasting German GDP. --Factor models,static and dynamic factors,principal components,forecasting accuracy

Factor-MIDAS for now- and forecasting with ragged-edge data: a model comparison for German GDP

This paper compares different ways to estimate the current state of the economy using factor models that can handle unbalanced datasets. Due to the different release lags of business cycle indicators, data unbalancedness often emerges at the end of multivariate samples, which is sometimes referred to as the 'ragged edge' of the data. Using a large monthly dataset of the German economy, we compare the performance of different factor models in the presence of the ragged edge: static and dynamic principal components based on realigned data, the Expectation-Maximisation (EM) algorithm and the Kalman smoother in a state-space model context. The monthly factors are used to estimate current quarter GDP, called the 'nowcast', using different versions of what we call factor-based mixed-data sampling (Factor-MIDAS) approaches. We compare all possible combinations of factor estimation methods and Factor-MIDAS projections with respect to nowcast performance. Additionally, we compare the performance of the nowcast factor models with the performance of quarterly factor models based on time-aggregated and thus balanced data, which neglect the most timely observations of business cycle indicators at the end of the sample. Our empirical findings show that the factor estimation methods don't differ much with respect to nowcasting accuracy. Concerning the projections, the most parsimonious MIDAS projection performs best overall. Finally, quarterly models are in general outperformed by the nowcast factor models that can exploit ragged-edge data. --MIDAS,large factor models,nowcasting,mixed-frequency data,missing values

Reconsidering the role of monetary indicators for euro area inflation from a Bayesian perspective using group inclusion probabilities

This paper addresses the relative importance of monetary indicators for forecasting inflation in the euro area in a Bayesian framework. Bayesian Model Averaging (BMA)based on predictive likelihoods provides a framework that allows for the estimation of inclusion probabilities of a particular variable, that is the probability of that variable being in the forecast model. A novel aspect of the paper is the discussion of group-wise inclusion probabilities, which helps to address the empirical question whether the group of monetary variables is relevant for forecasting euro area inflation. In our application, we consider about thirty monetary and non-monetary indicators for inflation. Using this data, BMA provides inclusion probabilities and weights for Bayesian forecast combination. The empirical results for euro area data show that monetary aggregates and non-monetary indicators together play an important role for forecasting inflation, whereas the isolated information content of both groups is limited. Forecast combination can only partly outperform single-indicator benchmark models. --inflation forecasting,monetary indicators,Bayesian Model Averaging,inclusion probability

Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP

This paper compares different ways to estimate the current state of the economy using factor models that can handle unbalanced datasets. Due to the different release lags of business cycle indicators, data unbalancedness often emerges at the end of multivariate samples, which is sometimes referred to as the "ragged edge" of the data. Using a large monthly dataset of the German economy, we compare the performance of different factor models in the presence of the ragged edge: static and dynamic principal components based on realigned data, the Expectation-Maximisation (EM) algorithm and the Kalman smoother in a state-space model context. The monthly factors are used to estimate current quarter GDP, called the "nowcast", using different versions of what we call factor-based mixed-data sampling (Factor-MIDAS) approaches. We compare all possible combinations of factor estimation methods and Factor-MIDAS projections with respect to now-cast performance. Additionally, we compare the performance of the nowcast factor models with the performance of quarterly factor models based on time-aggregated and thus balanced data, which neglect the most timely observations of business cycle indicators at the end of the sample. Our empirical findings show that the factor estimation methods don't differ much with respect to nowcasting accuracy. Concerning the projections, the most parsimonious MIDAS projection performs best overall. Finally, quarterly models are in general outperformed by the nowcast factor models that can exploit ragged-edge data.nowcasting, business cycle, large factor models, mixed-frequency data, missing values, MIDAS

Biased interpretation of performance feedback: The role of ceo overconfidence

Research summary: This study examines how managerial biases in the form of overconfidence change the interpretation of performance feedback and, consequently, shape a firm's risk taking in response to it. Our formal analysis suggests that CEO overconfidence is associated with a lower willingness to increase firm risk taking when facing negative performance feedback and a higher willingness to decrease risk when facing positive feedback. An extension of our model also shows that, when firms are operating close to their survival level, the effects of CEO overconfidence will reverse. We test our predictions empirically with a sample of 847 American manufacturing firms in the years 1992 to 2014. Our results are consistent with our hypotheses and are robust to different empirical operationalizations of CEO overconfidence. Managerial summary: Managers evaluate the success of their current business strategy through feedback in the form of their firm's current financial results relative to their own previous performance or that of their peers. Our results show that overconfident CEOs interpret information about the financial situation of their firms more optimistically than non-overconfident CEOs, which in turn causes them to exhibit a less pronounced reaction to both positive or negative performance feedback. It is thus crucial that managers are clearly aware of how their interpretations and reactions to feedback are affected by their own deeply held personal beliefs and dispositions

Pooling versus Model Selection for Nowcasting with Many Predictors: An Application to German GDP

This paper discusses pooling versus model selection for now- and forecasting in the presence of model uncertainty with large, unbalanced datasets. Empirically, unbalanced data is pervasive in economics and typically due to di¤erent sampling frequencies and publication delays. Two model classes suited in this context are factor models based on large datasets and mixed-data sampling (MIDAS) regressions with few predictors. The specification of these models requires several choices related to, amongst others, the factor estimation method and the number of factors, lag length and indicator selection. Thus, there are many sources of mis-specification when selecting a particular model, and an alternative could be pooling over a large set of models with di¤erent specifications. We evaluate the relative performance of pooling and model selection for now- and forecasting quarterly German GDP, a key macroeconomic indicator for the largest country in the euro area, with a large set of about one hundred monthly indicators. Our empirical findings provide strong support for pooling over many speci.cations rather than selecting a specific model.nowcasting, forecast combination, forecast pooling, model selection, mixed-frequency data, factor models, MIDAS

MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area

This paper compares the mixed-data sampling (MIDAS) and mixed-frequency VAR (MF-VAR) approaches to model speci.cation in the presence of mixed-frequency data, e.g., monthly and quarterly series. MIDAS leads to parsimonious models based on exponential lag polynomials for the coe¢ cients, whereas MF-VAR does not restrict the dynamics and therefore can su¤er from the curse of dimensionality. But if the restrictions imposed by MIDAS are too stringent, the MF-VAR can perform better. Hence, it is di¢ cult to rank MIDAS and MF-VAR a priori, and their relative ranking is better evaluated empirically. In this paper, we compare their performance in a relevant case for policy making, i.e., nowcasting and forecasting quarterly GDP growth in the euro area, on a monthly basis and using a set of 20 monthly indicators. It turns out that the two approaches are more complementary than substitutes, since MF-VAR tends to perform better for longer horizons, whereas MIDAS for shorter horizons.nowcasting, mixed-frequency data, mixed-frequency VAR, MIDAS

Pooling versus model selection for nowcasting with many predictors: an application to German GDP

This paper discusses pooling versus model selection for now- and forecasting in the presence of model uncertainty with large, unbalanced datasets. Empirically, unbalanced data is pervasive in economics and typically due to di¤erent sampling frequencies and publication delays. Two model classes suited in this context are factor models based on large datasets and mixed-data sampling (MIDAS) regressions with few predictors. The specification of these models requires several choices related to, amongst others, the factor estimation method and the number of factors, lag length and indicator selection. Thus, there are many sources of mis-specification when selecting a particular model, and an alternative could be pooling over a large set of models with different specifications. We evaluate the relative performance of pooling and model selection for now- and forecasting quarterly German GDP, a key macroeconomic indicator for the largest country in the euro area, with a large set of about one hundred monthly indicators. Our empirical findings provide strong support for pooling over many specifications rather than selecting a specific model. --casting,forecast combination,forecast pooling,model selection,mixed - frequency data,factor models,MIDAS

MIDAS versus mixed-frequency VAR: nowcasting GDP in the euro area

This paper compares the mixed-data sampling (MIDAS) and mixed-frequency VAR (MF-VAR) approaches to model speci…cation in the presence of mixed-frequency data, e.g., monthly and quarterly series. MIDAS leads to parsimonious models based on exponential lag polynomials for the coe¢ cients, whereas MF-VAR does not restrict the dynamics and therefore can su¤er from the curse of dimensionality. But if the restrictions imposed by MIDAS are too stringent, the MF-VAR can perform better. Hence, it is di¢ cult to rank MIDAS and MF-VAR a priori, and their relative ranking is better evaluated empirically. In this paper, we compare their performance in a relevant case for policy making, i.e., nowcasting and forecasting quarterly GDP growth in the euro area, on a monthly basis and using a set of 20 monthly indicators. It turns out that the two approaches are more complementary than substitutes, since MF-VAR tends to perform better for longer horizons, whereas MIDAS for shorter horizons. --nowcasting,mixed-frequency data,mixed-frequency VAR,MIDAS

Das Produktionspotenzial im Euroraum: Aktuelle Schätzungen und Prognosen

Das Produktionspotenzial ist eine wichtige makroökonomische Indikatorvariable. Die Abweichung der Produktion vom Produktionspotenzial, die Produktionslücke, dient der Einschätzung der konjunkturellen Lage und als Indikator für Inflations- oder Deflationsrisiken. Wie sicher kann die Entwicklung des Produktionspotenzials im Euroraum am aktuellen Rand eingeschätzt werden? Wie sind die Perspektiven für das kommende Jahr? --