Forecasting the Fragility of the Banking and Insurance Sector

This paper considers the issue of forecasting financial fragility of banks and insurances using a panel data set of performance indicators, namely distance-to- default, taking unobserved common factors into account. We show that common factors are important in the performance of banks and insurances, analyze the influences of a number of observable factors on banking and insurance performance, and evaluate the forecasts from our model. We find that taking unobserved common factors into account reduces the the root mean square forecasts error of firm specific forecasts by up to 11% and of system forecasts by up to 29% relative to a model based only on observed variables. Estimates of the factor loadings suggest that the correlation of financial institutions has been relatively stable over the forecast period.Financial stability, financial linkages, banking, insurances, unobserved common factors, forecasting

Forecasting Random Walks Under Drift Instability

This paper considers forecast averaging when the same model is used but estimation is carried out over different estimation windows. It develops theoretical results for random walks when their drift and/or volatility are subject to one or more structural breaks. It is shown that compared to using forecasts based on a single estimation window, averaging over estimation windows leads to a lower bias and to a lower root mean square forecast error for all but the smallest of breaks. Similar results are also obtained when observations are exponentially down-weighted, although in this case the performance of forecasts based on exponential down-weighting critically depends on the choice of the weighting coefficient. The forecasting techniques are applied to monthly inflation series of 21 OECD countries and it is found that average forecasting methods in general perform better than using forecasts based on a single estimation window.forecast combinations, averaging over estimation windows, exponentially down-weighting observations, structural breaks

Keep It Real!: A Real-time UK Macro Data Set

In this paper, we present a real-time macro data set for the UK. Each variable has many different vintages---reflecting the revisions that occur in real time. Our aim is to provide a resource that allows researchers to assess the robustness of their results to data revisions. We illustrate the importance of this issue by analysing the impact of real-time data on UK inflation forecasts.

Diagnostic Tests of Cross Section Independence for Nonlinear Panel Data Models

In this paper we discuss tests for residual cross section dependence in nonlinear panel data models. The tests are based on average pair-wise residual correlation coefficients. In nonlinear models, the definition of the residual is ambiguous and we consider two approaches: deviations of the observed dependent variable from its expected value and generalized residuals. We show the asymptotic consistency of the cross section dependence (CD) test of Pesaran (2004). In Monte Carlo experiments it emerges that the CD test has the correct size for any combination of N and T whereas the LM test relies on T large relative to N. We then analyze the roll-call votes of the 104th U.S. Congress and find considerable dependence between the votes of the members of Congress.cross-section dependence, nonlinear panel data model

Variable Selection and Inference for Multi-period Forecasting Problems

This paper conducts a broad-based comparison of iterated and direct multi-step forecasting approaches applied to both univariate and multivariate models. Theoretical results and Monte Carlo simulations suggest that iterated forecasts dominate direct forecasts when estimation error is a first-order concern, i.e. in small samples and for long forecast horizons. Conversely, direct forecasts may dominate in the presence of dynamic model misspecification. Empirical analysis of the set of 170 variables studied by Marcellino, Stock and Watson (2006) shows that multivariate information, introduced through a parsimonious factor-augmented vector autoregression approach, improves forecasting performance for many variables, particularly at short horizons.

Bestimmung einer Prognosegüte für TAF-Meldungen

Das Wetter ist der wohl größte Unsicherheitsfaktor, durch den an Verkehrsflughäfen Verspätungen auftreten können. So bedeutet eine geringe Sichtweite höhere Staffelungsabstände gegenüber hohen Sichtweiten, Schnee und Eis sorgen für Verzögerungen durch entsprechende Anti- und De-Icing-Maßnahmen der Flugzeuge, aber auch der Pisten. Um diese Unsicherheiten im Rahmen einer prätaktischen Planung quantifizieren zu können, wurde eine Metrik für die Ermittlung der Prognosegüte einer Wettervorhersage in Form einer TAF (Terminal Aerodrome Forecast) ermittelt, welche im Folgenden vorgestellt wird. Dabei werden die TAF-Meldungen, die für einen Zeitraum von 6 Stunden gelten, mit den tatsächlich eingetroffenen METAR- Meldungen (Meteorological Aerodrome Report) verglichen. Für mehrere Wetterklassen wurden spezifische Modelle zur Bestimmung der Prognosegüte ermittelt und am Beispiel der Sichtweite ausführlich erläutert

"Keep it real!": A real-time UK macro data set

We present a real-time macro data set for the UK. Each variable has many different vintages - reflecting the revisions and s that occur over time. Our aim is to provide a resource for researchers evaluating UK forecasting performance and policy-making in real time. We illustrate the importance of these data by analysing their impacts on UK inflation forecasts and monetary policy in the late 1980s. We find that, contrary to the view of contemporary policy-makers, the initial measurements of demand-side macro variables did not disguise inflationary pressures.

Diagnostic Tests of Cross Section Interdependence for Nonlinear Panel Data Models

In this paper we discuss tests for residual cross section dependence in nonlinear panel data models. The tests are based on average pair-wise residual correlation coefficients. In nonlinear models, the definition of the residual is ambiguous and we consider two approaches: deviations of the observed dependent variable from its expected value and generalized residuals. We show the asymptotic consistency of the cross section dependence (CD) test of Pesaran (2004). In Monte Carlo experiments it emerges that the CD test has the correct size for any combination of N and T whereas the LM test relies on T large relative to N. We then analyze the roll-call votes of the 104th U.S. Congress and find considerable dependence between the votes of the members of Congress