Forecasting with Many Models: Model Confidence Sets and Forecast Combination Staff Working Paper 2013-11 Jon D. Samuels, Rodrigo Sekkel A longstanding finding in the forecasting literature is that averaging forecasts from different models often improves upon forecasts based on a single model, with equal weight averaging working particularly well. This paper analyzes the effects of trimming the set of models prior to averaging. Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods JEL Code(s): C, C5, C53
Short-Term Forecasting of the Japanese Economy Using Factor Models Staff Working Paper 2012-7 Claudia Godbout, Marco J. Lombardi While the usefulness of factor models has been acknowledged over recent years, little attention has been devoted to the forecasting power of these models for the Japanese economy. In this paper, we aim at assessing the relative performance of factor models over different samples, including the recent financial crisis. Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, International topics JEL Code(s): C, C5, C50, C53, E, E3, E37, E4, E47
Real-Time Analysis of Oil Price Risks Using Forecast Scenarios Staff Working Paper 2012-1 Christiane Baumeister, Lutz Kilian Recently, there has been increased interest in real-time forecasts of the real price of crude oil. Standard oil price forecasts based on reduced-form regressions or based on oil futures prices do not allow consumers of forecasts to explore how much the forecast would change relative to the baseline forecast under alternative scenarios about future oil demand and oil supply conditions. Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, International topics JEL Code(s): C, C5, C53, E, E3, E32, Q, Q4, Q43
A Stochastic Volatility Model with Conditional Skewness Staff Working Paper 2011-20 Bruno Feunou, Roméo Tedongap We develop a discrete-time affine stochastic volatility model with time-varying conditional skewness (SVS). Importantly, we disentangle the dynamics of conditional volatility and conditional skewness in a coherent way. Content Type(s): Staff research, Staff working papers Topic(s): Asset pricing, Econometric and statistical methods JEL Code(s): C, C1, C5, G, G1, G12
Measuring Systemic Importance of Financial Institutions: An Extreme Value Theory Approach Staff Working Paper 2011-19 Toni Gravelle, Fuchun Li In this paper, we define a financial institution’s contribution to financial systemic risk as the increase in financial systemic risk conditional on the crash of the financial institution. The higher the contribution is, the more systemically important is the institution for the system. Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, Financial institutions, Financial stability, Financial system regulation and policies JEL Code(s): C, C1, C14, C5, C58, G, G2, G21, G3, G32
Real-Time Forecasts of the Real Price of Oil Staff Working Paper 2011-16 Christiane Baumeister, Lutz Kilian We construct a monthly real-time data set consisting of vintages for 1991.1-2010.12 that is suitable for generating forecasts of the real price of oil from a variety of models. Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, International topics JEL Code(s): C, C5, C53, E, E3, E32, Q, Q4, Q43
Forecasting the Price of Oil Staff Working Paper 2011-15 Ron Alquist, Lutz Kilian, Robert Vigfusson We address some of the key questions that arise in forecasting the price of crude oil. What do applied forecasters need to know about the choice of sample period and about the tradeoffs between alternative oil price series and model specifications? Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, International topics JEL Code(s): C, C5, C53, Q, Q4, Q43, Q47
Mixed Frequency Forecasts for Chinese GDP Staff Working Paper 2011-11 Philipp Maier We evaluate different approaches for using monthly indicators to predict Chinese GDP for the current and the next quarter (‘nowcasts’ and ‘forecasts’, respectively). We use three types of mixed-frequency models, one based on an economic activity indicator (Liu et al., 2007), one based on averaging over indicator models (Stock and Watson, 2004), and a static factor model (Stock and Watson, 2002). Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, International topics JEL Code(s): C, C5, C50, C53, E, E3, E37, E4, E47
'Lean' versus 'Rich' Data Sets: Forecasting during the Great Moderation and the Great Recession Staff Working Paper 2010-37 Marco J. Lombardi, Philipp Maier We evaluate forecasts for the euro area in data-rich and ‘data-lean' environments by comparing three different approaches: a simple PMI model based on Purchasing Managers' Indices (PMIs), a dynamic factor model with euro area data, and a dynamic factor model with data from the euro plus data from national economies (pseudo-real time data). Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, International topics JEL Code(s): C, C5, C50, C53, E, E3, E37, E4, E47
Semi-Structural Models for Inflation Forecasting Staff Working Paper 2010-34 Maral Kichian, Rumler Fabio, Paul Corrigan We propose alternative single-equation semi-structural models for forecasting inflation in Canada, whereby structural New Keynesian models are combined with time-series features in the data. Several marginal cost measures are used, including one that in addition to unit labour cost also integrates relative price shocks known to play an important role in open-economies. Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, Inflation and prices JEL Code(s): C, C1, C13, C5, C53, E, E3, E31