Real-Time Forecasts of the Real Price of Oil
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.
C5 - Econometric Modeling
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Forecasting the Price of Oil
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? -
Mixed Frequency Forecasts for Chinese GDP
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). -
'Lean' versus 'Rich' Data Sets: Forecasting during the Great Moderation and the Great Recession
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). -
Semi-Structural Models for Inflation Forecasting
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. -
On the Advantages of Disaggregated Data: Insights from Forecasting the U.S. Economy in a Data-Rich Environment
The good forecasting performance of factor models has been well documented in the literature. While many studies focus on a very limited set of variables (typically GDP and inflation), this study evaluates forecasting performance at disaggregated levels to examine the source of the improved forecasting accuracy, relative to a simple autoregressive model. We use the latest revision of over 100 U.S. time series over the period 1974-2009 (monthly and quarterly data). -
Real Time Detection of Structural Breaks in GARCH Models
A sequential Monte Carlo method for estimating GARCH models subject to an unknown number of structural breaks is proposed. Particle filtering techniques allow for fast and efficient updates of posterior quantities and forecasts in real time. -
Structural Inflation Models with Real Wage Rigidities: The Case of Canada
Real wage rigidities have recently been proposed as a way of building intrinsic persistence in inflation within the context of New Keynesian Phillips Curves. Using two recent illustrative structural models, we evaluate empirically the importance of real wage rigidities in the data and the extent to which such models provide useful information regarding price stickiness. -
Structural Multi-Equation Macroeconomic Models: Identification-Robust Estimation and Fit
Weak identification is likely to be prevalent in multi-equation macroeconomic models such as in dynamic stochastic general equilibrium setups. Identification difficulties cause the breakdown of standard asymptotic procedures, making inference unreliable. -
Assessing Indexation-Based Calvo Inflation Models
Using identification-robust methods, the authors estimate and evaluate for Canada and the United States various classes of inflation equations based on generalized structural Calvo-type models. The models allow for different forms of frictions and vary in their assumptions regarding the type of price indexation adopted by firms.