C53 - Forecasting and Prediction Methods; Simulation Methods
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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 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. -
A Structural VAR Approach to Core Inflation in Canada
The author constructs a measure of core inflation using a structural vector autoregression containing oil-price growth, output growth, and inflation. This "macro-founded" measure of inflation forecasts total inflation at least as well as other, atheoretical measures. -
Estimation and Inference by the Method of Projection Minimum Distance
A covariance-stationary vector of variables has a Wold representation whose coefficients can be semi-parametrically estimated by local projections (Jordà, 2005). Substituting the Wold representations for variables in model expressions generates restrictions that can be used by the method of minimum distance to estimate model parameters.