C53 - Forecasting and Prediction Methods; Simulation Methods
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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. -
Multivariate Realized Stock Market Volatility
We present a new matrix-logarithm model of the realized covariance matrix of stock returns. The model uses latent factors which are functions of both lagged volatility and returns. -
How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables
For stationary transformations of variables, there exists a maximum horizon beyond which forecasts can provide no more information about the variable than is present in the unconditional mean. Meteorological forecasts, typically excepting only experimental or exploratory situations, are not reported beyond this horizon; by contrast, little generally accepted information about such maximum horizons is available for economic variables. -
Short-Run and Long-Run Causality between Monetary Policy Variables and Stock Prices
The authors examine simultaneously the causal links connecting monetary policy variables, real activity, and stock returns.