C32 - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models - Bank of Canada
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Bank of Canada RSS Feedsen2024-03-29T16:01:53+00:00Estimation and Inference by the Method of Projection Minimum Distance
https://www.bankofcanada.ca/2007/12/working-paper-2007-56/
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.2007-12-02T09:26:00+00:00enEstimation and Inference by the Method of Projection Minimum Distance2007-12-02Econometric and statistical methodsWorking Paper 2007-56 https://www.bankofcanada.ca/wp-content/uploads/2010/02/wp07-56.pdfEstimation and Inference by the Method of Projection Minimum DistanceÒscar JordàSharon KozickiDecember 2007CC3C32C5C53EE4E47Tracking Canadian Trend Productivity: A Dynamic Factor Model with Markov Switching
https://www.bankofcanada.ca/2007/11/discussion-paper-2007-12/
The author attempts to track Canadian labour productivity over the past four decades using a multivariate dynamic factor model that, in addition to the labour productivity series, includes aggregate compensation and consumption information. Productivity is assumed to switch between two regimes (the high-growth state and the low-growth state) with different trend growth rates according to […]2007-11-21T11:15:47+00:00enTracking Canadian Trend Productivity: A Dynamic Factor Model with Markov Switching2007-11-21ProductivityDiscussion Paper 2007-12https://www.bankofcanada.ca/wp-content/uploads/2010/01/dp07-12.pdfTracking Canadian Trend Productivity: A Dynamic Factor Model with Markov SwitchingMichael DolegaNovember 2007CC3C32OO4O5O51The Canadian Business Cycle: A Comparison of Models
https://www.bankofcanada.ca/2007/07/working-paper-2007-38/
This paper examines the ability of linear and nonlinear models to replicate features of real Canadian GDP. We evaluate the models using various business-cycle metrics.2007-07-01T12:54:10+00:00enThe Canadian Business Cycle: A Comparison of Models2007-07-01Business fluctuations and cyclesEconometric and statistical methodsWorking Paper 2007-38 https://www.bankofcanada.ca/wp-content/uploads/2010/02/wp07-38.pdfThe Canadian Business Cycle: A Comparison of ModelsFrédérick DemersRyan MacdonaldJuly 2007CC3C32EE3E37Multivariate Realized Stock Market Volatility
https://www.bankofcanada.ca/2007/03/working-paper-2007-20/
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.2007-03-07T15:39:50+00:00enMultivariate Realized Stock Market Volatility2007-03-07Econometric and statistical methodsFinancial marketsWorking Paper 2007-20 https://www.bankofcanada.ca/wp-content/uploads/2010/03/wp07-20.pdfMultivariate Realized Stock Market VolatilityGregory BauerKeith VorkinkMarch 2007CC3C32C5C53GG1G14Evaluating Forecasts from Factor Models for Canadian GDP Growth and Core Inflation
https://www.bankofcanada.ca/2007/02/working-paper-2007-8/
This paper evaluates the performance of static and dynamic factor models for forecasting Canadian real output growth and core inflation on a quarterly basis. We extract the common component from a large number of macroeconomic indicators, and use the estimates to compute out-of-sample forecasts under a recursive and a rolling scheme with different window sizes.2007-02-08T11:00:53+00:00enEvaluating Forecasts from Factor Models for Canadian GDP Growth and Core Inflation2007-02-08Econometric and statistical methodsWorking Paper 2007-8 https://www.bankofcanada.ca/wp-content/uploads/2010/03/wp07-8.pdfEvaluating Forecasts from Factor Models for Canadian GDP Growth and Core InflationFrédérick DemersCalista CheungFebruary 2007CC3C32EE3E37