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395 Results

Perceived Inflation Persistence

Staff Working Paper 2013-43 Monica Jain
The Survey of Professional Forecasters (SPF) has had vast influence on research related to better understanding expectation formation and the behaviour of macroeconomic agents. Inflation expectations, in particular, have received a great deal of attention, since they play a crucial role in determining real interest rates, the expectations-augmented Phillips curve and monetary policy.

High-Frequency Real Economic Activity Indicator for Canada

Staff Working Paper 2013-42 Gitanjali Kumar
I construct a weekly measure of real economic activity in Canada. Based on the work of Aruoba et al. (2009), the indicator is extracted as an unobserved component underlying the co-movement of four monthly observed real macroeconomic variables - employment, manufacturing sales, retail sales and GDP.
November 14, 2013

Assessing Financial System Vulnerabilities: An Early Warning Approach

This article focuses on a quantitative method to identify financial system vulnerabilities, specifically, an imbalance indicator model (IIM) and its application to Canada. An IIM identifies potential vulnerabilities in a financial system by comparing current economic and financial data with data from periods leading up to past episodes of financial stress. It complements other sources of information - including market intelligence and regular monitoring of the economy - that policy-makers use to assess vulnerabilities.

The Common Component of CPI: An Alternative Measure of Underlying Inflation for Canada

Staff Working Paper 2013-35 Mikael Khan, Louis Morel, Patrick Sabourin
In this paper, the authors propose a measure of underlying inflation for Canada obtained from estimating a monthly factor model on individual components of the CPI. This measure, labelled the common component of CPI, has intuitive appeal and a number of interesting features.

Which Parametric Model for Conditional Skewness?

Staff Working Paper 2013-32 Bruno Feunou, Mohammad R. Jahan-Parvar, Roméo Tedongap
This paper addresses an existing gap in the developing literature on conditional skewness. We develop a simple procedure to evaluate parametric conditional skewness models. This procedure is based on regressing the realized skewness measures on model-implied conditional skewness values.
Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods JEL Code(s): C, C2, C22, C5, C51, G, G1, G12, G15

Volatility and Liquidity Costs

Staff Working Paper 2013-29 Selma Chaker
Observed high-frequency prices are contaminated with liquidity costs or market microstructure noise. Using such data, we derive a new asset return variance estimator inspired by the market microstructure literature to explicitly model the noise and remove it from observed returns before estimating their variance.

Are Product Spreads Useful for Forecasting? An Empirical Evaluation of the Verleger Hypothesis

Staff Working Paper 2013-25 Christiane Baumeister, Lutz Kilian, Xiaoqing Zhou
Notwithstanding a resurgence in research on out-of-sample forecasts of the price of oil in recent years, there is one important approach to forecasting the real price of oil which has not been studied systematically to date.
August 15, 2013

CSI: A Model for Tracking Short-Term Growth in Canadian Real GDP

Canada’s Short-Term Indicator (CSI) is a new model that exploits the information content of 32 indicators to produce daily updates to forecasts of quarterly real GDP growth. The model is a data-intensive, judgment-free approach to short-term forecasting. While CSI’s forecasts at the start of the quarter are not very accurate, the model’s accuracy increases appreciably as more information becomes available. CSI is the latest addition to a wide range of models and information sources that the Bank of Canada uses, combined with expert judgment, to produce its short-term forecasts.
August 15, 2013

The Accuracy of Short-Term Forecast Combinations

This article examines whether combining forecasts of real GDP from different models can improve forecast accuracy and considers which model-combination methods provide the best performance. In line with previous literature, the authors find that combining forecasts generally improves forecast accuracy relative to various benchmarks. Unlike several previous studies, however, they find that, rather than assigning equal weights to each model, unequal weighting based on the past forecast performance of models tends to improve accuracy when forecasts across models are substantially different.
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