C5 - Econometric Modeling
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GDP by Industry in Real Time: Are Revisions Well Behaved?
The monthly data for real gross domestic product (GDP) by industry are used extensively in real time both to ground the Bank of Canada’s monitoring of economic activity and in the Bank’s nowcasting tools, making these data one of the most important high-frequency time series for Canadian nowcasting. -
An Alternative Estimate of Canadian Potential Output: The Multivariate State-Space Framework
In this paper, we extend the state-space methodology proposed by Blagrave et al. (2015) and decompose Canadian potential output into trend labour productivity and trend labour input. As in Blagrave et al. (2015), we include output growth and inflation expectations from consensus forecasts to help refine our estimates. -
The Framework for Risk Identification and Assessment
Risk assessment models are an important component of the Bank’s analytical tool kit for assessing the resilience of the financial system. We describe the Framework for Risk Identification and Assessment (FRIDA), a suite of models developed at the Bank of Canada to quantify the impact of financial stability risks to the broader economy and a range of financial system participants (households, businesses and banks). -
Characterizing the Canadian Financial Cycle with Frequency Filtering Approaches
In this note, I use two multivariate frequency filtering approaches to characterize the Canadian financial cycle by capturing fluctuations in the underlying variables with respect to a long-term trend. The first approach is a dynamically weighted composite, and the second is a stochastic cycle model. -
Challenges in Implementing Worst-Case Analysis
Worst-case analysis is used among financial regulators in the wake of the recent financial crisis to gauge the tail risk. We provide insight into worst-case analysis and provide guidance on how to estimate it. We derive the bias for the non-parametric heavy-tailed order statistics and contrast it with the semi-parametric extreme value theory (EVT) approach. -
Nowcasting Canadian Economic Activity in an Uncertain Environment
This paper studies short-term forecasting of Canadian real GDP and its expenditure components using combinations of nowcasts from different models. Starting with a medium-sized data set, we use a suite of common nowcasting tools for quarterly real GDP and its expenditure components. -
Analysis of Asymmetric GARCH Volatility Models with Applications to Margin Measurement
We explore properties of asymmetric generalized autoregressive conditional heteroscedasticity (GARCH) models in the threshold GARCH (GTARCH) family and propose a more general Spline-GTARCH model, which captures high-frequency return volatility, low-frequency macroeconomic volatility as well as an asymmetric response to past negative news in both autoregressive conditional heteroscedasticity (ARCH) and GARCH terms. -
State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models
Implications for signal extraction from specifying unobserved components (UC) models with correlated or orthogonal innovations have been well investigated. In contrast, the forecasting implications of specifying UC models with different state correlation structures are less well understood. -
Can Media and Text Analytics Provide Insights into Labour Market Conditions in China?
The official Chinese labour market indicators have been seen as problematic, given their small cyclical movement and their only-partial capture of the labour force. In our paper, we build a monthly Chinese labour market conditions index (LMCI) using text analytics applied to mainland Chinese-language newspapers over the period from 2003 to 2017.