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This issue features three articles that present research and analysis by Bank of Canada staff. The first updates previous Bank estimates of measurement bias in the Canadian consumer price index; the second uses a new term-structure model to analyze the relationship between the short-term policy rate and long-term interest rates; and the third examines indicators of balance-sheet risks at financial institutions in Canada.

We perform an analysis to determine how well the introduction of a countercyclical loanto- value (LTV) ratio can reduce household indebtedness and housing price fluctuations compared with a monetary policy rule augmented with house price inflation.

We propose a tool that decomposes TFP growth into sectoral contributions. The analysis incorporates three structural factors—digitalization, aging and climate change policies—and measures their contributions. Overall, we expect that aggregate TFP growth will slow down in the 2020s below both its historical average and the average from the 2010s.

In the past few years, many have postulated that the possible disinflationary effects of digitalization could explain the subdued inflation in advanced economies. In this note, we review the evidence found in the literature. We look at three main channels.

This paper presents a structural framework of the global oil market that relies on information on global fuel consumption to identify flow demand for oil. We show that under mild identifying assumptions, data on global fuel consumption help to provide comparatively sharp insights on elasticities and other key structural parameters of the global oil market.

In a speech to the Vancouver Board of Trade, Governor Mark Carney discusses the fundamentals of the Canadian residential real estate market, the international context, and the implications for monetary policy.

We analyze 40 years’ worth of natural disasters using a local projection framework to assess their impact on provincial labour markets in Canada. We find that disasters decrease hours worked within a week and lower wage growth in the medium run. Our study highlights that disasters affect vulnerable workers through the income channel.

In this article, the authors build on the framework used in the Bank of Canada's Financial System Review to assess the evolution of household indebtedness and financial vulnerabilities in response to changing economic conditions. To achieve this, they first compare two microdata sets generated by Ipsos Reid's Canadian Financial Monitor and Statistics Canada's Survey of Financial Security. They find that the surveys are broadly comparable, despite methodological differences. This enables them to use the combined information content for the identification of the threshold value of the debt-service ratio (DSR). The article then presents an innovative framework that uses household-level microdata to simulate changes in the distribution of the DSR under various stress scenarios. The authors show how this framework can be used by analyzing the effects of two different scenarios on the distribution of the debt-service ratio and the impact on vulnerable households. This tool will enable researchers to refine their analyses of current risks to the financial health of Canadian households. The article concludes with comments on future directions for refining the Bank's analyses of household sector risk.

We introduce a new class of time-varying parameter vector autoregressions (TVP-VARs) where the identified structural innovations are allowed to influence — contemporaneously and with a lag — the dynamics of the intercept and autoregressive coefficients in these models.

This article reviews selected papers that use machine learning for economics research and policy analysis. Our review highlights when machine learning is used in economics, the commonly preferred models and how those models are used.