1. CIRANO Workshop on Macroeconomic Forecasting, Analysis and Policy with Data Revision “Research Perspectives” Sharon Kozicki Bank of Canada

2. Overview Data Revisions Uncertainty Policy questions

3. Data Revisions 1 Statistics Offices want to produce high quality data –Time to move from “critiques” to “guidance” –Correlations between GDP revisions and various data series or lags of revisions identify room for improvement, but don’t provide explanations Besides… as a profession, we’ve been there, done that –We don’t want statistical agencies to make ad hoc adjustments to data to remove predictability—this opens the door for other sources of ad hoc adjustments –Disaggregated investigations (including correlations of revisions across disaggregates) may provide more useful information

4. Data Revisions 2 Important to distinguish between different sources of revisions (which likely have different statistical properties) (McKenzie) Additional or better raw data New methodologies New definitions

5. Data Revisions 3 Keeping track of timing is complicated, but important (Croushore) –Annual revisions of quarter t data does not necessarily occur in t+4

6. Data Revisions 4 Zero revisions are often not optimal In general there is a tradeoff between timeliness and revisions—initial release data can generally be of better quality if it is released later If new information suggests that prior releases of data contain errors, we generally want the data to be revised (rather than have zero revisions)

7. Uncertainty 1 Incorporate information on all sources of uncertainty into forecast confidence intervals –Coefficient uncertainty –Model error uncertainty –Data uncertainty –…

8. Uncertainty 2 Having a sense of the uncertainty in data and forecasts is important for policymakers –Orphanides opened the door to examinations of the importance of uncertainty in key unobservable variables (such as potential output and the output gap) –Croushore is now teaching us about the importance of uncertainty in “observables” (core PCE inflation)

9. Policy questions 1 What vintage of data should policymakers be interested in? –Bank of Canada forecasts (in our Monetary Policy Report and the MPR Update) are typically compared to initial release data (by the public) (from a Current Analysis perspective, the sort of reduced form regressions I criticized earlier may have merit) –Pre-benchmark final or Final Hard to argue that policy should be anticipating changes in measurement methodology or definition –“Truth” perhaps most relevant, but raises communication challenges and accountability issues; also may be seen as a critique of statistical agencies

10. Policy questions 2 Should revisions be considered in choosing “variables of interest”? Are there other measures of variables of interest that are revised less? –Are weighted-median or trimmed-mean measures of central tendencies of inflation revised less than conventional measures of core inflation?

11. Policy questions 3 What is the implication of revisions for policy evaluation with forward-looking agents? –The answer may depend on the perceived persistence of the revisions. Do revisions have permanent and transitory components? –What is the effect of annual revisions (of prior 3 years) on near-term and longer-term forecasts (estimates of BN trend growth, endpoint)?