Real-Time State-Space Method for Computing Smoothed Estimates for Future Revisions of U.S. Monthly Chained CPI

Well known CPI of urban consumers is never revised. Recently initiated chained CPI is initially released every month (ICPI), for that month without delay within BLS and for the previous month with one month delay to the public. Final estimates of chained CPI (FCPI) are released every February for January to December of the calendar year two years before. Every month, simultaneously with the release of ICPI, we would like to have a best estimate, given current information, of FCPI for that month, which will not be released until two calendar years later. ICPI and FCPI data may be indexed in historical time by months of occurrence or in current or real time by months of observation or release. The essence of the solution method is to use data indexed in historical time to estimate models and, then, for an estimated model, to use data indexed in real time to estimate FCPI. We illustrate the method with regression and VARMA models. Using a regression model, estimated FCPI is given directly by an estimated regression line; and, using a VARMA model, estimated FCPI is computed using a Kalman smoother.* *The paper has been presented at the following conferences: Computing in Economics and Finance (Montreal, June 2007), Measurement Errors (Birmingham, UK, July 2007), Joint Statistical Meeting (Salt Lake City, August 2007), NBER-NSF Time-Series (Iowa City, Sept. 2007), CIRANO Data Revision in Macroeconomic Forecasting and Policy (Montreal, Oct. 2007), Society for Nonlinear Dynamics and Econometrics (Atlanta, April 2009), UCR Business Cycles (Riverside, April 2009), Econometric Society (Boston, June 2009), and Midwest Macro (Nashville, May 2011). The paper represents the author's views and does not necessarily represent any official positions of the Bureau of Labor Statistics. **Affiliated as Research Fellow with Center for Financial Studies (CFS), Johann Wolfgang Goethe University, Frankfurt, Germany, and with Center for Economic Studies and Ifo Institute for Economic Research (CESifo), Munich, Germany.

• Publication year

  2016

• Venue

  Social Science Research Network

• Publication date

  Unknown publication date

• Fields of study

  Not labeled

• Identifiers
  DOI 10.2139/ssrn.2795491
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Modeling data revisions: Measurement error and dynamics of 'true' values

  2011cited by this paper
• VAR Estimation and Forecasting When Data Are Subject to Revision

  2010cited by this paper
• Real-Time Quarterly Signal-Plus-Noise Model for Estimating True GDP

  2002cited by this paper
• A state space model for reducing the uncertainty associated with preliminary vintages of data with an application to aggregate consumption

  1994cited by this paper
• Adjustment of provisional mortality series: the dynamic linear model with structured measurement errors.

  1991cited by this paper
• Two Models of Measurements and the Investment Accelerator

  1989cited by this paper
• Gaussian Likelihood of Continuous-Time ARMAX Models When Data Are Stocks and Flows at Different Frequencies

  1988cited by this paper
• Benchmarking of Economic Time Series

  1987cited by this paper
• Data Revision, Reconstruction, and Prediction: An Application to Inventory Investment

  1984cited by this paper
• Exact likelihood of vector autoregressive-moving average process with missing or aggregated data

  1983cited by this paper
• Maximum Likelihood Fitting of ARMA Models to Time Series With Missing Observations

  1980cited by this paper
• Estimating the Dimension of a Model

  1978cited by this paper
• The Use of Preliminary Data in Econometric Forecasting

  1978cited by this paper

• No citing papers are available for this paper.