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Title (in a larger font), venue, and date

Title (in a larger font), venue, and date. A Framework for Decomposing Shocks and Measuring Volatilities Derived from Multi-Dimensional Panel Data of Survey Forecasts Econometrics in Public Policy University at Albany October 29, 2005.

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Title (in a larger font), venue, and date

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  1. Title (in a larger font), venue, and date A Framework for Decomposing Shocks and Measuring Volatilities Derived from Multi-Dimensional Panel Data of Survey Forecasts Econometrics in Public Policy University at Albany October 29, 2005 Include slide numbers so your audience can refer back to specific slides during Q&A. Logo of the institution at which you are presenting if different from your home institution (on the right). Your name and logo of your institution (on the left).

  2. Include a title on each slide the summarizes the information on the slide. Set this apart via font size, underlining, etc. • Topics • Evolution of three-dimensional panel data sets. • Early methods for analyzing three-dimensional data. • Three-dimensional structure of the ASA-NBER data. • Decomposition of forecast shocks. • Davies-Lahiri framework. • Inflation shocks. • Suggestions for future research. • The order of topics is typically • Background and theory • Model and variable definitions • Results • Interpretation and implications • Suggestions for future research Use a sans serif font (e.g., Tahoma). Use 16 point or larger font size.

  3. Evolution of Three-Dimensional Panel Data Sets Three-dimensional data sets have a reasonably long history: Livingston Survey (from 1946) ASA-NBER Survey of Professional Forecasters (from 1968) Blue Chip Survey of Professional Forecasters (from 1976) Ideally, each page will have a title that matches one of the bullet points in the topics list.

  4. Early Methods for Analyzing Three-Dimensional Panel Data Not until early 1990’s that the first two-dimensional analytic techniques were employed: Swindler and Ketcher (JMCB, 1990) Keane and Runkle (AER, 1990) Batchelor and Dua (JMCB, 1991) De Bont and Bange (JFQA, 1992) Give a brief overview of work that has preceded yours. Where relevant, cite background literature.

  5. Three-Dimensional Structure of the ASA-NBER Data Introduce your work conceptually before going into technical detail. The goal is to give the audience an intuitive grasp of your topic.

  6. Decomposition of Shocks Next, introduce the technical work. Before showing regression equations, make sure that the audience understands what your data measures and the definitions of your variables.

  7. Decomposition of Shocks Make variable definitions very clear. Make sure to correctly identify indices.

  8. Individual i forecast for quarter t made h quarters prior to the end of quarter t Individual i forecast bias specific to horizon h forecasts Idiosyncratic error (assumed white noise over all three dimensions) (Possibly unobserved) inflation at h quarters prior to the end of quarter t Unbiased and efficient forecasted change in inflation anticipated to occur from h quarters prior to the end of quarter t to the end of quarter t Davies-Lahiri Framework Show the regression equation you intend to estimate along with variable definitions.

  9. Davies-Lahiri Framework Where relevant, show how estimates are measured. It is not necessary to go into detail regarding commonly understood estimates (e.g., OLS, TSLS). Where you have done something less common, show how you calculated your estimates.

  10. Where possible, display results graphically. Label graphs completely. Inflation Shocks Reinforcing (discrete) shocks Canceling (discrete) shocks

  11. Inflation Shocks

  12. Always end with a repeat of your title slide. A Framework for Decomposing Shocks and Measuring Volatilities Derived from Multi-Dimensional Panel Data of Survey Forecasts Econometrics in Public Policy University at Albany October 29, 2005

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