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Chapter 14

Chapter 14. Introduction to Time Series Regression and Forecasting. Introduction to Time Series Regression and Forecasting (SW Chapter 14).

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Chapter 14

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  1. Chapter 14 Introduction to Time Series Regression and Forecasting

  2. Introduction to Time Series Regression and Forecasting(SW Chapter 14)

  3. Example #1 of time series data: US rate of price inflation, as measured by the quarterly percentage change in the Consumer Price Index (CPI), at an annual rate

  4. Example #2: US rate of unemployment

  5. Why use time series data?

  6. Time series data raises new technical issues

  7. Using Regression Models for Forecasting (SW Section 14.1)

  8. Introduction to Time Series Data and Serial Correlation (SW Section 14.2)

  9. We will transform time series variables using lags, first differences, logarithms, & growth rates

  10. Example: Quarterly rate of inflation at an annual rate (U.S.)

  11. Example: US CPI inflation – its first lag and its change

  12. Autocorrelation

  13. Sample autocorrelations

  14. Example:

  15. Other economic time series:

  16. Other economic time series, ctd:

  17. Stationarity: a key requirement for external validity of time series regression

  18. Autoregressions(SW Section 14.3)

  19. The First Order Autoregressive (AR(1)) Model

  20. Example: AR(1) model of the change in inflation

  21. Example: AR(1) model of inflation – STATA

  22. Example: AR(1) model of inflation – STATA, ctd.

  23. Example: AR(1) model of inflation – STATA, ctd

  24. Forecasts: terminology and notation

  25. Forecast errors

  26. Example: forecasting inflation using an AR(1)

  27. The AR(p) model: using multiple lags for forecasting

  28. Example: AR(4) model of inflation

  29. Example: AR(4) model of inflation – STATA

  30. Example: AR(4) model of inflation – STATA, ctd.

  31. Digression: we used Inf, not Inf, in the AR’s. Why?

  32. So why use Inft, not Inft?

  33. Time Series Regression with Additional Predictors and the Autoregressive Distributed Lag (ADL) Model (SW Section 14.4)

  34. Example: inflation and unemployment

  35. The empirical U.S. “Phillips Curve,” 1962 – 2004 (annual)

  36. The empirical (backwards-looking) Phillips Curve, ctd.

  37. Example: dinf and unem – STATA

  38. Example: ADL(4,4) model of inflation – STATA, ctd.

  39. The test of the joint hypothesis that none of the X’s is a useful predictor, above and beyond lagged values of Y, is called a Granger causality test

  40. Forecast uncertainty and forecast intervals

  41. The mean squared forecast error (MSFE) is,

  42. The root mean squared forecast error (RMSFE)

  43. Three ways to estimate the RMSFE

  44. The method of pseudo out-of-sample forecasting

  45. Using the RMSFE to construct forecast intervals

  46. Example #1: the Bank of England “Fan Chart”, 11/05

  47. Example #2: Monthly Bulletin of the European Central Bank, Dec. 2005, Staff macroeconomic projections

  48. Example #3: Fed, Semiannual Report to Congress, 7/04

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