1 / 14

Consumer Price Index - Clothing and Footwear

Consumer Price Index - Clothing and Footwear. Consumer Price Index - Clothing and Footwear. Seasonally differenced Consumer Price Index - Clothing and Footwear. Seasonally differenced Consumer Price Index - Clothing and Footwear. CPI Clothing and Footwear SARIMA (1, 0, 0, 0, 1, 0).

kaelem
Download Presentation

Consumer Price Index - Clothing and Footwear

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Consumer Price Index- Clothing and Footwear Time series analysis - lecture 4

  2. Consumer Price Index- Clothing and Footwear Time series analysis - lecture 4

  3. Seasonally differenced Consumer Price Index- Clothing and Footwear Time series analysis - lecture 4

  4. Seasonally differenced Consumer Price Index- Clothing and Footwear Time series analysis - lecture 4

  5. CPI Clothing and FootwearSARIMA (1, 0, 0, 0, 1, 0) Final Estimates of Parameters Type Coef SE Coef T P AR 1 0.7457 0.0522 14.29 0.000 Constant 0.2222 0.1783 1.25 0.214 Differencing: 0 regular, 1 seasonal of order 12 Number of observations: Original series 178, after differencing 166 Residuals: SS = 865.115 (backforecasts excluded) MS = 5.275 DF = 164 Time series analysis - lecture 4

  6. CPI Clothing and FootwearSARIMA (1, 0, 0, 2, 1, 0) Final Estimates of Parameters Type Coef SE Coef T P AR 1 0.8145 0.0460 17.72 0.000 SAR 12 -0.6092 0.0830 -7.34 0.000 SAR 24 -0.2429 0.0843 -2.88 0.005 Constant 0.3275 0.1557 2.10 0.037 Differencing: 0 regular, 1 seasonal of order 12 Number of observations: Original series 178, after differencing 166 Residuals: SS = 651.883 (backforecasts excluded) MS = 4.024 DF = 162 Time series analysis - lecture 4

  7. CPI Clothing and FootwearSARIMA (1, 0, 0, 2, 1, 0) residuals Time series analysis - lecture 4

  8. Models for multiple time series of data • Dynamic regression models • General input-output models • Models for intervention analysis • Response surface methodologies • Smoothing of multiple time series • Change-point detection Time series analysis - lecture 4

  9. Percentage of carbon dioxide in the output from a gas furnace Time series analysis - lecture 4

  10. The dynamic regression model where Yt = the forecast variable (output series); Xt = the explanatory variable (input series); Nt = the combined effect of all other factors influencing Yt(the noise); (B) = (0 + 1B + 2B2 + … +kBk), wherekis the order of the transfer function Time series analysis - lecture 4

  11. Using the SAS procedure AUTOREG- regression in which the noise is modelled as an autoregressive sequence Consider a dataset with one input variable (gasrate) and one output variable (CO2) data newdata; set mining.gasfurnace; gasrate1= lag1(gasrate); gasrate2= lag2(gasrate); gasrate3=lag3(gasrate); gasrate4= lag4(gasrate); run; procautoreg data=newdata; model CO2 = gasrate/nlag=1; model CO2 = gasrate gasrate1/nlag=1; model CO2 = gasrate gasrate1 gasrate2/nlag=1; model CO2 = gasrate gasrate1 gasrate 2 gasrate3/nlag=1; model CO2 = gasrate gasrate1 gasrate2 gasrate3 gasrate4/nlag=1; output out=model4 residual=res; run; Time series analysis - lecture 4

  12. Predicted and observed levels of carbon dioxide in the output from a gas furnace- dynamic regression model with inputs time-lagged up to 4 steps Time series analysis - lecture 4

  13. No. air passengers by week in Sweden-original series and seasonally differenced data Time series analysis - lecture 4

  14. Intervention analysis where Yt = the forecast variable (output series); Xt = the explanatory variable (step or pulse function); Nt = the combined effect of all other factors influencing Yt(the noise); (B) = (0 + 1B + 2B2 + … + kBk), wherekis the order of the transfer function Time series analysis - lecture 4

More Related