forecasting cpi
DESCRIPTION
Forecasting CPI. Xiang Huang , Wenjie Huang, Teng Wang , Hong Wang Benjamin Wright , Naiwen Chang, Jake Stamper. Definition. The consumer price index (CPI) measures the cost of a standard basket of goods and services commonly purchased by households. - PowerPoint PPT PresentationTRANSCRIPT
Forecasting CPI
Xiang Huang , Wenjie Huang, Teng Wang , Hong Wang Benjamin Wright , Naiwen Chang, Jake Stamper
Definition
The consumer price index (CPI) measures the cost of a standard basket of goods and services commonly purchased by households.
The index is published monthly by the Bureau of Labor Statistics, and is used to calculate the rate of inflation.
CPI index since 1983
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CPI
Trace Histogram
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Series: CPISample 1 340Observations 340
Mean 158.9701Median 159.7500Maximum 224.4330Minimum 97.90000Std. Dev. 36.57263Skewness 0.045908Kurtosis 1.864855
Jarque-Bera 18.37395Probability 0.000102
Time Trend Forecast
Correlogram of CPI
Evidence of an
evolutionary series.
Use first-differencing to pre-whiten and obtain a stationary series.
First-Difference of CPI Trace Histogram
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DCPI
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Series: DCPISample 1 340Observations 339
Mean 0.373254Median 0.400000Maximum 2.700000Minimum -3.928000Std. Dev. 0.481705Skewness -2.153560Kurtosis 25.06468
Jarque-Bera 7138.795Probability 0.000000
Correlogram of DCPI
AddAR(1),AR(2),and
MA(12)
Unit Root of DCPI
Augmented Dickey-Fuller is sufficiently negative, rejecting the presence of a unit root.
ARIMA MODEL OF DCPI
Tan theta=0.36/0.28=1.2857Theta=52.125 degreeCycle=360/52.125=6.9 years
Cycle Calculation:
ARIMA MODEL OF DCPI
Actual, Fitted and Residuals Graph
Histogram of Residuals
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Residual Actual Fitted
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Series: ResidualsSample 4 340Observations 337
Mean 4.09e-05Median -0.024544Maximum 2.109857Minimum -2.862702Std. Dev. 0.413371Skewness -1.009439Kurtosis 14.32945
Jarque-Bera 1859.569Probability 0.000000
Correlogram of Residuals Breusch-Godfrey
Serial Correlation Test
Correlogram of Square Residuals
Add ARCH(1)
Add ARCH(1) and GARCH(1) in ARIMA model
Correlogram
Drop AR(2)
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SDRESID
Trace of the standardized residuals
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Series: SDRESIDSample 1 340Observations 338
Mean 0.044213Median -0.031016Maximum 4.034350Minimum -3.266398Std. Dev. 1.000513Skewness 0.540747Kurtosis 4.851605
Jarque-Bera 64.75614Probability 0.000000
Histogram of Standardized Squared Residuals
Correlogram of Standardized Squared Residuals
Exponential Smoothing ForecastSample: 1 340
Included observations: 340
Method: Single Exponential
Original Series: CPI
Forecast Series: CPISM
Parameters: Alpha 0.9990
Sum of Squared Residuals 1027.477
Root Mean Squared Error 1.738388
End of Period Levels: Mean 223.442055779
Attempt to Create Distributed Lag Model
No Granger Causality for Relevant Variables• GDP• Unemployment Rate• Capacity Utilization• Industrial Production• Manufacturing Production• Commercial and Industrial Loans• Consumer Loans• Consumer Sentiment• Money Supply (M2)• Federal Funds Rate
Without Granger Causality, no distributed lag model could be crated
Comparison of Different ModelsMethod Forecast of CPI for April 2011
Time Trend Forecast 221.89
ARIMA Model 224.19
GARCH(1,1) MODEL 224.14
Exponential Smoothing 223.44
True value CPI in April 2011: 224.43True value CPI in April 2011: 224.43
Actual Value of CPI in April 2011: 224.43
Forecast for May 2011
CPI Monthly Inflation Rate (Annualized)
Point Forecast 225.05 3.37%
95 Percent Confidence Interval 224.22 to 225.88 -1.12% to 8.03%