1 Econ 240 C Lecture 3. 2 3 4 5 6 1 White noise inputoutput 1/(1 – z) White noise input output...
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Transcript of 1 Econ 240 C Lecture 3. 2 3 4 5 6 1 White noise inputoutput 1/(1 – z) White noise input output...
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Econ 240 CEcon 240 C
Lecture 3Lecture 3
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White noiseWhite noise
inputoutput
1/(1 – z)
White noise
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Random walk
SynthesisSynthesis
1/(1 – bz)
White noise
inputoutput
First order autoregressive
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Simulated Random walkSimulated Random walk
• Eviews, sample 1 1000, gen wn = nrnd
• EViews, sample 1 1, gen rw = wn
• Sample 2 1000, gen rw = rw(-1) + wn
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Simulated First Order Simulated First Order Autoregressive Process Autoregressive Process
• Eviews, sample 1 1000, gen wn = nrnd
• EViews, sample 1 1, gen arone = wn
• Sample 2 1000, gen arone = b*arone(-1) + wn
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SystematicsSystematics
• b =1, random walk
• b = 0.9
• b = 0.5
• b = 0.1
• b = 0, white noise
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ARONE
arone = 0.9*arone(-1) + wn
Arone(t) = 0.9*arone(t-1) = wn(t)Arone(t) = 0.9*arone(t-1) = wn(t)
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Arone(t) = 0.5*arone(t-1) + wn(t)Arone(t) = 0.5*arone(t-1) + wn(t)
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ARONE
arone = 0.5*arone(-1) + wn
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Arone(t) = 0.1*arone(t-1) + wn(t)Arone(t) = 0.1*arone(t-1) + wn(t)
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arone = 0.1*aronne(-1) + wn
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Moving AveragesMoving Averages
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BloombergBloomberg
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Time Series ConceptsTime Series Concepts
• Analysis and Synthesis
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AnalysisAnalysis
• Model a real time seies in terms of its components
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Total Returns to Standard and Total Returns to Standard and Poors 500, Monthly, 1970-2003Poors 500, Monthly, 1970-2003
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SPRETURN
Total Returns for the Standard and Poors 500
Source: FRED http://research.stlouisfed.org/fred/
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Trace of ln S&P 500(t) Trace of ln S&P 500(t)
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TIME
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Logarithm of Total Returns to Standard & Poors 500
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ModelModel
• Ln S&P500(t) = a + b*t + e(t)
• time series = linear trend + error
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Dependent Variable: LNSP500Method: Least SquaresSample(adjusted): 1970:01 2003:02Included observations: 398 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
C 4.049837 0.022383 180.9370 0.0000TIME 0.010867 9.76E-05 111.3580 0.0000
R-squared 0.969054 Mean dependent var 6.207030 Adjusted R-squared 0.968976 S.D. dependent var 1.269965 S.E. of regression 0.223686 Akaike info criterion -0.152131 Sum squared resid 19.81410 Schwarz criterion -0.132098 Log likelihood 32.27404 F-statistic 12400.61Durbin-Watson stat 0.041769 Prob(F-statistic) 0.000000
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ERROR
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Series: ResidualsSample 1970:01 2003:02Observations 398
Mean 2.59E-15Median -0.036203Maximum 0.478857Minimum -0.494261Std. Dev. 0.223405Skewness 0.419110Kurtosis 2.377547
Jarque-Bera 18.07682Probability 0.000119
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Time Series Components ModelTime Series Components Model
• Time series = trend + cycle + seasonal + error
• two components, trend and seasonal, are time dependent and are called non-stationary
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SynthesisSynthesis
• The Box-Jenkins approach is to start with the simplest building block to a time series, white noise and build from there, or synthesize.
• Non-stationary components such as trend and seasonal are removed by differencing
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First DifferenceFirst Difference
• Lnsp500(t) - lnsp500(t-1) = dlnsp500(t)
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DLNSP500
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Series: DLNSP500Sample 1970:02 2003:02Observations 397
Mean 0.008625Median 0.011000Maximum 0.155371Minimum -0.242533Std. Dev. 0.045661Skewness -0.614602Kurtosis 5.494033
Jarque-Bera 127.8860Probability 0.000000
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LNDNSROTM
Log of Rotterdam Inport Price: Dark Northern Spring
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First Difference in Log of Rotterdam Import Price: Dark Northern Spring
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Series: DLNDNSROTMSample 1971:08 2001:12Observations 365
Mean 0.002290Median 0.000000Maximum 0.277165Minimum -0.244453Std. Dev. 0.052324Skewness 0.141729Kurtosis 7.454779
Jarque-Bera 303.0323Probability 0.000000
Histogram: First Difference of Log of Rotterdam Import Price, DNS
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Time seriesTime series
• A sequence of values indexed by time
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Stationary time seriesStationary time series
• A sequence of values indexed by time where, for example, the first half of the time series is indistinguishable from the last half
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Stochastic Stationary Time Stochastic Stationary Time SeriesSeries
• A sequence of random values, indexed by time, where the time series is not time dependent
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Summary of ConceptsSummary of Concepts
• Analysis and Synthesis
• Stationary and Evolutionary
• Deterministic and Stochastic
• Time Series Components Model
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White Noise SynthesisWhite Noise Synthesis
• Eviews: New Workfile– undated 1 1000
• Genr wn = nrnd
• 1000 observations N(0,1)
• Index them by time in the order they were drawn from the random number generator
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Series: WNSample 1 1000Observations 1000
Mean 0.002336Median 0.017618Maximum 2.967726Minimum -3.713726Std. Dev. 0.992117Skewness -0.099788Kurtosis 2.973119
Jarque-Bera 1.689727Probability 0.429616
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SynthesisSynthesis• Random Walk
• RW(t) -RW(t-1) = WN(t) = dRW(t)
• or RW(t) = RW (t-1) + WN(t)
• lag by one: RW(t-1) = RW(t-2) + WN(t-1)
• substitute: RW(t) = RW(t-2) + WN(t) + WN(t-1)
• continue with lagging and substitutingRW(t) = WN(t) + WN (t-1) + WN (t-2) + ...
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Part IPart I
• Modeling Economic Time Series
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Total Returns to Standard and Total Returns to Standard and Poors 500, Monthly, 1970-2003Poors 500, Monthly, 1970-2003
0
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SPRETURN
Total Returns for the Standard and Poors 500
Source: FRED http://research.stlouisfed.org/fred/
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Analysis (Decomposition)Analysis (Decomposition)
• Lesson one: plot the time series
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Model One: Random WalksModel One: Random Walks
• we can characterize the logarithm of total returns to the Standard and Poors 500 as trend plus a random walk.
• Ln S&P 500(t) = trend + random walk = a + b*t + RW(t)
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Trace of ln S&P 500(t) Trace of ln S&P 500(t)
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Logarithm of Total Returns to Standard & Poors 500
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Analysis(Decomposition)Analysis(Decomposition)
• Lesson one: Plot the time series
• Lesson two: Use logarithmic transformation to linearize
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Ln S&P 500(t) = trend + RW(t)Ln S&P 500(t) = trend + RW(t)
• Trend is an evolutionary process, i.e. depends on time explicitly, a + b*t, rather than being a stationary process, i. e. independent of time
• A random walk is also an evolutionary process, as we will see, and hence is not stationary
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Model One: Random WalksModel One: Random Walks
• This model of the Standard and Poors 500 is an approximation. As we will see, a random walk could wander off, upward or downward, without limit.
• Certainly we do not expect the Standard and Poors to move to zero or into negative territory. So its lower bound is zero, and its model is an approximation.
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Model One: Random WalksModel One: Random Walks
• The random walk model as an approximation to economic time series– Stock Indices– Commodity Prices– Exchange Rates
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Model Two: White NoiseModel Two: White Noise
• we saw that the difference in a random walk was white noise.
)()( tWNtRW
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Model Two: White NoiseModel Two: White Noise
• How good an approximation is the white noise model?
• Take first difference of ln S&P 500(t) and plot it and look at its histogram.
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Trace of ln S&P 500(t) – ln S&P(t-1)Trace of ln S&P 500(t) – ln S&P(t-1)
-0.3
-0.2
-0.1
0.0
0.1
0.2
70 75 80 85 90 95 00
DLNSP500
Trace of lnsp500 - lnsp500(-1)
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Histogram of Histogram of ln S&P 500(t) – ln S&P(t-1)ln S&P 500(t) – ln S&P(t-1)
0
20
40
60
80
100
-0.2 -0.1 0.0 0.1
Series: DLNSP500Sample 1970:02 2003:02Observations 397
Mean 0.008625Median 0.011000Maximum 0.155371Minimum -0.242533Std. Dev. 0.045661Skewness -0.614602Kurtosis 5.494033
Jarque-Bera 127.8860Probability 0.000000
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The First Difference of ln S&P The First Difference of ln S&P 500(t)500(t)
ln S&P 500(t)=ln S&P 500(t) - ln S&P 500(t-1) ln S&P 500(t) = a + b*t + RW(t) -
{a + b*(t-1) + RW(t-1)} ln S&P 500(t) = b + RW(t) = b + WN(t)
• Note that differencing ln S&P 500(t) where both components, trend and the random walk were evolutionary, results in two components, a constant and white noise, that are stationary.
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Analysis(Decomposition)Analysis(Decomposition)
• Lesson one: Plot the time series
• Lesson two: Use logarithmic transformation to linearize
• Lesson three: Use difference transformation to reduce an evolutionary process to a stationary process
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Model Two: White NoiseModel Two: White Noise
• Kurtosis or fat tails tend to characterize financial time series
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The Lag Operator, ZThe Lag Operator, Z
• Z x(t) = x(t-1)• Zn x(t) = x(t-n)• RW(t) – RW(t-1) = (1 – Z) RW(t) = RW(t) = WN(t)• So the difference operator, can be written in
terms of the lag operator, = (1 – Z)
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Model Three: Model Three: Autoregressive Time Series of Autoregressive Time Series of
Order OneOrder One• An analogy to our model of trend plus
shock for the logarithm of the Standard Poors is inertia plus shock for an economic time series such as the ratio of inventory to sales for total business
• Source: FRED http://research.stlouisfed.org/fred/
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Trace of Inventory to Sales, Trace of Inventory to Sales, Total Business Total Business
1.30
1.35
1.40
1.45
1.50
1.55
1.60
92 93 94 95 96 97 98 99 00 01 02 03
RATIOINVSALE
Ratio of Inventory to Sales, Monthly, 1992:01-2003:01
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AnalogyAnalogy
• Trend plus random walk:
• Ln S&P 500(t) = a + b*t + RW(t)
• where RW(t) = RW(t-1) + WN(t)
• inertia plus shock
• Ratioinvsale(t) = b*Ratioinvsale(t-1) + WN(t)
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Model Three: Autoregressive of Model Three: Autoregressive of First OrderFirst Order
• Note: RW(t) = 1*RW(t-1) + WN(t)
• where the coefficient b = 1
• Contrast ARONE(t) = b*ARONE(t-1) + WN(t)
• What would happen if b were greater than one?
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Using Simulation to Explore Using Simulation to Explore Time Series BehaviorTime Series Behavior
• Simulating White Noise:
• EVIEWS: new workfile, irregular, 1000 observations, GENR WN = NRND
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Trace of Simulated White Noise:Trace of Simulated White Noise:100 Observations100 Observations
-3
-2
-1
0
1
2
3
10 20 30 40 50 60 70 80 90 100
WN
Simulated White Noise
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Histogram of Simulated White Histogram of Simulated White NoiseNoise
0
20
40
60
80
100
120
-3 -2 -1 0 1 2 3 4
Series: WNSample 1 1000Observations 1000
Mean 0.008260Median -0.003042Maximum 3.782479Minimum -3.267831Std. Dev. 1.005635Skewness -0.047213Kurtosis 3.020531
Jarque-Bera 0.389072Probability 0.823216
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Simulated ARONE ProcessSimulated ARONE Process
• SMPL 1 1, GENR ARONE = WN
• SMPL 2 1000
• GENR ARONE =1.1* ARONE(-1) + WN
• Smpl 1 1000
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Simulated Unstable First Order Simulated Unstable First Order Autoregressive Process Autoregressive Process
-20000
-15000
-10000
-5000
0
10 20 30 40 50 60 70 80 90 100
ARONE
First 100 Observations of ARONE = 1.1*Arone(-1) + WN
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First 10 Observations of ARONEFirst 10 Observations of ARONE
obs WN ARONE
1 -1.204627 -1.2046272 -1.728779 -3.0538693 1.478125 -1.8811314 -0.325830 -2.3950735 -0.593882 -3.2284636 0.787438 -2.7638727 0.157040 -2.8832198 -0.211357 -3.3828989 -0.722152 -4.44334010 0.775963 -4.111711
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Model Three: AutoregressiveModel Three: Autoregressive
• What if b= -1.1?
• ARONE*(t) = -1.1*ARONE*(t-1) + WN(t)
• SMPL 1 1, GENR ARONE* = WN
• SMPL 2 1000
• GENR ARONE* = -1.1*ARONE*(-1) + WN
• SMPL 1 1000
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Simulated Autoregressive, b=-1.1Simulated Autoregressive, b=-1.1
-400
-200
0
200
400
5 10 15 20 25 30 35 40 45 50 55 60
ARONESTAR
Simulated First Order Autoregressive Process, b = -1.1
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Model Three: ConclusionModel Three: Conclusion
• For Stability ( stationarity) -1<b<1
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Part IIPart II
• Forecasting: A preview of coming attractions
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Ratio of Inventory to SalesRatio of Inventory to Sales
• EVIEWS Model: Ratioinvsale(t) = c + AR(1)
• Ratioinvsale is a constant plus an autoregressive process of the first order
• AR(t) = b*AR(t-1) + WN(t)
• Note: Ratioinvsale(t) - c = AR(t), so
• Ratioinvsale(t) - c = b*{ Ratioinvsale(t-1) - c} + WN (t)
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Ratio of Inventory to SalesRatio of Inventory to Sales
• Use EVIEWS to estimate coefficients c and b.
• Forecast of Ratioinvsale at time t is based on knowledge at time t-1 and earlier (information base)
• Forecast at time t-1 of Ratioinvsale at time t is our expected value of Ratioinvsale at time t
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One Period Ahead ForecastOne Period Ahead Forecast
• Et-1[Ratioinvsale(t)] is:
• Et-1[Ratioinvsale(t) - c] =
• Et-1[Ratioinvsale(t)] - c =
• Forecast - c = b*Et-1[Ratioinvsale(t-1) - c] + Et-1[WN(t)]
• Forecast = c + b*Ratioinvsale(t-1) -b*c + 0
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Dependent Variable: RATIOINVSALEMethod: Least SquaresDate: 04/08/03 Time: 13:56Sample(adjusted): 1992:02 2003:01Included observations: 132 after adjusting endpoints
Convergence achieved after 3 iterations
Variable Coefficient Std. Error t-Statistic Prob.
C 1.417293 0.030431 46.57405 0.0000AR(1) 0.954517 0.024017 39.74276 0.0000
R-squared 0.923954 Mean dependent var 1.449091
Adjusted R-squared 0.923369 S.D. dependent var 0.046879
S.E. of regression 0.012977 Akaike info criterion -5.836210
Sum squared resid 0.021893 Schwarz criterion -5.792531
Log likelihood 387.1898 F-statistic 1579.487
Durbin-Watson stat 2.674982 Prob(F-statistic) 0.000000
Inverted AR Roots .95
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How Good is This Estimated How Good is This Estimated Model?Model?
-0.06
-0.04
-0.02
0.00
0.02
0.04
0.06
1.30
1.35
1.40
1.45
1.50
1.55
1.60
93 94 95 96 97 98 99 00 01 02 03
Residual Actual Fitted
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Plot of the Estimated ResidualsPlot of the Estimated Residuals
0
5
10
15
20
25
-0.050 -0.025 0.000 0.025
Series: ResidualsSample 1992:02 2003:01Observations 132
Mean -2.74E-13Median 0.000351Maximum 0.042397Minimum -0.048512Std. Dev. 0.012928Skewness 0.009594Kurtosis 4.435641
Jarque-Bera 11.33788Probability 0.003452
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Forecast for Ratio of Inventory Forecast for Ratio of Inventory to Sales for February 2003to Sales for February 2003
• E2003:01 [Ratioinvsale(2003:02)= c - b*c + b*Ratioinvsale(2003:02)
• Forecast = 1.417 - 0.954*1.417 + 0.954*1.360
• Forecast = 0.06514 + 1.29744
• Forecast = 1.36528
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How Well Do We Know This How Well Do We Know This Value of the Forecast?Value of the Forecast?
• Standard error of the regression = 0.0130
• Approximate 95% confidence interval for the one period ahead forecast = forecast +/- 2*SER
• Ratioinvsale(2003:02) = 1.36528 +/- 2*.0130
• interval for the forecast 1.34<forecast<1.39
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Trace of Inventory to Sales, Trace of Inventory to Sales, Total Business Total Business
1.30
1.35
1.40
1.45
1.50
1.55
1.60
92 93 94 95 96 97 98 99 00 01 02 03
RATIOINVSALE
Ratio of Inventory to Sales, Monthly, 1992:01-2003:01
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Lessons About ARIMA Lessons About ARIMA Forecasting ModelsForecasting Models
• Use the past to forecast the future
• “sophisticated” extrapolation models
• competitive extrapolation models– use the mean as a forecast for a stationary time
series, Et-1[y(t)] = mean of y(t)
– next period is the same as this period for a stationary time series and for random walks, Et-1[y(t)] = y(t-1)
– extrapolate trend for an evolutionary trended time series, Et-1[y(t)] = a + b*t = y(t-1) + b