Tau2 dating

0.1069 0.1028 0.0058 sigma^2 estimated as 0.0008173: log likelihood=176.1 AIC=-344.19 AICc=-343.67 BIC=-334.56 Training set error measures: ME RMSE MAE MPE MAPE MASE ACF1 Training set -0.0002070952 0.02806013 0.02273145 -21.42509 42.85735 0.7837788 -0.005665764 This model takes into account the level shift highlighted by the structural change analysis and the seasonal component at lag = 10 observed in the ACF plot.To represent the structural change as level shift, a regressor variable named as is defined as equal to zero for the timeline preceeding the breakpoint date and as equal to one afterwards such date.I am going to compare the following six ARIMA models (represented with the usual (p,d,q)(P, D, Q)[S] notation): 1. seasonal (0,0,0)(0,0,1)[10] with level shift regressor as intervention variable 5.non seasonal (1,1,1), as determined by auto.arima() within forecast package 2. seasonal (1,0,0)(0,0,1)[10] with level shift regressor as intervention variable 6.0.1318 0.0683 sigma^2 estimated as 0.0009316: log likelihood=167.94 AIC=-329.88 AICc=-329.57 BIC=-322.7 Series: excess_ts ARIMA(1,1,1) Coefficients: ar1 ma1 0.2224 -0.9258 s.e.0.1318 0.0683 sigma^2 estimated as 0.0009316: log likelihood=167.94 AIC=-329.88 AICc=-329.57 BIC=-322.7 Training set error measures: ME RMSE MAE MPE MAPE MASE ACF1 Training set -0.002931698 0.02995934 0.02405062 -27.05674 46.53832 0.8292635 -0.01005689 A spike at lag = 1 in the ACF plot suggests the presence of an auto-regressive component.As an example of intervention, a permanent level shift, as we will see in this tutorial.

lag2 -0.40952 0.26418 -1.550 0.1266 lag3 -0.34933 0.26464 -1.320 0.1921 lag4 -0.35207 0.25966 -1.356 0.1805 lag5 -0.39863 0.25053 -1.591 0.1171 lag6 -0.44797 0.23498 -1.906 0.0616 .Our second model candidate takes into account the seasonality observed at lag = 10.As a result, the candidate model (1,0,0)(0,0,1)[10] is investigated.That suggests the presence of a seasonal component with period = 10. First let us verify if regression against a constant is significative for our time series.Optimal (m 1)-segment partition: Call: breakpoints.formula(formula = excess_ts ~ 1) Breakpoints at observation number: m = 1 42 m = 2 20 42 m = 3 20 35 48 m = 4 20 35 50 66 m = 5 17 30 42 56 69 Corresponding to breakdates: m = 1 1670 m = 2 1648 1670 m = 3 1648 1663 1676 m = 4 1648 1663 1678 1694 m = 5 1645 1658 1670 1684 1697 Fit: m 0 1 2 3 4 5 RSS 0.07912 0.06840 0.06210 0.06022 0.05826 0.05894 BIC -327.84807 -330.97945 -330.08081 -323.78985 -317.68933 -307.92410 The BIC minimum value is reached when m = 1, hence just one break point is determined corresponding to year 1670.

Search for tau2 dating:

tau2 dating-76

In my previous tutorial Structural Changes in Global Warming I introduced the strucchange package and some basic examples to date structural breaks in time series.

Leave a Reply

Your email address will not be published. Required fields are marked *

One thought on “tau2 dating”

  1. You can even do the same yourself - it takes just a minute to register and verify your identity and then you're ready to start broadcasting your naughtiest, most intimate moments with like minded individuals over the internet.