ITBAL Session5

Assignment1:

> z<- read.csv(file.choose(),header=T)
> close<- z$Close[10:95]
> close
 [1] 5380.35 5362.95 5366.30 5421.00 5412.85 5415.35 5386.70 5350.25 5334.60 5287.80 5315.05 5258.50 5253.75 5274.00 5225.70 5238.40 5342.10 5358.70 5363.45 5390.00
[21] 5431.00 5435.35 5577.65 5610.00 5600.05 5554.25 5691.15 5669.60 5673.90 5663.45 5649.50 5703.30 5718.80 5731.25 5787.60 5746.95 5676.00 5704.60 5652.15 5708.05
[41] 5676.05 5687.25 5648.00 5660.25 5718.70 5684.25 5717.15 5691.40 5705.30 5664.30 5665.60 5597.90 5619.70 5645.05 5697.70 5704.20 5724.40 5760.10 5738.75 5686.25
[61] 5683.70 5666.95 5631.00 5574.05 5571.40 5571.55 5614.80 5627.75 5626.60 5635.90 5727.45 5825.00 5879.85 5870.95 5889.25 5900.50 5930.90 5907.40 5908.90 5898.80
[81] 5888.00 5851.50 5879.60 5857.90 5896.80 5929.60
> close.ts<- ts(close,deltat=1/252)
> close.ts
Time Series:
Start = c(1, 1)
End = c(1, 86)
Frequency = 252
 [1] 5380.35 5362.95 5366.30 5421.00 5412.85 5415.35 5386.70 5350.25 5334.60 5287.80 5315.05 5258.50 5253.75 5274.00 5225.70 5238.40 5342.10 5358.70 5363.45 5390.00
[21] 5431.00 5435.35 5577.65 5610.00 5600.05 5554.25 5691.15 5669.60 5673.90 5663.45 5649.50 5703.30 5718.80 5731.25 5787.60 5746.95 5676.00 5704.60 5652.15 5708.05
[41] 5676.05 5687.25 5648.00 5660.25 5718.70 5684.25 5717.15 5691.40 5705.30 5664.30 5665.60 5597.90 5619.70 5645.05 5697.70 5704.20 5724.40 5760.10 5738.75 5686.25
[61] 5683.70 5666.95 5631.00 5574.05 5571.40 5571.55 5614.80 5627.75 5626.60 5635.90 5727.45 5825.00 5879.85 5870.95 5889.25 5900.50 5930.90 5907.40 5908.90 5898.80
[81] 5888.00 5851.50 5879.60 5857.90 5896.80 5929.60
> z.diff<- diff(close.ts)
> returns=z.diff/lag(close.ts,k=-1)
> returns
Time Series:
Start = c(1, 2)
End = c(1, 86)
Frequency = 252
 [1] -3.233990e-03  6.246562e-04  1.019324e-02 -1.503413e-03  4.618639e-04 -5.290517e-03 -6.766666e-03 -2.925097e-03 -8.772916e-03  5.153372e-03 -1.063960e-02
[12] -9.032994e-04  3.854390e-03 -9.158134e-03  2.430296e-03  1.979612e-02  3.107392e-03  8.864090e-04  4.950172e-03  7.606679e-03  8.009575e-04  2.618047e-02
[23]  5.799934e-03 -1.773619e-03 -8.178498e-03  2.464779e-02 -3.786581e-03  7.584309e-04 -1.841767e-03 -2.463163e-03  9.522967e-03  2.717725e-03  2.177030e-03
[34]  9.832061e-03 -7.023637e-03 -1.234568e-02  5.038760e-03 -9.194334e-03  9.890042e-03 -5.606118e-03  1.973203e-03 -6.901402e-03  2.168909e-03  1.032640e-02
[45] -6.024096e-03  5.787923e-03 -4.503992e-03  2.442281e-03 -7.186300e-03  2.295076e-04 -1.194931e-02  3.894318e-03  4.510917e-03  9.326755e-03  1.140811e-03
[56]  3.541250e-03  6.236461e-03 -3.706533e-03 -9.148334e-03 -4.484502e-04 -2.947024e-03 -6.343800e-03 -1.011366e-02 -4.754173e-04  2.692321e-05  7.762651e-03
[67]  2.306405e-03 -2.043445e-04  1.652863e-03  1.624408e-02  1.703201e-02  9.416309e-03 -1.513644e-03  3.117042e-03  1.910260e-03  5.152106e-03 -3.962299e-03
[78]  2.539188e-04 -1.709286e-03 -1.830881e-03 -6.199049e-03  4.802187e-03 -3.690727e-03  6.640605e-03  5.562339e-03
> plot(returns)

Assignment 2:

> z<-read.csv(file.choose(),header=T)

> head(z)

  age ed employ address income debtinc creddebt othdebt default

1  41  3     17      12    176     9.3    11.36    5.01       1

2  27  1     10       6     31    17.3     1.36    4.00       0

3  40  1     15      14     55     5.5     0.86    2.17       0

4  41  1     15      14    120     2.9     2.66    0.82       0

5  24  2      2       0     28    17.3     1.79    3.06       1

6  41  2      5       5     25    10.2     0.39    2.16       0

> data<- z[1:700,1:9]

> sapply(data,mean)

       age         ed     employ    address     income    debtinc   creddebt    othdebt    default 

34.8600000  1.7228571  8.3885714  8.2785714 45.6014286 10.2605714  1.5534571  3.0582286  0.2614286 

> data$ed<-factor(data$ed)

> logit.est<-glm(default~age+employ+address+income+debtinc+creddebt+othdebt,data=data,family="binomial")

> summary(logit.est)

Call:

glm(formula = default ~ age + employ + address + income + debtinc + 

    creddebt + othdebt, family = "binomial", data = data)

Deviance Residuals: 

    Min       1Q   Median       3Q      Max  

-2.3659  -0.6516  -0.2882   0.2625   2.9757  

Coefficients:

             Estimate Std. Error z value Pr(>|z|)    

(Intercept) -1.376573   0.571560  -2.408   0.0160 *  

age          0.033712   0.017342   1.944   0.0519 .  

employ      -0.265086   0.031999  -8.284  < 2e-16 ***

address     -0.103960   0.023192  -4.483 7.38e-06 ***

income      -0.007566   0.008095  -0.935   0.3500    

debtinc      0.065099   0.030621   2.126   0.0335 *  

creddebt     0.628475   0.113759   5.525 3.30e-08 ***

othdebt      0.070761   0.077682   0.911   0.3623    

---

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 804.36  on 699  degrees of freedom

Residual deviance: 552.20  on 692  degrees of freedom

AIC: 568.2

Number of Fisher Scoring iterations: 6

> confint.default(logit.est)

                    2.5 %      97.5 %

(Intercept) -2.4968094058 -0.25633710

age         -0.0002768342  0.06770153

employ      -0.3278025120 -0.20236959

address     -0.1494167558 -0.05850405

income      -0.0234310297  0.00829931

debtinc      0.0050836856  0.12511410

creddebt     0.4055112145  0.85143903

othdebt     -0.0814939858  0.22301505

> logit.eg2<-with(z[701:850,1:8],data.frame(age=mean(age),employ=mean(employ),address=mean(address),income=mean(income),debtinc=mean(debtinc),creddebt=mean(creddebt),othdebt=mean(othdebt),ed=factor(1:3)))

> logit.eg2$prob<-predict(logit.est,newdata=logit.eg2,type="response")

> head(logit.eg2)

    age   employ  address   income  debtinc creddebt  othdebt ed      prob

1 35.82 9.393333 8.806667 51.68667 9.756667   1.6852 3.174933  1 0.1143839

2 35.82 9.393333 8.806667 51.68667 9.756667   1.6852 3.174933  2 0.1143839

3 35.82 9.393333 8.806667 51.68667 9.756667   1.6852 3.174933  3 0.1143839

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