[R] probit model on time series

[evil_iggy]

I have created a few versions of a probit model that predicts (gives me a
probability between 0 and 1) of a recession in the United States in the next
12 months. It uses some well known economic time series data I got from the
St. Lewis Fed’s website.
I got this to work with the following code:

#rebuild the object to include only the data I want in the model
predictors.TS <- cbind(NAPM.TS,
FEDFUNDS.TS,
IC4WSA.TS,
CurveSlope.TS,
CreditSpread.TS,
MCOILWTI.real.TS,
sp500Ret.TS)

recession.probModel <- glm(formula =
window(lag(recession.TS,k=12),start=c(1986,2),end=c(2010,7)) ~
window(predictors.TS,start=c(1986,2),end=c(2010,7)),
family=binomial(link="probit"))

that all works nicely and looks like I expected:

> summary(recession.probModel)

Call:
glm(formula = window(lag(recession.TS, k = 12), start = c(1986, 
    2), end = c(2010, 7)) ~ window(predictors.TS, start = c(1986, 
    2), end = c(2010, 7)), family = binomial(link = "probit"))

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-1.70829  -0.12217  -0.01181   0.00000   2.31322  

Coefficients:
                                                                             
Estimate Std. Error z value Pr(>|z|)    
(Intercept)                                                                 
1.513e+01  6.122e+00   2.472  0.01345 *  
window(predictors.TS, start = c(1986, 2), end = c(2010, 7))NAPM.TS         
-2.128e-01  7.017e-02  -3.032  0.00243 ** 
window(predictors.TS, start = c(1986, 2), end = c(2010, 7))FEDFUNDS.TS      
2.993e-01  1.140e-01   2.626  0.00864 ** 
window(predictors.TS, start = c(1986, 2), end = c(2010, 7))IC4WSA.TS       
-2.822e-05  9.675e-06  -2.917  0.00353 ** 
window(predictors.TS, start = c(1986, 2), end = c(2010, 7))CurveSlope.TS   
-4.451e-01  3.319e-01  -1.341  0.17990    
window(predictors.TS, start = c(1986, 2), end = c(2010, 7))CreditSpread.TS 
-2.209e-01  1.300e+00  -0.170  0.86507    
window(predictors.TS, start = c(1986, 2), end = c(2010, 7))MCOILWTI.real.TS 
9.426e-02  2.122e-02   4.442  8.9e-06 ***
window(predictors.TS, start = c(1986, 2), end = c(2010, 7))sp500Ret.TS     
-8.458e-01  3.799e+00  -0.223  0.82384    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 222.512  on 293  degrees of freedom
Residual deviance:  97.135  on 286  degrees of freedom
AIC: 113.14

Number of Fisher Scoring iterations: 10

Now what I want to get it to do is use the model I just estimated to predict
a recession probability in, say, 2010-8, a period for which I do have data.
To do that I tried the following:

predict.glm(object=recession.probModel,
newdata=window(predictors.TS,start=c(2010,8),end=c(2010,8)),
type=”response”)

I expected this to output one data point but instead it spits out a vector
of 286 values, none of which is between 0 and 1. Any idea of how I can get
it to tell me what the predicted probability is for Aug 2008 given the data
I have for the independent variables? Should I not be trying to do this as a
time series? I’m at a bit of a loss here so any help pointing me in the
right direction would be appreciated.

My ultemate goal is to run a rolling estimation of the model, holding out
the most recent periods to see how well it does on out of sample prediction
and to get a forecast of the near future.

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