[R] How to use "lag"?

[Remigijus Lapinskas]

I use the following two function for a lagged regression:

lm.lag=function(y,lag=1) summary(lm(embed(y,lag+1)[,1]~embed(y,lag+1)[,2:(lag+1)]))
lm.lag.x=function(y,x,lag=1) summary(lm(embed(y,lag+1)[,1]~embed(x,lag+1)[,2:(lag+1)]))

for, respectively,

y_t=a+b_1*y_t-1+...+b_lag*y_t-lag
y_t=a+b_1*x_t-1+...+b_lag*x_t-lag

I am not quite sure whether this an answer to your question, but here
are two examples:

set.seed(7)
ar1=arima.sim(n=300,list(ar=0.8))
lm.lag(ar1)
lm.lag.x(ar1,ar1)

set.seed(8)
ar3=arima.sim(n = 200, list(ar = c(0.4, -0.5, 0.7)))
lm.lag(ar3,3) 
lm.lag.x(ar3,ar3,3)

Best wishes,
Rem




Saturday, March 5, 2005, 6:14:15 PM, you wrote:

SG>       Is it possible to fit a lagged regression, "y[t]=b0+b1*x[t-1]+e",
SG> using the function "lag"?  If so, how?  If not, of what use is the
SG> function "lag"?  I get the same answer from y~x as y~lag(x), whether
SG> using lm or arima.  I found it using y~c(NA, x[-length(x)])). Consider
SG> the following: 

 >> set.seed(1)
 >> x <- rep(c(rep(0, 4), 9), len=9)
 >> y <- (rep(c(rep(0, 5), 9), len=9)+rnorm(9)) # y[t] = x[t-1]+e
 >>
 >> lm(y~x)
SG> (Intercept)            x 
SG>      1.2872      -0.1064 
 >> lm(y~lag(x))
SG> (Intercept)       lag(x) 
SG>      1.2872      -0.1064 
 >> arima(y, xreg=x)
SG>       intercept        x
SG>          1.2872  -0.1064
SG> s.e.     0.9009   0.3003
SG> sigma^2 estimated as 6.492:  log likelihood = -21.19,  aic = 48.38
 >> arima(y, xreg=lag(x))
SG>       intercept   lag(x)
SG>          1.2872  -0.1064
SG> s.e.     0.9009   0.3003
 >> arima(y, xreg=c(NA, x[-9]))
SG>       intercept  c(NA, x[-9])
SG>          0.4392        0.8600
SG> s.e.     0.2372        0.0745
SG> sigma^2 estimated as 0.3937:  log likelihood = -7.62,  aic = 21.25
 >> arima(ts(y), xreg=lag(ts(x)))
SG> arima(x = ts(y), xreg = lag(ts(x)))
SG>       intercept  lag(ts(x))
SG>          1.2872     -0.1064
SG> s.e.     0.9009      0.3003
SG> sigma^2 estimated as 6.492:  log likelihood = -21.19,  aic = 48.38
 
SG>       Thanks for your help. 
SG>       Spencer Graves

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