Computes the Phillips and Hansen (1990) Fully Modified OLS estimator.
1 2 
x 
[ 
y 
[ 
deter 
[ 
kernel 
[ 
bandwidth 
[ 
demeaning 
[ 
check 
[ 
... 
Arguments passed to 
The equation for which the FMOLS estimator is calculated:
y = δ * D + β * x + u
with D as the deterministics matrix. Then θ = (δ', β')' is the full parameter vector.
The calculation of tvalues and the variancecovariance matrix is only
possible, if y
is onedimensional.
[cointReg
]. List with components:
delta
[numeric
 matrix
]coefficients as vector / matrix
beta
[numeric
 matrix
]coefficients as vector / matrix
theta
[numeric
 matrix
]combined coefficients of
beta
and delta
as vector / matrix
sd.theta
[numeric
]standard errors for theta
t.theta
[numeric
]tvalues for theta
p.theta
[numeric
]pvalues for theta
residuals
[numeric
]FMOLS residuals (first value is always missing)
omega.u.v
[numeric
]conditional longrun variance based on OLS residuals.
varmat
[matrix
]variancecovariance matrix
Omega
[list
]the whole longrun variance matrix and parts of it
beta.OLS
[numeric
 matrix
]OLS coefficients as vector / matrix
delta.OLS
[numeric
 matrix
]OLS coefficients as vector / matrix
u.OLS
[numeric
]OLS residuals
bandwidth
[list
]number
and name
of bandwidth
kernel
[character
]abbr. name of kernel type
Phillips, P.C.B. and B. Hansen (1990): "Statistical Inference in Instrumental Variables Regression with I(1) Processes," Review of Economic Studies, 57, 99–125, DOI:10.2307/2297545.
Other cointReg: cointRegD
,
cointRegIM
, cointReg
,
plot.cointReg
, print.cointReg
1 2 3 4 5 6 7 8 9  set.seed(1909)
x1 = cumsum(rnorm(100, mean = 0.05, sd = 0.1))
x2 = cumsum(rnorm(100, sd = 0.1)) + 1
x3 = cumsum(rnorm(100, sd = 0.2)) + 2
x = cbind(x1, x2, x3)
y = x1 + x2 + x3 + rnorm(100, sd = 0.2) + 1
deter = cbind(level = 1, trend = 1:100)
test = cointRegFM(x, y, deter, kernel = "ba", bandwidth = "and")
print(test)

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