| tvOLS | R Documentation | 
tvOLS estimate time-varying coefficient of univariate 
linear models using the kernel smoothing OLS.
tvOLS(x, ...)
## S3 method for class 'matrix'
tvOLS(
  x,
  y,
  z = NULL,
  ez = NULL,
  bw,
  est = c("lc", "ll"),
  tkernel = c("Triweight", "Epa", "Gaussian"),
  singular.ok = TRUE,
  ...
)
## S3 method for class 'tvlm'
tvOLS(x, ...)
## S3 method for class 'tvar'
tvOLS(x, ...)
## S3 method for class 'tvvar'
tvOLS(x, ...)
x | 
 An object used to select a method.  | 
... | 
 Other arguments passed to specific methods.  | 
y | 
 A vector with dependent variable.  | 
z | 
 A vector with the variable over which coefficients are smooth over.  | 
ez | 
 (optional) A scalar or vector with the smoothing values. If 
values are not included then the vector   | 
bw | 
 A numeric vector.  | 
est | 
 The nonparametric estimation method, one of "lc" (default) for linear constant or "ll" for local linear.  | 
tkernel | 
 A character, either "Triweight" (default), "Epa" or "Gaussian" kernel function.  | 
singular.ok | 
 Logical. If FALSE, a singular model is an error.  | 
tvOLS returns a list containing:
coefficients | 
 A vector of length obs, number of observations time observations.  | 
fitted | 
 A vector of length obs with the fitted values from the estimation.  | 
residuals | 
 A vector of length obs with the residuals from the estimation.  | 
bw for bandwidth selection, tvLM and
tvAR.
tau <- seq(1:500)/500
beta <- data.frame(beta1 = sin(2*pi*tau), beta2 = 2*tau)
X <- data.frame(X1 = rnorm(500), X2 = rchisq(500, df = 4))
error <- rt(500, df = 10)
y <- apply(X*beta, 1, sum) + error
coef.lm <- stats::lm(y~0+X1+X2, data = X)$coef
coef.tvlm <-  tvOLS(x = as.matrix(X), y = y, bw = 0.1)$coefficients
plot(tau, beta[, 1], type="l", main="", ylab = expression(beta[1]), xlab = expression(tau),
ylim = range(beta[,1], coef.tvlm[, 1]))
abline(h = coef.lm[1], col = 2)
lines(tau, coef.tvlm[, 1], col = 4)
legend("topright", c(expression(beta[1]), "lm", "tvlm"), col = c(1, 2, 4), bty="n", lty = 1)
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