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ppooled_hac1

Panel equation regressions

CALLING SEQUENCE

 res=ppooled_hac1(y,index,x,typvcv,win)

PARAMETERS

Input

• y = a (nobs*nindiv x 1) matrix of all of the individual's observations vertically concatenated. This matrix must include in the first column the dependent variable, the independent variables must follow accordingly.

• index = index vector that identifies each observation with an individual e.g. 1 (first 2 observations for individual # 1) 1 2 (next 1 observation for individual # 2) 3 (next 3 observations for individual # 3) 3 3

• x = a (nobs*nindiv x k) matrix of exogenous variables

• typvcv = 1, 2 or 3 with

  - 1 "clustered" covariance matrix of Arellano (1987) recommended when T is fixed and N large but also "works" when T is large and N fixed see Hansen C. B. (2007) (reference below)

  - 2 a Newey-west type (Driscoll-Kray) estimator recommended when T is large and N fixed

• win = the length of the Barlett window kernel estimator (default = automatic selection by Andrews (1991) using an AR(1) model)

Output

• res = a tlist with

• res('meth') = 'panel pooled'

• res('y') = y data vector

• res('x') = x data matrix

• res('nobs') = nobs

• res('nvar') = nvars

• res('beta') = bhat

• res('yhat') = yhat

• res('resid') = residuals

• res('vcovar') = estimated variance-covariance matrix of beta

• res('sige') = estimated variance of the residuals

• res('sige') = estimated variance of the residuals

• res('ser') = standard error of the regression

• res('tstat') = t-stats

• res('pvalue') = pvalue of the betas

• res('condindex') = multicolinearity cond index

• res('prescte') = boolean indicating the presence or absence of a constant in the regression

• res('rsqr') = rsquared

• res('rbar') = rbar-squared

• res('f') = F-stat for the nullity of coefficients other than the constant

• res('pvaluef') = its significance level

DESCRIPTION

Performs HAC variance correction for pooled effect panel model. Low level function that works only with matrices.

AUTHOR

Emmanuel Michaux 2012

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