Prais–Winsten estimation

In econometrics, Prais–Winsten estimation is a procedure meant to take care of the serial correlation of type AR(1) in a linear model. Conceived by Sigbert Prais and Christopher Winsten in 1954,[1] it is a modification of Cochrane–Orcutt estimation in the sense that it does not lose the first observation, which leads to more efficiency as a result and makes it a special case of feasible generalized least squares.[2]

Theory

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Consider the model

 

where   is the time series of interest at time t,   is a vector of coefficients,   is a matrix of explanatory variables, and   is the error term. The error term can be serially correlated over time:   and   is white noise. In addition to the Cochrane–Orcutt transformation, which is

 

for t = 2,3,...,T, the Prais-Winsten procedure makes a reasonable transformation for t = 1 in the following form:

 

Then the usual least squares estimation is done.

Estimation procedure

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First notice that

 

Noting that for a stationary process, variance is constant over time,

 

and thus,

 

Without loss of generality suppose the variance of the white noise is 1. To do the estimation in a compact way one must look at the autocovariance function of the error term considered in the model blow:

 

It is easy to see that the variance–covariance matrix,  , of the model is

 

Having   (or an estimate of it), we see that,

 

where   is a matrix of observations on the independent variable (Xt, t = 1, 2, ..., T) including a vector of ones,   is a vector stacking the observations on the dependent variable (yt, t = 1, 2, ..., T) and   includes the model parameters.

Note

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To see why the initial observation assumption stated by Prais–Winsten (1954) is reasonable, considering the mechanics of generalized least square estimation procedure sketched above is helpful. The inverse of   can be decomposed as   with[3]

 

A pre-multiplication of model in a matrix notation with this matrix gives the transformed model of Prais–Winsten.

Restrictions

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The error term is still restricted to be of an AR(1) type. If   is not known, a recursive procedure (Cochrane–Orcutt estimation) or grid-search (Hildreth–Lu estimation) may be used to make the estimation feasible. Alternatively, a full information maximum likelihood procedure that estimates all parameters simultaneously has been suggested by Beach and MacKinnon.[4][5]

References

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  1. ^ Prais, S. J.; Winsten, C. B. (1954). "Trend Estimators and Serial Correlation" (PDF). Cowles Commission Discussion Paper No. 383. Chicago.
  2. ^ Johnston, John (1972). Econometric Methods (2nd ed.). New York: McGraw-Hill. pp. 259–265. ISBN 9780070326798.
  3. ^ Kadiyala, Koteswara Rao (1968). "A Transformation Used to Circumvent the Problem of Autocorrelation". Econometrica. 36 (1): 93–96. doi:10.2307/1909605. JSTOR 1909605.
  4. ^ Beach, Charles M.; MacKinnon, James G. (1978). "A Maximum Likelihood Procedure for Regression with Autocorrelated Errors". Econometrica. 46 (1): 51–58. doi:10.2307/1913644. JSTOR 1913644.
  5. ^ Amemiya, Takeshi (1985). Advanced Econometrics. Cambridge: Harvard University Press. pp. 190–191. ISBN 0-674-00560-0.

Further reading

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