Multiple minima and the Cochrane-Orcutt technique

Oxley, Les; Roberts, Colin J.

doi:10.1016/0165-1765(86)90031-5

Cited by 4 publications

(1 citation statement)

References 5 publications

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“…Another example, involving a more realistic model and data, was provided by Dufour et al (1980). The issue was also more extensively treated by Oxley and Roberts (1986) who used a lagged dependent variable model. (It should be pointed out, though, that in this case the iterative C-O estimator is inconsistent since the starting least squares estimator is inconsistent unless p = 0.)…”

Section: Multiple Minimamentioning

confidence: 99%

Review of Economics and Statistics

Mansfield¹,

Romeo²,

Wagner³

2005

Encyclopedia of Statistical Sciences

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The results show that demand decreases with prices. They indicate that if we take a household with particular characteristics and vary only the marginal price a negative relationship holds between quantity and price. While this approach is less informative than that of section 1I, table 4 supports the downward sloping demand curve results rather than those produced in the Rosen framework and does not make any assumptions regarding the household's utility function. IV. Conclusion This paper develops estimates of the demand for electricity in Medellin, Colombia using a method which exploits the information implicit in constrained maximization subject to a convex, but segmented linear, budget set. The estimates seem adequate statistically, fall within the accepted range of parameter estimates, and show a consistent pattern whereby richer consumers have absolutely larger price and income elasticities than do the poor. Their veracity is enhanced by similar results developed using a generalised Heckman method due to Vella (1990). This contrasts sharply with the results obtained when the standard Rosen method is applied to the same data. The formation of instruments which linearise the budget constraint was incapable with these data of identifying the downward sloping demand curve from the upward sloping supply curve.

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Section: Multiple Minimamentioning

confidence: 99%

Review of Economics and Statistics

Mansfield¹,

Romeo²,

Wagner³

2005

Encyclopedia of Statistical Sciences

View full text Add to dashboard Cite

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The Size and Power of the Bias-Corrected Bootstrap Test for Regression Models with Autocorrelated Errors

Kim

Yeasmin

2005

Comput Econ

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This paper is concerned with statistical inference for the coefficient of the linear regression model when the error term follows an autoregressive (AR) model. Past studies have reported severe size distortions, when the data are trending and autocorrelation of the error term is high. In this paper, we consider a test based on the bias-corrected bootstrap, where bias-corrected parameter estimators for the AR and regression coefficients are used. For bias-correction, the jackknife and bootstrap methods are employed. Monte Carlo simulations are conducted to compare size and power properties of the bias-corrected bootstrap test. It is found that the bias-corrected bootstrap test shows substantially improved size properties and exhibits excellent power for most of cases considered. It also appears that bootstrap bias-correction leads to better size and higher power values than jackknife bias-correction. These results are found to be robust to the choice of parameter estimation methods. Copyright Springer Science + Business Media, Inc. 2005bias-correction, bootstrap, jackknife, statistical inference,

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