## Abstract

This paper puts the case for the inclusion of point optimal tests in the econometrician's repertoire. They do not suit every testing situation but the current evidence, which is reviewed here, indicates that they can have extremely useful small-sample power properties. As well as being most powerful at a nominated point in the alternative hypothesis parameter space, they may also have optimum power at a number of other points and indeed be uniformly most powerful when such a test exists. Point optimal tests can also be used to trace out the maximum attainable power envelope for a given testing problem, thus providing a benchmark against which test procedures can be evaluated. In some cases, point optimal tests can be constructed from tests of a simple-null hypothesis against a simple alternative. For a wide range of models of interest to econometricians, this paper shows how one can check whether a point optimal test can be constructed in this way. When it cannot, one may wish to consider approximately point optimal tests. As an illustration, the approach is applied to the non-nested problem of testing for AR(1) disturbances against MA(1) disturbances in the linear regression model.

Original language | English |
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Pages (from-to) | 169-218 |

Number of pages | 50 |

Journal | Econometric Reviews |

Volume | 6 |

Issue number | 2 |

DOIs | |

Publication status | Published - 1987 |

## Keywords

- autocorrelation
- hypothesis testing
- linear regression model
- most powerful test
- Neyman-Pearson theory
- normality
- power envelope