Estimation by generalized method of moments (GMM).

The system object must be specified with a list of instruments.

Syntax

system_name.gmm(options)

Options

m=integer | Maximum number of iterations. |

c=number | Set convergence criterion. The criterion is based upon the maximum of the percentage changes in the scaled coefficients. The criterion will be set to the nearest value between 1e-24 and 0.2. |

l=number | Set maximum number of iterations on the first-stage iteration to get the one-step weighting matrix. |

numericderiv / ‑numericderiv | [Do / do not] use numeric derivatives only. If omitted, EViews will follow the global default. |

fastderiv / ‑fastderiv | [Do / do not] use fast derivative computation. If omitted, EViews will follow the global default. |

showopts / ‑showopts | [Do / do not] display the starting coefficient values and estimation options in the estimation output. |

w | Use White’s diagonal weighting matrix (for cross section data). |

b=arg (default=“nw”) | Specify the bandwidth: “nw” (Newey-West fixed bandwidth based on the number of observations), number (user specified bandwidth), “v” (Newey-West automatic variable bandwidth selection), “a” (Andrews automatic selection). |

q | Use the quadratic kernel. Default is to use the Bartlett kernel. |

n | Prewhiten by a first order VAR before estimation. |

i | Iterate simultaneously over the weighting matrix and the coefficient vector. |

s | Iterate sequentially over the weighting matrix and coefficient vector. |

o (default) | Iterate only on the coefficient vector with one step of the weighting matrix. |

c | One step (iteration) of the coefficient vector following one step of the weighting matrix. |

e | TSLS estimates with GMM standard errors. |

prompt | Force the dialog to appear from within a program. |

p | Print results. |

Note that some options are only available for a subset of specifications.

Examples

For system estimation, the command:

sys1.gmm(b=a, q, i)

estimates the system SYS1 by GMM with a quadratic kernel, Andrews automatic bandwidth selection, and iterates simultaneously over the weight and coefficient vectors until convergence.

Cross-references

See “Additional Regression Tools” and “System Estimation” for discussion of the various GMM estimation techniques.