midas |

Estimates an equation using Mixed Data Sampling (MIDAS) regression.

MIDAS regression is an estimation technique which allows for data sampled at different frequencies to be used in the same regression.

Syntax

midas(options) y x1 [x2 x3 ...] @ z1page\z1 [z2page\z2 ...]

where y, x1, etc., are the dependent and explanatory variables in the current page frequency, and z1page\z1 and z2page\z2 are the high frequency variable page\series specification.

You may not include ARMA terms in a MIDAS regression.

Options

midwgt=arg | MIDAS weight method: step function(“step”), normalized exponential Almon (“expalmon”), normalized beta function (“beta”), or the default Almon/PDL weighting (“almon”). |

lag=arg | Method for specifying the number of lags of the high frequency regressor to use: lag selection (“auto”), fixed (“fixed”). The default is “lag=fixed”. |

maxlag=arg | Maximum number of lags of the high frequency regressor to use when using lag selection. For use when “lag=auto”. The default value is 4. |

fixedlag=arg | Fixed number of lags of the high frequency regressor to use. For use when “lags=fixed”. The default value is 4. |

steps=integer | Stepsize (number of high frequency periods to group). For use when “midwgt=step”. |

polynomial=integer | Polynomial degree. For use when Almon/PDL weighting is employed. |

beta=arg | Beta function restriction: none (“none”), trend coefficient equals 1 (“trend”), endpoints coefficient equals 0 (“endpoint”), both trend and endpoints restriction (“both”). For use when “midwgt=beta”. The default is “beta=none”. |

optmethod = arg | Optimization method for nonlinear estimation: “bfgs” (BFGS); “newton” (Newton-Raphson), “opg” or “bhhh” (OPG or BHHH), “hybrid” (initial BHHH followed by BFGS). Hybrid is the default method. |

optstep = arg | Step method for nonlinear estimation: “marquardt” (Marquardt); “dogleg” (Dogleg); “linesearch” (Line search). Marquardt is the default method. |

cov=arg | Covariance method for nonlinear models: “ordinary” (default method based on inverse of the estimated information matrix), “huber” or “white” (Huber-White sandwich). |

covinfo = arg | Information matrix method for nonlinear models: “opg” (OPG); “hessian” (observed Hessian). |

nodf | Do not perform degree of freedom corrections in computing coefficient covariance matrix. The default is to use degree of freedom corrections. |

m=integer | Set maximum number of iterations. |

c=scalar | 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. |

s | Use the current coefficient values in estimator coefficient vector as starting values in nonlinear estimation (see also param ). |

s=number | Determine starting values for nonlinear estimation.. Specify a number between zero and one representing the fraction of preliminary EViews chosen values. Note that out of range values are set to “s=1”. Specifying “s=0” initializes coefficients to zero. By default EViews uses “s=1”. |

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

coef=arg | Specify the name of the coefficient vector (if specified by list); the default behavior is to use the “C” coefficient vector. |

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

p | Print estimation results. |

Examples

midas(fixedlag=9, midwgt=beta, beta=endpoint) realgdp c realgdp(-1) @ monthlypage\emp(-5)

estimates a MIDAS beta weight specification using the low frequency dependent variable REALGDP and regressors C and REALGDP(-1), and 9 beta weighted lags of EMP(-5) from the “monthlypage” workfile page. The beta weight function places zero restrictions on the endpoint coefficient.

midas(maxlag=12, lag=auto) realgdp c realgdp(-1) @ monthlypage\emp(-5)

estimates the same equation using PDL/Almon weights. The number of lags is chosen automatically with a maximum of 12 lags.

Cross-references

“Midas Regression” discusses the specification and estimation of MIDAS regression models in EViews.