This paper fixes size distortions of tests for structural parameters in the simultaneous equations model by computing critical value functions based on the conditional distribution of test statistics. The conditional tests can then be used to construct informative confidence regions for the...

The nlsur command is better suited to demand-system estimation than the suite of ado-fifies provided in Poi (2002, Stata Journal 2: 403–410) because it is faster and requires only one ancillary ado-file. This article replicates the results presented in Poi (2002) by using nlsur instead of ml.

The two-stage least-squares (2SLS) instrumental variables estimator is commonly used to address endogeneity. However, the estimator suffers from bias that is exacerbated when the instruments are only weakly correlated with the en- dogenous variables and when many instruments are used. In this...

Previously, to fit an almost-ideal demand system in Stata, one would have to use the nlsur command and write a function evaluator program as described in [R] nlsur and Poi (2008, Stata Journal 8: 554–556). In this article, I introduce the command quaids, which obviates the need for any...

The purpose of this paper is to provide theoretical justification for some existing methods for constructing confidence intervals for the sum of coefficients in autoregressive models. We show that the methods of Stock (1991), Andrews (1993), and Hansen (1999) provide asymptotically valid...

This paper considers a moments based non-linear estimator that is root-T consistent and uniformly asymptotically normal irrespective of the degree of persistence of the forcing process. These properties hold for linear autoregressive models, linear predictive regressions, as well as certain...

This paper provides a brief review of the current state of knowledge on the topic of weakly-identified instrumental variable regression. We describe the essence of the problem of weak identification, possible methods for detecting it in applied work as well as methods robust to weak...