Bootstrapping a conditional moments test for normality after tobit estimation

Categorical and limited dependent variable models are routinely estimated via maximum likelihood. It is well-known that the ML estimates of the parameters are inconsistent if the distribution or the skedastic component is misspecified. When conditional moment tests were first developed by Newey (1985) and Tauchen (1985),they appeared to offer a wide range of easy-to-compute specification tests for categorical and limited dependent variable models estimated by maximum likelihood. However, subsequent studies found that using the asymptotic critical values produced severe size distortions. This paper presents simulation evidence that the standard conditional moment test for normality after tobit estimation has essentially no size distortion and reasonable power when the critical values are obtained via a parametric bootstrap. Copyright 2002 by Stata Corporation.conditional moment tests,bootstrap,tobit,normality

State Space Methods in Stata

We illustrate how to estimate parameters of linear state-space models using the Stata program sspace. We provide examples of how to use sspace to estimate the parameters of unobserved-component models, vector autoregressive moving-average models, and dynamic-factor models. We also show how to compute one-step, filtered, and smoothed estimates of the series and the states; dynamic forecasts and their confidence intervals; and residuals.

New multivariate time-series estimators in Stata

Stata 11 has new commands sspace and dvech for estimating the parameters of space-space models and diagonal-vech multivariate GARCH models, respectively. In this presentation, I provide an introduction to space-space models, diagonal-vech multivariate GARCH models, the implemented estimators, and the new Stata commands.

Generalized method of moments estimators in Stata

Stata 11 has new command gmm for estimating parameters by the generalized method of moments (GMM). gmm can estimate the parameters of linear and nonlinear models for cross-sectional, panel, and time-series data. In this presentation, I provide an introduction to GMM and to the gmm command.

Filtering and decomposing time series in Stata 12

In this talk, I introduce new methods in Stata 12 for filtering and decomposing time series and I show how to implement them. I provide an underlying framework for understanding and comparing the different methods. I also present a framework for interpreting the parameters.

Estimating the parameters of simultaneous-equations models with the sem command in Stata 12

In this talk, I introduce Stata 12's new sem command for estimating the parameters of simultaneous-equations models. Some of the considered models include unobserved factors. Estimation methods include maximum likelihood and the generalized method of moments.

State Space Methods in Stata

We illustrate how to estimate parameters of linear state-space models using the Stata program sspace. We provide examples of how to use sspace to estimate the parameters of unobserved-component models, vector autoregressive moving-average models, and dynamic-factor models. We also show how to compute one-step, filtered, and smoothed estimates of the series and the states; dynamic forecasts and their confidence intervals; and residuals

A Spatial Cliff-Ord-type Model with Heteroskedastic Innovations: Small and Large Sample Results

In this paper we specify a linear Cliff and Ord-type spatial model. The model allows for spatial lags in the dependent variable, the exogenous variables, and disturbances. The innovations in the disturbance process are assumed to be heteroskedastic with an unknown form. We formulate a multi-step GMM/IV type estimation procedure for the parameters of the model. We then establish the limiting distribution of our suggested estimators, and give consistent estimators for their asymptotic variance covariance matrices, utilizing results given in Kelejian and Prucha (2007b). Monte Carlo results are given which suggest that the derived large sample distribution provides a good approximation to the actual small sample distribution of our estimators.

An Exact Prediction of N=4 SUSYM Theory for String Theory

We propose that the expectation value of a circular BPS-Wilson loop in N=4 SUSYM can be calculated exactly, to all orders in a 1/N expansion and to all orders in g^2 N. Using the AdS/CFT duality, this result yields a prediction of the value of the string amplitude with a circular boundary to all orders in alpha' and to all orders in g_s. We then compare this result with string theory. We find that the gauge theory calculation, for large g^2 N and to all orders in the 1/N^2 expansion does agree with the leading string theory calculation, to all orders in g_s and to lowest order in alpha'. We also find a relation between the expectation value of any closed smooth Wilson loop and the loop related to it by an inversion that takes a point along the loop to infinity, and compare this result, again successfully, with string theory.Comment: LaTeX, 22 pages, 3 figures. Argument corrected and two new sections adde

Nonperturbative aspects of ABJM theory

Using the matrix model which calculates the exact free energy of ABJM theory on S^3 we study non-perturbative effects in the large N expansion of this model, i.e., in the genus expansion of type IIA string theory on AdS4xCP^3. We propose a general prescription to extract spacetime instanton actions from general matrix models, in terms of period integrals of the spectral curve, and we use it to determine them explicitly in the ABJM matrix model, as exact functions of the 't Hooft coupling. We confirm numerically that these instantons control the asymptotic growth of the genus expansion. Furthermore, we find that the dominant instanton action at strong coupling determined in this way exactly matches the action of an Euclidean D2-brane instanton wrapping RP^3.Comment: 26 pages, 14 figures. v2: small corrections, final version published in JHE