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

An exact prediction of [script N] = 4 supersymmetric Yang–Mills theory for string theory

We propose that the expectation value of a circular BPS-Wilson loop in [script N] = 4 supersymmetric Yang–Mills can be calculated exactly, to all orders in a 1/N expansion and to all orders in g2N. 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[prime] and to all orders in gs. We then compare this result with string theory. We find that the gauge theory calculation, for large g2N and to all orders in the 1/N2 expansion, does agree with the leading string theory calculation, to all orders in gs and to lowest order in alpha[prime]. 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

Green-Schwarz String in AdS_5 x S^5: Semiclassical Partition Function

A systematic approach to the study of semiclassical fluctuations of strings in AdS_5 x S^5 based on the Green-Schwarz formalism is developed. We show that the string partition function is well defined and finite. Issues related to different gauge choices are clarified. We consider explicitly several cases of classical string solutions with the world surface ending on a line, on a circle or on two lines on the boundary of AdS. The first example is a BPS object and the partition function is one. In the third example the determinants we derive should give the first corrections to the Wilson loop expectation value in the strong coupling expansion of the n=4 SYM theory at large N.Comment: 61 pages, harvmac, minor change

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.

Econometric analysis of panel data in Stata

Many researchers need to estimate panel data models in which either the idiosyncratic term is autocorrelated or the model includes a lagged dependent variable. This talk will review some of the estimation and inference methods that have appeared in the econometric literature to deal with these problems.These issues will discussed in the context of an extended example based on the same data used by Arellano and Bond in their 1991 Review of Economic Studies paper. In the course of the example, some two-stage least squares estimators for simultaneous equations with panel data will also be discussed.

Estimating the parameters of dynamic panel-data models using Stata

In this talk, I will review dynamic panel-data analysis and how to perform it using Stata. I also cover static models with predetermined variables. For each model discussed, I review the econometrics and then show how to perform the estimation using Stata.

Analyzing spatial autoregressive models in Stata

In this talk, I will provide a quick introduction to estimators for the parameters of spatial-autoregressive models and a quick introduction to a suite of user-written Stata commands for managing spatial data and parameter estimation.

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.