Exports and Capacity Constraints: A smooth transition regression model for six euro-area countries. CEPS Working Document No. 395/May 2014

The significant gains in export market shares made in a number of vulnerable euro-area crisis countries have not been accompanied by an appropriate improvement in price competitiveness. This paper argues that, under certain conditions, firms consider export activity as a substitute for serving domestic demand. The strength of the link between domestic demand and exports is dependent on capacity constraints. Our econometric model for six euro-area countries suggests domestic demand pressure and capacity-constraint restrictions as additional variables of a properly specified export equation. As an innovation to the literature, we assess the empirical significance through the logistic and the exponential variant of the non-linear smooth transition regression model. We find that domestic demand developments are relevant for the short-run dynamics of exports in particular during more extreme stages of the business cycle. A strong substitutive relationship between domestic and foreign sales can most clearly be found for Spain, Portugal and Italy, providing evidence of the importance of sunk costs and hysteresis in international trade.

LOGITCPRPLOT: Stata module to graph component-plus-residual plot for logistic regression

logitcprplot can be used after logistic regression for graphing a component-plus-residual plot (a.k.a. partial residual plot) for a given predictor, including a lowess, local polynomial, restricted cubic spline, fractional polynomial, penalized spline, regression spline, running line, or adaptive variable span running line smooth

RRLOGIT: Stata module to estimate logistic regression for randomized response data

rrlogit fits a maximum-likelihood logistic regression for randomized response data.

WGTTEST: Stata module to test the impact of sampling weights in regression analysis

wgttest performs a test proposed by DuMouchel and Duncan (1983) to evaluate whether the weighted and unweighted estimates of a regression model are significantly different.

Statistical analysis of the goodness of classical factor analysis regression (CFAR) /

Cover title.

OPTREG - an interactive computer program for optimization and regression. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

An analysis of the regression model for nonsaturating logic circuit analysis.

Also issued as thesis (M.S.) University of Illinois.

Forcing regression through a given point using any familiar computational routine /

"March 1983."

Modeling and interpreting interactive hypotheses in regression analysis /

Includes bibliographical references (p. 147-150) and index.

Maps of residuals from regression: their characteristics and uses in geographic research.

Bibliography: p. 59-60.

Use of multiple regression analysis to summarize and interpret linear programming shadow prices in an economic planning model /

Issued May 1980.

Simple correlation and multiple regression among leaf characteristics, smoke components, and biological responses of bright tobaccos /

Chiefly tables.

Multiple classification analysis ; a report on a computer program for multiple regression using categorical predictors /

Bibliographical footnotes.

Finite mixture regression model with random effects: application to neonatal hospital length of stay

A two-component mixture regression model that allows simultaneously for heterogeneity and dependency among observations is proposed. By specifying random effects explicitly in the linear predictor of the mixture probability and the mixture components, parameter estimation is achieved by maximising the corresponding best linear unbiased prediction type log-likelihood. Approximate residual maximum likelihood estimates are obtained via an EM algorithm in the manner of generalised linear mixed model (GLMM). The method can be extended to a g-component mixture regression model with the component density from the exponential family, leading to the development of the class of finite mixture GLMM. For illustration, the method is applied to analyse neonatal length of stay (LOS). It is shown that identification of pertinent factors that influence hospital LOS can provide important information for health care planning and resource allocation. (C) 2002 Elsevier Science B.V. All rights reserved.

Modelling inpatient length of stay by a hierarchical mixture regression via the EM algorithm

The modelling of inpatient length of stay (LOS) has important implications in health care studies. Finite mixture distributions are usually used to model the heterogeneous LOS distribution, due to a certain proportion of patients sustaining-a longer stay. However, the morbidity data are collected from hospitals, observations clustered within the same hospital are often correlated. The generalized linear mixed model approach is adopted to accommodate the inherent correlation via unobservable random effects. An EM algorithm is developed to obtain residual maximum quasi-likelihood estimation. The proposed hierarchical mixture regression approach enables the identification and assessment of factors influencing the long-stay proportion and the LOS for the long-stay patient subgroup. A neonatal LOS data set is used for illustration, (C) 2003 Elsevier Science Ltd. All rights reserved.