Time series count data regression

The count data model studied in the paper extends the Poisson model by al-lowing for overdispersion and serial correlation. Alternative approaches to esti-mate nuisance parameters, required for the correction of the Poisson maximum likelihood covariance matrix estimator and for a quasi-likelihood estimator, are studied. The estimators are evaluated by finite sample Monte Carlo experi-mentation. It is found that the Poisson maximum likelihood estimator with corrected covariance matrix estimators provide reliable inferences for longer time series. Overdispersion test statistics are wellbehaved, while conventional portmanteau statistics for white noise have too large sizes. Two empirical illustrations are included.     

Panel data regression for counts

A new panel data model for count data is introduced. We suggest alternative estimators, such as pseudo maximum likelihood and generalized method of moments, of structural and nuisance parameters. In addition, different test statistics of independence and overdispersion are obtained. The small sample performance of the estimators and tests are evaluated in Monte Carlo experiments. The model is applied to the number of days absent in Sweden 1981–1991 for a panel of Swedish male workers.     

Building successful strategic alliances

In this paper we will discuss (1) why a firm would want to go into a strategic alliance, (2) the different types of alliance, and (3) guidelines to follow when forming an alliance.     

Heteroskedasticity in the Tobit model

The paper deals with parameter estimation and the testing of individual parameters in heteroskedastic Tobit models. The statistical properties of semiparametric and maximum likelihood estimators are evaluated. Correspondingt-test statistics are compared. Results from a Monte Carlo experiment indicate that the semiparametric estimator performs relatively better than the maximum likelihood estimator. The associatedt-test statistics appear to perform better than the corresponding maximum likelihood test statistics. *** DIRECT SUPPORT *** A06GP002 00008     

Searching for Innovative Design: Architectural Competitions in the Silvering Swedish Welfare State

The new millennium has seen an increased interest in appropriate housing for the senior part of the Swedish population, defined as those aged 65 years and older. In 2010, the Swedish government launched a 2-year program called “Growing Old, Living Well” that targeted the living conditions of not only older people with few or some needs for homecare services, but also the smaller group of dependent and frail older persons whose everyday living depends upon regular caregiving. The program promoted architectural competitions as a tool for innovation. This article assesses the outcome of 3 architectural competitions that were carried out by 3 municipalities in accordance with the program. It concludes that existing notions about appropriate space for aging populations prevailed, since the competition briefs evolved from existing data and consequently allowed for only a low degree of innovative thinking. Hence, the relationship between architectural design and older people's age-related needs was only addressed indirectly. Further work needs to be done in the area of housing for the Swedish silvering welfare state in order for this factor to become an essential criterion for creating innovative architecture and urban design.     

Nonparametric maximum likelihood estimation for shifted curves

The analysis of a sample of curves can be done by self-modelling regression methods. Within this framework we follow the ideas of nonparametric maximum likelihood estimation known from event history analysis and the counting process set-up. We derive an infinite dimensional score equation and from there we suggest an algorithm to estimate the shape function for a simple shape invariant model. The nonparametric maximum likelihood estimator that we find turns out to be a Nadaraya–Watson-like estimator, but unlike in the usual kernel smoothing situation we do not need to select a bandwidth or even a kernel function, since the score equation automatically selects the shape and the smoothing parameter for the estimation. We apply the method to a sample of electrophoretic spectra to illustrate how it works.     

Improper use of the ordinary least squares estimator in the switching regression model

The paper considers the consequences of incorrectly using the ordinary least squares estimator, when the true but unknown model is a switching regression. Bias and mean square error express ons are given for slope and residual variance estimators. Except for in very specialized cases the estimators are biased. A numerical exarnple illustrates some of the issues raised and provides a conpelison between the ordinary least squares and maximum likelihood estimators.     

Testing linearity against nonlinear moving average models

Lagrange multiplier (LM) test statistics are derived for testing a linear moving average model against an asymmetric moving average model and an LM type test against an additive smooth transition moving average model. The latter model is introduced in the paper. The small sample performance of the proposed tests are evaluated in a Monte Carlo study and compared to Wald and likelihood ratio statistics. The size properties of the Lagrange multiplier test are better than those of other tests.     

Invertibility of non-linear time series models

Sufficient conditions for invertibility of non-linear time series models are available in the literature only for a few special cases. In this paper a practical and general method for checking invertibility is presented. Briefly stated, it consists of feeding independent and identically distributed innovations into the non-linear model and then observing whether the model blows up or not. Using this idea invertibility conditions are derived for several recently proposed non-linear moving average models. Finally, the method is applied to a number of bilinear models fitted to economic time series.