Stockholm School of Economics

About: Stockholm School of Economics is a education organization based out in Stockholm, Sweden. It is known for research contribution in the topics: Population & Cost effectiveness. The organization has 1186 authors who have published 4891 publications receiving 285543 citations. The organization is also known as: Stockholm Business School & Handelshögskolan i Stockholm.

Modeling asymmetries and moving equilibria in unemployment rates

Abstract: Among the parametric nonlinear time series model families, the smooth transition regression (STR) model has recently received attention in the literature. The considerations in this dissertation focus on the univariate special case of this model, the smooth transition autoregression (STAR) model, although large parts of the discussion can be easily generalised to the more general STR case. Many nonlinear univariate time series models can be described as consisting of a number of regimes, each one corresponding to a linear autoregressive parametrisation, between which the process switches. In the STAR models, as opposed to certain other popular models involving multiple regimes, the transition between the extreme regimes is smooth and assumed to be characterised by a bounded continuous function of a transition variable. The transition variable, in turn, may be a lagged value of the variable in the model, or another stochastic or deterministic observable variable. A number of other commonly discussed nonlinear autoregressive models can be viewed as special or limiting cases of the STAR model.The applications presented in the first two chapters of this dissertation,Chapter I: Another look at Swedish Business Cycles, 1861-1988Chapter II: Modelling asymmetries and moving equilibria in unemployment rates, make use of STAR models.In these two studies, STAR models are used to provide insight into dynamic properties of the time series which cannot be be properly characterised by linear time series models, and which thereby may be obscured by estimating only a linear model in cases where linearity would be rejected if tested. The applications being of interest in their own right, an important common objective of these two chapters is also to develop, suggest, and give examples of various methods that may be of use in discussing the dynamic properties of estimated STAR models in general.Chapter III, Testing linearity against smooth transition autoregression using a parametric bootstrap, reports the result of a small simulation study considering a new test of linearity against STAR based on bootstrap methodology.

Corporate Scandals and Household Stock Market Participation

Abstract: We show that, after the revelation of corporate fraud in a state, household stock market participation in that state decreases. Households decrease holdings in fraudulent as well as nonfraudulent firms, even if they do not hold stocks in fraudulent firms. Within a state, households with more lifetime experience of corporate fraud hold less equity. Following the exogenous increase in fraud revelation due to Arthur Andersen's demise, states with more Arthur Andersen clients experience a larger decrease in stock market participation. We provide evidence that the documented effect is likely to reflect a loss of trust in the stock market. [ABSTRACT FROM AUTHOR]

Building neural network models for time series: a statistical approach

Abstract: This paper is concerned with modelling time series by single hidden layer feedforward neural network models. A coherent modelling strategy based on statistical inference is presented. Variable selection is carried out using simple existing techniques. The problem of selecting the number of hidden units is solved by sequentially applying Lagrange multiplier type tests, with the aim of avoiding the estimation of unidentified models. Misspecification tests are derived for evaluating an estimated neural network model. All the tests are entirely based on auxiliary regressions and are easily implemented. A small-sample simulation experiment is carried out to show how the proposed modelling strategy works and how the misspecification tests behave in small samples. Two applications to real time series, one univariate and the other multivariate, are considered as well. Sets of one-step-ahead forecasts are constructed and forecast accuracy is compared with that of other nonlinear models applied to the same series. Copyright © 2006 John Wiley & Sons, Ltd.