A MATHEMATICAL MODEL OF THE ADAPTIVE BEHAVIOUR OF ORGANIZATIONS

The recent work on organizational configuration and quantum structural change by Miller and Friesen (1984), and on the population ecology of organizations by Hannan and Freeman (1978, 1983) suggests some integral relationships between eiivironments, structure and optimal adaptive strategies. These appear to warrant a more systematic and formal analysis. Towards that end a mathematical model is developed to show the way organizations adapt t o changing environments. Using a Markovian representation, three types of environments are examined t o test the implications that the model has for adopting strategies of generalism vs. specialism, concerted ‘quantum’ 71,s. piecemeal change, and batching changes together vs. changing gradually and promptly.     

Outlier Detection in the Lognormal Logarithmic Conditional Autoregressive Range Model

An outlier detection procedure in the lognormal logarithmic conditional autoregressive range (lognormal Log‐CARR) model is proposed. The proposed test statistic is demonstrated to be well‐sized and to have good power using Monte Carlo simulations. Furthermore, the outlier detection procedure suffers less from the masking effect caused by multiple outliers. The results of an empirical investigation show that the proposed method can effectively detect volatility outliers and improve forecasting accuracy.     

Further experience in Bayesian analysis using Monte Carlo integration

An earlier paper [Kloek and Van Dijk (1978)] is extended in three ways. First, Monte Carlo integration is performed in a nine-dimensional parameter space of Klein's model I [Klein (1950)]. Second, Monte Carlo is used as a tool for the elicitation of a uniform prior on a finite region by making use of several types of prior information. Third, special attention is given to procedures for the construction of importance functions which make use of nonlinear optimization methods.     

Diagnostics analysis for log‐Birnbaum–Saunders regression models with censored data

This article develops influence diagnostics for log‐Birnbaum–Saunders (LBS) regression models with censored data based on case‐deletion model (CDM). The one‐step approximations of the estimates in CDM are given and case‐deletion measures are obtained. Meanwhile, it is shown that CDM is equivalent to mean shift outlier model (MSOM) in LBS regression models and an outlier test is presented based on MSOM. Furthermore, we discuss a score test for homogeneity of shape parameter in LBS regression models. Two numerical examples are given to illustrate our methodology and the properties of score test statistic are investigated through Monte Carlo simulations under different censoring percentages.     

The identification of outliers in exponential samples

In this paper, the task of identifying outliers in exponential samples is treated conceptionally in the sense of DAVIES and GATHER (1989, 1993) by means of a so-called outlier region. In case of an exponential distribution, an empirical version of such a region – also called an outlier identifier – is mainly dependent on some estimator of the unknown scale parameter. The worst-case behaviour of several reasonable outlier identifiers is investigated thoroughly and it is shown that only robust estimators of scale should be used to construct reliable identifiers. These findings lead to the recommendation of an outlier identifier that is based on a standardized version of the sample median.     

Large shocks,small shocks,and economic fluctuations: Outliers in macroeconomic time series

We analyse fifteen post-World War II US macroeconomic time series using a modified outlier identification procedure based on Tsay (1988a). ‘Large shocks’ appear to be present in all the series we examined. Furthermore, there are three basic outlier patterns: (1) outliers seem to be associated with business cycles, (2) outliers are clustered together—both over time and across series, (3) there appears to be a dichotomy between outlier behaviour of real versus nominal series. Also, after controlling for outliers, much of the evidence of non-linearity in many of the time series is eliminated.     

Estimation of a simultaneous equations model with an ordinal endogenous variable: The extent of teacher bargaining and the state legal environment

This paper presents a simple two-step estimator for a simultaneous equations model that contains an ordinal endogenous variable. The estimation rules are extensions of the Heckman (1978) estimators, also considered by Amemiya (1978). Asymptotic covariance matrices of the estimators also are derived. The estimator is applied to an economic model in which the statewide extent of teacher bargaining and teacherbargaining legislation are determined jointly.     

Modelling the process of job search

This paper is concerned with the specification and estimation of a genuinely structural but static model of individual search behaviour in which the estimated reservation wage actually satisfies the optimality condition. The identification of the parameters of interest is achieved by the a priori specification of the vacancy-wage distribution. The model is estimated using the data from the 1978–1979 D.H.S.S. Cohort Study of the Unemployed. The estimated parameters reflect, in the main, our prior views and generate a number of interesting results. In particular the elasticity of unemployment spell duration with respect to unemployment benefits is estimated to be around 0.18–0.26.     

Racial gaps in the transition to first-time homeownership: The role of residential location

Evidence suggests that African Americans wait longer to transition into first-time homeownership than white households with similar endowments. This paper relies on data from the Panel Study of Income Dynamics (PSID) to examine the contribution of residential location toward the black–white gap in first-time homeownership transitions. For a sample of young renters who first left their parents' home during the period 1978 through 1987, I estimate continuous time duration models that explain racial gaps in rental tenure durations prior to first-time homeownership as a function of individual, household, and location-specific covariates. I find that while several residential location characteristics, particularly those associated with the supply of affordable owner-occupied housing, impinge upon racial gaps in first-time homeownership transitions, most of the racial gap in homeownership transitions would be eliminated if blacks and whites had similar individual and household characteristics.     

Testing Integration of Macroeconomic Time Series in Transitional Socialist Economies. A Modification of Perron Test

The presence of structural breaks reduces the power of integration tests. A number of methods were suggested to improve the statistical properties of integration tests in the presence of structural breaks. The most known are Perron tests, which allow to test for the level of integration of time series with one structural break. Perron tests allow for two types of structural breaks: additive outlier an innovative outlier. These tests are, however, not very useful in testing the level of integration of macroeconomic time series in countries in transition from centrally-planned to market economy. In such case one should expect two structural breaks to affect the time series: one at the beginning and one at the end of the transformation process. Test that allows for two additive outlier type structural breaks in time series is developed in this paper. This test has superior power as compared to standard Dickey-Fuller and Perron tests. This paper provides asymptotic distribution as well as finite sample properties of proposed test. Therefore practitioners receive a reliable tool for analyzing macroeconomic processes in transitional economies. This revised version was published online in July 2006 with corrections to the Cover Date.     

Robust Hierarchical Bayes Small Area Estimation for the Nested Error Linear Regression Model

Standard model‐based small area estimates perform poorly in presence of outliers. Sinha & Rao ( 2009 ) developed robust frequentist predictors of small area means. In this article, we present a robust Bayesian method to handle outliers in unit‐level data by extending the nested error regression model. We consider a finite mixture of normal distributions for the unit‐level error to model outliers and produce noninformative Bayes predictors of small area means. Our modelling approach generalises that of Datta & Ghosh ( 1991 ) under the normality assumption. Application of our method to a data set which is suspected to contain an outlier confirms this suspicion, correctly identifies the suspected outlier and produces robust predictors and posterior standard deviations of the small area means. Evaluation of several procedures including the M‐quantile method of Chambers & Tzavidis ( 2006 ) via simulations shows that our proposed method is as good as other procedures in terms of bias, variability and coverage probability of confidence and credible intervals when there are no outliers. In the presence of outliers, while our method and Sinha–Rao method perform similarly, they improve over the other methods. This superior performance of our procedure shows its dual (Bayes and frequentist) dominance, which should make it attractive to all practitioners, Bayesians and frequentists, of small area estimation.     

Technology and the exploratory success rate in the U.S. onshore

According to the Energy Information Administration, the exploratory success rate in the U.S. onshore increased to 34.2% in 1998 from 27.5% in 1978. It is tempting to conclude that this increase can be attributed to the many advances in seismic and drilling technology that have occurred over the same period. However, such a conclusion may be premature. While the average success rate for the industry is substantially higher, the success rate for a group of large technologically progressive producers including Exxon Mobil, Shell, Chevron, and Texaco was only marginally higher in 1998 than in 1978. Building on the work of Forbes and Zampelli (2000) that examined the impact of technology on the offshore success rate, this paper develops an econometric model that attempts to disentangle and quantify the effects of the major factors hypothesized to affect the onshore exploratory success rate. The analysis relies on a panel of company level data from the Energy Information Administration’s Financial Reporting System (FRS).     

Identification with latent variables

This is an essay on a unified approach to the identifiability problem in static models with and without hidden endogenous variables. As is well known, when some of these variables are unobserved, the prior information requirements for models when all endogenous variables are observed, are still there. In addition, extra prior information that takes the place of the means and covariances of the missing variables will have to be supplied directly or indirectly by the statistical researcher. In the paper we characterize the quality and quantity of the required information for the general linear static model and apply it when the model is i) an econometric demand and supply model with missing observations on the quantity transacted, ii) a factor analysis model with observed characteristics of the test takers and iii) a LISREL Model without fixed exogenous variables. With unknown true parameters, the exact rank conditions are seldom verifiable but we do recommend an implementable check-list that is adequate for almost all parameters.     

The Sociopolitical Analysis of Global Inflation: A Theoretical and Empirical Examination of an Influential Basic Explanation

A bstract . One variant of the influential sociopolitical model of inflation is examined, a version distinguished by its econometric testable form and its use of relative deprivation as a principal efficient cause of global inflation. Hence its relevance to the contemporary debate on the nature and origins of the inflationary process. Specifically, the model postulates that the rate of inflation in a country is inversely related to its level of per capita income (and social security expenditure ) and directly related to its degree of income inequality. It emerges from our analysis, however, that the association between the level of economic development and the comparative propensity to inflate is of theoretically ambiguous sign. Also, the empirical robustness of the postulated relation is in doubt and the multivariate analysis is itself sensitive to the inclusion/ exclusion from the sample of outlier countries. More positively, the general focus of the model on fundamental as opposed to proximate determinants of inflation is to be welcomed. The message is that sociopolitical analysts must sharpen their analytical tools and, in the spirit of the model, formulate testable propositions in an area where economists have tended to overemphasize 'mechanical' relationships/solutions.     

On the non-existence of equilibria in differentiated product models

It is sometimes suggested that the non-existence of an equilibrium in the Hotelling spatial model pointed out by d'Aspremont, Gabszewicz and Thisse (1979) is due to demand discontinuity. In this paper we first show that demand continuity is a natural property in general spatial or differential product markets. However, we also claim that the non-existence of an equilibrium is also a general property of these models. Further the assumption of no mill-price undercutting suggested by Eaton and Lipsey (1978) and Novshek (1980) is shown to be not sufficient to restore existence.     

The London Business School with Gower Publishing

A reasoned assessment of the impact of the Budget leads to the following implications:
Interest rates over the course of the year will be higher than they are today.
The possibility of an upturn in the inflation rate before the end of 1978 is more likely than before the Budget.
The combination of this higher inflation rate and the weaker currency is likely to keep the growth of consumer spending significantly below the official (and most other) forecasts, although the 1978 growth will still be substantial.
In February we argued that on the assumption of £l½b net tax reduction in the Budget 'a responsible financial policy would require that part at least should be reversed in 1979'. The scale of the Budget tax reductions and suggestions of further net reliefs in July increase the likelihood that we will be seeing forces at work to slacken demand later this year, or in the first part of 1979.     

Robust Dickey–Fuller tests based on ranks for time series with additive outliers

In this paper the unit root tests proposed by Dickey and Fuller (DF) and their rank counterpart suggested by Breitung and Gouriéroux (J Econom 81(1): 7–27, 1997) (BG) are analytically investigated under the presence of additive outlier (AO) contaminations. The results show that the limiting distribution of the former test is outlier dependent, while the latter one is outlier free. The finite sample size properties of these tests are also investigated under different scenarios of testing contaminated unit root processes. In the empirical study, the alternative DF rank test suggested in Granger and Hallman (J Time Ser Anal 12(3): 207–224, 1991) (GH) is also considered. In Fotopoulos and Ahn (J Time Ser Anal 24(6): 647–662, 2003), these unit root rank tests were analytically and empirically investigated and compared to the DF test, but with outlier-free processes. Thus, the results provided in this paper complement the studies of the previous works, but in the context of time series with additive outliers. Equivalently to DF and Granger and Hallman (J Time Ser Anal 12(3): 207–224, 1991) unit root tests, the BG test shows to be sensitive to AO contaminations, but with less severity. In practical situations where there would be a suspicion of additive outlier, the general conclusion is that the DF and Granger and Hallman (J Time Ser Anal 12(3): 207–224, 1991) unit root tests should be avoided, however, the BG approach can still be used.     

A Multilevel Multinomial Analysis of Interviewer Effects on Various Components of Unit Nonresponse

This note demonstrates an application of a multilevel multinomial model. We use that model to analyse interviewer effects on various components of unit nonresponse to a face-to-face survey: refusals and noncontacts. The model allows for an analysis of these two interviewer effects and a possible connection between both at the same time. Our results show that both the chances for refusals as for noncontacts are subject to interviewer effects. We also find some evidence for a relation between both interviewer effects: interviewers who obtain more refusals are also more likely to report noncontacts. That result is however at least partly dependent on an outlier, an interviewer with a high number of refusals and noncontacts.     

The discrete sequential analysis of dynamic international behavior

This study reexamines the dynamic patterns of US-Soviet foreign policy behavior using recently developed methods for the sequential analysis of categorical data. The methods proposed here have several advantages over conventional regression based modeling procedures. First, assumptions about the role of time in foreign policy behavior and decisionmaking processes are more realistic in that time is represented as an ordinal sequence on human experience. Second, these methods better approximate the causal structure of actionreaction theories by delineating the temporal order of stimulus and response, and by focusing attention on individual behaviors rather than aggregate behavioral constructs. Third, the probabilistic nature of action-reaction hypotheses are made explicit through the use of logit transformed conditional probabilities. Finally, by accommodating model specifications that are virtually isomorphic to traditional action-reaction modeling efforts, these methods retain the flexibility and interpretability of standard regression approaches. Application of these methods to US-Soviet interaction revealed a marked asymmetry in the superpowers' responsiveness to one another's foreign policy behavior during the 1966–1978 period. According to these analyses, Soviet behavior is more clearly a function of its own prior activities than a response to the United States whereas American behavior is far more dependent on prior Soviet actions than its on own.     

Comparison of Hypothesis Testing and Bayesian Model Selection

The main goal of both Bayesian model selection and classical hypotheses testing is to make inferences with respect to the state of affairs in a population of interest. The main differences between both approaches are the explicit use of prior information by Bayesians, and the explicit use of null distributions by the classicists. Formalization of prior information in prior distributions is often difficult. In this paper two practical approaches (encompassing priors and training data) to specify prior distributions will be presented. The computation of null distributions is relatively easy. However, as will be illustrated, a straightforward interpretation of the resulting p-values is not always easy. Bayesian model selection can be used to compute posterior probabilities for each of a number of competing models. This provides an alternative for the currently prevalent testing of hypotheses using p-values. Both approaches will be compared and illustrated using case studies. Each case study fits in the framework of the normal linear model, that is, analysis of variance and multiple regression.