The role of observed and unobserved heterogeneity in the duration of unemployment

This paper studies the degree to which observable and unobservable worker characteristics account for the variation in the aggregate duration of unemployment. I model the distribution of unobserved worker heterogeneity as time varying to capture the interaction of latent attributes with changes in labor-market conditions. Unobserved heterogeneity is the main explanation for the duration dependence of unemployment hazards. Both cyclical and low-frequency variations in the mean duration of unemployment are mainly driven by one subgroup: workers who, for unobserved reasons, stay unemployed for a long time. In contrast, changes in the composition of observable characteristics of workers have negligible effects.     

A Monte Carlo study of ranked efficiency estimates from frontier models

Parametric stochastic frontier models yield firm-level conditional distributions of inefficiency that are truncated normal. Given these distributions, how should one assess and rank firm-level efficiency? This study compares the techniques of estimating (a) the conditional mean of inefficiency and (b) probabilities that firms are most or least efficient. Monte Carlo experiments suggest that the efficiency probabilities are easier to estimate (less noisy) in terms of mean absolute percent error when inefficiency has large variation across firms. Along the way we tackle some interesting problems associated with simulating and assessing estimator performance in the stochastic frontier model.     

A Monte Carlo study of the forecasting performance of empirical SETAR models

In this paper we investigate the multi-period forecast performance of a number of empirical self-exciting threshold autoregressive (SETAR) models that have been proposed in the literature for modelling exchange rates and GNP, among other variables. We take each of the empirical SETAR models in turn as the DGP to ensure that the ‘non-linearity’ characterizes the future, and compare the forecast performance of SETAR and linear autoregressive models on a number of quantitative and qualitative criteria. Our results indicate that non-linear models have an edge in certain states of nature but not in others, and that this can be highlighted by evaluating forecasts conditional upon the regime. Copyright © 1999 John Wiley & Sons, Ltd.     

Optimal forecasting with heterogeneous panels: A Monte Carlo study

We contrast the forecasting performance of alternative panel estimators, divided into three main groups: homogeneous, heterogeneous and shrinkage/Bayesian. Via a series of Monte Carlo simulations, the comparison is performed using different levels of heterogeneity and cross sectional dependence, alternative panel structures in terms of T and N and the specification of the dynamics of the error term. To assess the predictive performance, we use traditional measures of forecast accuracy (Theil’s U statistics, RMSE and MAE), the Diebold–Mariano test, and Pesaran and Timmerman’s statistic on the capability of forecasting turning points. The main finding of our analysis is that when the level of heterogeneity is high, shrinkage/Bayesian estimators are preferred, whilst when there is low or mild heterogeneity, homogeneous estimators have the best forecast accuracy.     

Mixed-effects models for health care longitudinal data with an informative visiting process: A Monte Carlo simulation study

Electronic health records are being increasingly used in medical research to answer more relevant and detailed clinical questions; however, they pose new and significant methodological challenges. For instance, observation times are likely correlated with the underlying disease severity: Patients with worse conditions utilise health care more and may have worse biomarker values recorded. Traditional methods for analysing longitudinal data assume independence between observation times and disease severity; yet, with health care data, such assumptions unlikely hold. Through Monte Carlo simulation, we compare different analytical approaches proposed to account for an informative visiting process to assess whether they lead to unbiased results. Furthermore, we formalise a joint model for the observation process and the longitudinal outcome within an extended joint modelling framework. We illustrate our results using data from a pragmatic trial on enhanced care for individuals with chronic kidney disease, and we introduce user-friendly software that can be used to fit the joint model for the observation process and a longitudinal outcome.     

A semiparametric random effects approach to unobserved heterogeneity in count data models of organizational founding

The paper discusses a semiparametric random-effects approach to the problem of unobserved population heterogeneity in organizational research based on models for pooled cross-sectional time series count data. The analytical value of this approach rests in its ability to produce estimates of the structural parameters that do not depend on any specific assumption about the distribution of the heterogeneity components in the population. The practical value of the method proposed is illustrated in an empirical application to processes of organizational founding, and to the relation between density dependence and unobserved heterogeneity in spatially distributed organizational populations. The empirical evidence produced suggests that future studies of organizational founding at the population level will have to account for variation in observed as well as unmeasured (or unobservable) variables.     

Small-sample properties of dimensionality statistics for fitting VAR models to aggregate economic data: A Monte Carlo study

The analysis of aggregate economic phenomena by VAR's as suggested by Sims often results in a small sample relative to the number of estimated parameters. Since the model is identified by a dimensionality criterion, the small-sample properties of available criteria are important. This paper presents a study of small-sample properties for six criteria with Monte Carlo methods. It is found that no criterion performs well, and that underfitting of models may be quite common.     

A Monte Carlo study of autoregressive integrated moving average processes

Six of the simpler ARMA type models are examined with respect to properties of a variety of proposed estimators of unknown parameters. The findings suggest that if only one estimation method were available to a researcher the choice should probably be maximum likelihood. Stationarity- and invertibility-restricted estimation would appear appropriate when parameters are thought to be within 5 percent of constraint boundaries.     

Tests of non-nested regression models: Small sample adjustments and Monte Carlo evidence

It is known that the small sample significance levels of Cox-type tests of non-nested regression models can be much greater than the nominal level. Adjustments designed to overcome this problem are discussed and two tests are proposed. Monte Carlo evidence on the performance of the tests derived in this paper, the Davidson-MacKinnon J-test and the Fisher-McAleer test is presented. The F-test applied to the comprehensive model is also included in the simulation experiments.     

The (mis)measurement of M&A performance—A systematic narrative literature review

This paper seeks to further the understanding of the variety of meanings M&A scholars attach to the label “M&A performance” by providing an alternative way to interpret the claimed inconsistency of M&A research findings. While many scholars contend that the problem stems from the multiplicity of M&A performance measures, we believe the problem rests in trying to compare different measures as if they were measuring the same feature of the organization. Through our narrative review of empirical research we analyze factors shaping the M&A – as well as the organizational – performance measurement process. The conclusion is that it is not possible to talk about M&A performance as if it was a universal construct.     

Bayesian semiparametric estimation of discrete duration models: an application of the dirichlet process prior

This paper proposes a Bayesian estimator for a discrete time duration model which incorporates a non‐parametric specification of the unobserved heterogeneity distribution, through the use of a Dirichlet process prior. This estimator offers distinct advantages over the Nonparametric Maximum Likelihood estimator of this model. First, it allows for exact finite sample inference. Second, it is easily estimated and mixed with flexible specifications of the baseline hazard. An application of the model to employment duration data from the Canadian province of New Brunswick is provided. Copyright © 2001 John Wiley & Sons, Ltd.     

Time and causality: A Monte Carlo assessment of the timing-of-events approach

We present new Monte Carlo evidence regarding the feasibility of separating causality from selection within non-experimental duration data, by means of the non-parametric maximum likelihood estimator (NPMLE). Key findings are: (i) the NPMLE is extremely reliable, and it accurately separates the causal effects of treatment and duration dependence from sorting effects, almost regardless of the true unobserved heterogeneity distribution; (ii) the NPMLE is normally distributed, and standard errors can be computed directly from the optimally selected model; and (iii) unjustified restrictions on the heterogeneity distribution, e.g., in terms of a pre-specified number of support points, may cause substantial bias.     

Monte Carlo tests with nuisance parameters: A general approach to finite-sample inference and nonstandard asymptotics

The technique of Monte Carlo (MC) tests [Dwass (1957, Annals of Mathematical Statistics 28, 181–187); Barnard (1963, Journal of the Royal Statistical Society, Series B 25, 294)] provides a simple method for building exact tests from statistics whose finite sample distribution is intractable but can be simulated (when no nuisance parameter is involved). We extend this method in two ways: first, by allowing for MC tests based on exchangeable possibly discrete test statistics; second, by generalizing it to statistics whose null distribution involves nuisance parameters [maximized MC (MMC) tests]. Simplified asymptotically justified versions of the MMC method are also proposed: these provide a simple way of improving standard asymptotics and dealing with nonstandard asymptotics.     

Forecast Evaluation Under Asymmetric Loss: A Monte Carlo Analysis of the EKT Method

This paper contributes to the literature on forecast evaluation by conducting an extensive Monte Carlo experiment using the evaluation procedure proposed by Elliott, Komunjer and Timmermann. We consider recent developments in weighting matrices for GMM estimation and testing. We pay special attention to the size and power properties of variants of the J‐test of forecast rationality. Proceeding from a baseline scenario to a more realistic setting, our results show that the approach leads to precise estimates of the degree of asymmetry of the loss function. For correctly specified models, we find the size of the J‐tests to be close to the nominal size, while the tests have high power against misspecified models. These findings are quite robust to inducing fat tails, serial correlation and outliers.     

The CES and the logit: Two related models of heterogeneity

The demand system generated from a CES utility function is formally equivalent to the solution of a nested logit model to which the second stage is described by a deterministic Cobb-Douglas utility. On the other hand, the logit demands can be associated with a representative consumer maximizing a deterministic utility of the entropy type.     

The demand for teleshopping: An application of discrete choice models

New telecommunications technologies have often been suggested to act as substitutes for travel. Teleshopping may be a substitute for traditional store shopping. This paper studies the determination of the demand for teleshopping. In general, the demand for telecommunications is derived from the demand for information. So we look at teleshopping as a form of information gathering. Agents can engage in various shopping activities, including teleshopping, which entail different time and monetary costs. Through a discrete choice model, we analyze the effect of costs on the choice of shopping activities. The model is estimated based on a pilot experiment in which individuals had to choose among information bundles when shopping for differentiated products. Implicit in the choice set were information items which can only be transmitted by specific media. Limitations of this preliminary work and suggestions for future research are discussed.     

The effects of social heterogeneity on travel patterns: A replication study

This paper examines differences between the U.S. and Australian urban landscape in terms of the relationship between urban structure and the effectiveness of public transportation. A replication study is undertaken that tests the validated hypothesis from a U.S. study that urban travel corridor social heterogeneity reduces patronage of public transportation because of resident concerns with social composition of passengers and effects on the travel privacy dimension. A causal model is identified for the city of Sydney that specifies enogenous and endogenous variables, as per the U.S. study, which affect bus and rail use. Results do not lead to the acceptance of the hypothesized relationship. The presence of social heterogeneity in Sydney travel corridors does not deter residents within the study corridors from using the bus or train for the journey to work, nor do they appear to affect longer term decisions on car ownership. Bus patronage was shown to be dependent on social heterogeneity factors while rail use was associated with car owning households. These results highlight differences in urban spatial structure and travel patterns between Sydney and cities in the U.S. study, and suggest differential effects of changes in urban spatial structure and policy responses.     

The impact of purchasing strategy-structure (mis)fit on purchasing cost and innovation performance

The organizational design literature strongly supports the notion of “structure follows strategy”, and suggests that a misfit between the two has a negative effect on performance. Building on this line of argument, we examine to what extent the (mis)fit between purchasing strategy and purchasing structure impacts purchasing performance. We focus on cost and innovation purchase category strategies, and examine how the deviation from an ideal purchasing structure defined along three dimensions (centralization, formalization, and cross-functionality) impacts purchasing performance. Analysing data collected from 469 firms in ten countries, we demonstrate that a strategy-structure misfit negatively impacts purchasing performance in both cost and innovation strategies. We also find that purchasing proficiency is a mediator in this relationship between misfit and performance. Our findings aid managerial decision making by empirically validating the necessity of having the right purchasing structure for successfully executing different purchasing strategies.