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.     

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.     

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.     

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.     

Discriminating between autoregressive forms: A Monte Carlo comparison of Bayesian and ad hoc methods

The paper considers the problem of discriminating between the autoregressive forms of a Koyck distributed lag model and a regression model with autocorrelated distrubances. Several interpretations of an ad hoc rule-of-thumb suggested by Griliches are compared with Bayesian posterior odds analysis in a Monte Carlo experiment. The Bayesian analysis is generally superior to the rules-of-thumb, the latter exhibiting large probabilities of type I error, and low power. The rules-of-thumb excessively favour the distributed lag model, while the Bayesian method is free from such bias. All methods improve with increased sample size.     

A Principal Components Analysis of Common Stochastic Trends in Heterogeneous Panel Data: Some Monte Carlo Evidence

In this paper we propose a new approach based on principal components analysis to test for the number of common stochastic trends driving the non-stationary series in a panel data set. This test has the advantage that it is also consistent when there is a mixture of I (0) and I (1) series, making it unnecessary to pre-test the panel for unit root. Furthermore, the test solves the problem of dimensionality encountered in large panel data sets.     

Pitfalls in the estimation of a cost function that ignores allocative inefficiency: A Monte Carlo analysis

In the stochastic frontier literature, it is a widely held view that allocative inefficiency can be lumped together with technical inefficiency in the estimation of cost frontiers. Therefore, a one-sided error term in the cost function is believed to capture the cost of overall (technical plus allocative) inefficiency. In this paper we challenge that view through a detailed Monte Carlo investigation. The results show that failure to include the cost of allocative inefficiency explicitly in the cost function biases the estimates of: (i) the cost function parameters, (ii) returns to scale, (iii) input price elasticities, and (iv) cost-inefficiency.     

Forecasting risk with Markov-switching GARCH models:A large-scale performance study

We perform a large-scale empirical study in order to compare the forecasting performances of single-regime and Markov-switching $\text{GARCH}$ ($\text{MSGARCH}$) models from a risk management perspective. We find that $\text{MSGARCH}$ models yield more accurate Value-at-Risk, expected shortfall, and left-tail distribution forecasts than their single-regime counterparts for daily, weekly, and ten-day equity log-returns. Also, our results indicate that accounting for parameter uncertainty improves the left-tail predictions, independently of the inclusion of the Markov-switching mechanism.     

When worlds collide: Different comparative static predictions of continuous and discrete agent models with land

This paper presents a difference in the comparative statics of general equilibrium models with land when there are finitely many agents, and when there is a continuum of agents. Restricting attention to quasi-linear and Cobb–Douglas utility, it is shown that with finitely many agents, an increase in the (marginal) commuting cost increases land rent per unit (that is, land rent averaged over the consumer's equilibrium parcel) paid by the consumer located at each fixed distance from the central business district. In contrast, with a continuum of agents, average land rent goes up for consumers at each fixed distance close to the central business district, is constant at some intermediate distance, and decreases for locations farther away. Therefore, there is a qualitative difference between the two types of models, and this difference is potentially testable.     

The effect of data transformation on common cycle,cointegration, and unit root tests: Monte Carlo results and a simple test

Cointegration, common cycle, and related tests statistics are often constructed using logged data, even without clear reason why logs should be used rather than levels. Unfortunately, it is also the case that standard data transformation tests, such as those based on Box–Cox transformations, cannot be shown to be consistent unless assumptions concerning whether variables I(0)$I(0)$ or I(1)$I(1)$ are made. In this paper, we propose a simple randomized procedure for choosing between levels and log-levels specifications in the (possible) presence of deterministic and/or stochastic trends, and discuss the impact of incorrect data transformation on common cycle, cointegration and unit root tests.     

现代标准化前沿——"模块化"研究报告(5)第五章产业集群:充满活力的新兴产业形式——模块化对产业结构的影响

电脑业能发展成今天这样的产业集群，其根源在于电脑设计的模块化。 ——鲍德温教授（哈佛大学商学院前副院长） IBM在设计上的模块化战略最终导致了电脑产业结构的飞速升级和持续的创新。 ——张军（上海复旦大学经济学教授）[编者按]     

全国英语等级考试(PETS)攻略

全国英语等级考试(PETS)是教育部组织的,旨在大力推进外语教学、考试改革的一项有力措施。2003年,要在全国大力推广。此文就PETS的产生背景及其理念,学习PETS所能得到的独特的收获,考题的等级及与其他国内考试的比较,以及如何备考,如何应考等,提出了几点建议。     

Duration of maternity leave in Germany: A case study of nonparametric hazard models and penalized splines

The paper investigates maternity leave behavior in West Germany for females being employed between 1995 and 2006 using data from the German Socio Economic Panel. The observational study focuses on the investigation of individual and family-related covariate effects on the duration of maternity leave following first or second childbirth, respectively. Dynamic duration time models are used in which covariate effects are allowed to vary smoothly with duration of being in maternity leave. The intention of the paper is to demonstrate with state of the art models how effects of covariables change over time and to analyse substantial differences between maternity leaves following first and second childbirth. Particularly the personal income of mothers and the educational attainment influence the decision when to return into employment. The leave period following second birth is influenced by the mothers' attachment to the labour market between their two maternity leave periods. As fitting routine penalized spline smoothing effects is employed using available software in R (http://www.r-project.org).