The beta-binomial convolution model for 2×2 tables with missing cell counts

This paper considers the beta-binomial convolution model for the analysis of 2×2 tables with missing cell counts. We discuss maximum-likelihood (ML) parameter estimation using the expectation–maximization algorithm and study information loss relative to complete data estimators. We also examine bias of the ML estimators of the beta-binomial convolution. The results are illustrated by two example applications.     

A CYCLING METHOD FOR CONSTRUCTING INPUT–OUTPUT TABLE TIME SERIES FROM INCOMPLETE DATA

There are a number of approaches for constructing time series of input–output tables. Some authors generate an initial estimate for a base year, and then serially estimate tables for subsequent years using the balanced prior-year table as an initial estimate. Others first generate a series of initial estimates for the entire period, and then balance tables in parallel. Current serial methods are affected by sudden leaps in the magnitude of table elements, which occur straight after a period of data unavailability. Current parallel methods require two complete tables for base and final years in the same classification, and therefore do not work under misaligned or incomplete data. We present a new method for constructing input–output table time series that overcomes these problems by averaging over alternate forward and backward sweeps across the time series period. We also solve the problem of hysteresis causing forecast and backcast table estimates to differ.     

Robust analysis of longitudinal data with nonignorable missing responses

We encounter missing data in many longitudinal studies. When the missing data are nonignorable, it is important to analyze the data by incorporating the missing data mechanism into the observed data likelihood function. The classical maximum likelihood (ML) method for analyzing longitudinal missing data has been extensively studied in the literature. However, it is well-known that the ordinary ML estimators are sensitive to extreme observations or outliers in the data. In this paper, we propose and explore a robust method, which is developed in the framework of the ML method, and is useful for downweighting any influential observations in the data when estimating the model parameters. We study the empirical properties of the robust estimators in small simulations. We also illustrate the robust method using incomplete longitudinal data on CD4 counts from clinical trials of HIV-infected patients.     

Taking ‘Don’t Knows’ as Valid Responses: A Multiple Complete Random Imputation of Missing Data

Incomplete data is a common problem of survey research. Recent work on multiple imputation techniques has increased analysts’ awareness of the biasing effects of missing data and has also provided a convenient solution. Imputation methods replace non-response with estimates of the unobserved scores. In many instances, however, non-response to a stimulus does not result from measurement problems that inhibit accurate surveying of empirical reality, but from the inapplicability of the survey question. In such cases, existing imputation techniques replace valid non-response with counterfactual estimates of a situation in which the stimulus is applicable to all respondents. This paper suggests an alternative imputation procedure for incomplete data for which no true score exists: multiple complete random imputation, which overcomes the biasing effects of missing data and allows analysts to model respondents’ valid ‘I don’t know’ answers.     

Restricted maximum likelihood estimation under Eisenhart model Ill

For a balanced two-way mixed model, the maximum likelihood (ML) and restricted ML (REML) estimators of the variance components were obtained and compared under the non-negativity requirements of the variance components by L ee and K apadia (1984). In this note, for a mixed (random blocks) incomplete block model, explicit forms for the REML estimators of variance components are obtained. They are always non-negative and have smaller mean squared error (MSE) than the analysis of variance (AOV) estimators. The asymptotic sampling variances of the maximum likelihood (ML) estimators and the REML estimators are compared and the balanced incomplete block design (BIBD) is considered as a special case. The ML estimators are shown to have smaller asymptotic variances than the REML estimators, but a numerical result in the randomized complete block design (RCBD) demonstrated that the performances of the REML and ML estimators are not much different in the MSE sense.     

An Incomplete Contract Perspective on Public Good Provision

Abstract.  This paper surveys what can be learned from recent advances in the incomplete contract literature to understand how public goods are or should be provided. The paper starts with a section on the full information case that presents and discusses the classical Samuelson condition on the optimal provision of public goods. The rest of the paper presents results under asymmetric information. It is constituted of two main parts. In the first one, the social planner has complete contracting ability. We discuss the basic setting and assumptions of this comprehensive contracting approach and study the trade-offs it generates. The second part of the paper is devoted to the study of contracting incompleteness. Such incompleteness can emerge from various sources, which we present and discuss. We then study the case of a politically chosen decision-maker and the consequences of its inability to commit for more than one period and of the ability for individuals to form groups. Finally, we address the problem of the choice between public and private forms of public good provision. The concluding section summarizes the main policy lessons.     

Protestantism, Labor Force Participation, and Employment Across Countries

A bstract .   Using data from 80 countries, this article analyzes whether Protestant religion affects labor market outcomes. Controlling for the impact of labor market regulations, business regulations, the tax burden, the business cycle, the level of economic development, demographic and geographical conditions, wars, and the transition from planned to market economy as well as unobserved country and year effects, we find that countries in which the largest portion of the population practices Protestant religion have substantially higher labor force participation and employment rates, particularly among women. We obtain the same result for a subgroup of 19 industrial countries for which we have better data to control for the impact of labor market institutions and business cycle fluctuations.     

PRIVATIZATION AND EFFICIENCY: FROM PRINCIPALS AND AGENTS TO POLITICAL ECONOMY

Abstract.  We survey the theoretical literature on privatization and efficiency by tracing its evolution from the applications of agency theory to recent contributions in the field of political economy. The former extend the theory of regulation with incomplete information to address privatization issues, comparing state-owned enterprises with private regulated firms. The benefits of privatization may derive either from the constraints it places on malevolent agents or from the impossibility of commitment by a benevolent government because of incomplete contracts. Contributions dealing with political economy issues separate privatization from restructuring decisions. They either explore bargaining between managers and politicians or analyse the impact of privatization shaped by political preferences on efficiency. The theoretical results regarding the relation between privatization and efficiency do not lead to any definitive conclusion. Privatization may increase productive efficiency when restructuring takes place whereas its effects on allocative efficiency still remain uncertain.     

Unemployment Duration: An Analysis of Incomplete,Completed, and Multiple Spells in Emilia-Romagna

Sets of incomplete and completed spells of unemployment wereobtained from the Italian Quarterly Labour Force Survey,carried out by ISTAT in Emilia-Romagna (1993:1–1995:1).The data were analysed through a proportional hazards modelwith a Weibull specification of the baseline hazard, includingboth unobserved heterogeneity applied to the scale parameterof the duration distribution, and telescoping effect to accountfor spikes in the distribution of unemployment spells.They were compared formally also with a non-proportional hazardmodel based on a log-logistic distribution of duration.The estimation of the parameters was carried out separately on bothcompleted spells and quarterly incomplete spells to ascertaindifferences and to envisage the potential seasonal effect.The results showed that the shape parameters changed over time.The parameters of each covariate proved to be statisticallystable over time and were also equal to the parameters ofthe completed spells model.Therefore, the analysis of incomplete spells is fairly feasiblewhen an even (closed form) baseline hazard function is suitablefor data.     

Sequential Monte Carlo Methods for Estimating Dynamic Microeconomic Models

This paper develops estimators for dynamic microeconomic models with serially correlated unobserved state variables using sequential Monte Carlo methods to estimate the parameters and the distribution of the unobservables. If persistent unobservables are ignored, the estimates can be subject to a dynamic form of sample selection bias. We focus on single‐agent dynamic discrete‐choice models and dynamic games of incomplete information. We propose a full‐solution maximum likelihood procedure and a two‐step method and use them to estimate an extended version of the capital replacement model of Rust with the original data and in a Monte Carlo study. Copyright © 2015 John Wiley & Sons, Ltd.     

Coordinating under incomplete information

We show that, in a minimum effort game with incomplete information where player types are independently drawn, there is a largest and smallest Bayesian equilibrium, leading to the set of equilibrium payoffs (as evaluated at the interim stage) having a lattice structure. Furthermore, the range of equilibrium payoffs converges to those of the deterministic complete information version of the game, in the limit as the incomplete information vanishes. This entails that such incomplete information alone cannot explain the equilibrium selection suggested by experimental evidence. We thank Atila Abdulkadiroglu, Hans Carlsson, Ani Guerdjikova and an anonymous referee for helpful comments. Part of this work was done while Asheim was visiting Cornell University, which hospitality is gratefully acknowledged.     

Modeling frailty-correlated defaults using many macroeconomic covariates

We propose a novel time series panel data framework for estimating and forecasting time-varying corporate default rates subject to observed and unobserved risk factors. In an empirical application for a U.S. dataset, we find a large and significant role for a dynamic frailty component even after controlling for more than 80% of the variation in more than 100 macro-financial covariates and other standard risk factors. We emphasize the need for a latent component to prevent a downward bias in estimated default rate volatility and in estimated probabilities of extreme default losses on portfolios of U.S. debt. The latent factor does not substitute for a single omitted macroeconomic variable. We argue that it captures different omitted effects at different times. We also provide empirical evidence that default and business cycle conditions partly depend on different processes. In an out-of-sample forecasting study for point-in-time default probabilities, we obtain mean absolute error reductions of more than forty percent when compared to models with observed risk factors only. The forecasts are relatively more accurate when default conditions diverge from aggregate macroeconomic conditions.     

信息不完全的信用风险定价模型及其在我国的应用

基于信息不完全的信用风险定价模型与传统的结构化模型和约化模型的最大区别在于它将信息不完全这一前提引入了以信息完全为前提的结构化模型,同时它又考虑了约化模型中强度的优点,引入短期信用风险的度量,成为当前最切合现实的信用风险定价模型。本文认为,应用基于信息不完全的信用风险定价模型来测度信用风险,将具有十分重要的现实意义。     

A Meta‐Analysis of β‐Convergence: the Legendary 2%

Abstract.  The topic of convergence is at the heart of a wide‐ranging debate in the growth literature, and empirical studies of convergence differ widely in their theoretical backgrounds, empirical specifications, and in their treatment of cross‐sectional heterogeneity. Despite these differences, a rate of convergence of about 2% has been found under a variety of different conditions, resulting in the widespread belief that the rate of convergence is a natural constant. We use meta‐analysis to investigate whether there is substance to the 'myth' of the 2% convergence rate and to assess several unresolved issues of interpretation and estimation. Our data set contains approximately 600 estimates taken from a random sample of empirical growth studies published in peer‐reviewed journals. The results indicate that it is misleading to speak of a natural convergence rate since estimates of different growth regressions come from different populations, and we find that correcting for the bias resulting from unobserved heterogeneity in technology levels leads to higher estimates of the rate of convergence. We also find that correcting for endogeneity of the explanatory variables has a substantial effect on the estimates and that measures of financial and fiscal development are important determinants of long‐run differences in per capita income levels. We show that although the odds of a study being published is not uniform for studies with different p ‐values, publication bias has no significant effect on the conclusions of the analysis.     

Efficient high-dimensional importance sampling

The paper describes a simple, generic and yet highly accurate efficient importance sampling (EIS) Monte Carlo (MC) procedure for the evaluation of high-dimensional numerical integrals. EIS is based upon a sequence of auxiliary weighted regressions which actually are linear under appropriate conditions. It can be used to evaluate likelihood functions and byproducts thereof, such as ML estimators, for models which depend upon unobservable variables. A dynamic stochastic volatility model and a logit panel data model with unobserved heterogeneity (random effects) in both dimensions are used to provide illustrations of EIS high numerical accuracy, even under small number of MC draws. MC simulations are used to characterize the finite sample numerical and statistical properties of EIS-based ML estimators.     

Maximum likelihood methods for fitting the burr type XII distribution to multiply (progressively) censored life test data

Summary This paper develops mathematical and computational methodology for fitting, by the method of maximum likelihood (ML), the Burr Type XII distribution to multiply (or progressively) censored life test data. Mathematical expressions are given for approximating the asymptotic variances and covariances of the ML estimates (MLEs) of the parameters of the Burr Type XII distribution. A rigorous mathematical analysis is undertaken to investigate the existence and uniqueness of the MLEs for arbitrary sample data. The methodology of this paper is applied to progressively censored sample data arising in a life test experiment.     

Statistical inference for continuous-time Markov processes with block structure based on discrete-time network data

A widely used approach to modeling discrete-time network data assumes that discrete-time network data are generated by an unobserved continuous-time Markov process. While such models can capture a wide range of network phenomena and are popular in social network analysis, the models are based on the homogeneity assumption that all nodes share the same parameters. We remove the homogeneity assumption by allowing nodes to belong to unobserved subsets of nodes, called blocks, and assuming that nodes in the same block have the same parameters, whereas nodes in distinct blocks have distinct parameters. The resulting models capture unobserved heterogeneity across nodes and admit model-based clustering of nodes based on network properties chosen by researchers. We develop Bayesian data-augmentation methods and apply them to discrete-time observations of an ownership network of non-financial companies in Slovenia in its critical transition from a socialist economy to a market economy. We detect a small subset of shadow-financial companies that outpaces others in terms of the rate of change and the desire to accumulate stocks of other companies.     

Properties of a distribution-free two-stage two-sample median test

Abstract  In this paper we consider a class of distribution-free two-stage two-sample median tests. These procedures are based on a fixed size sample from one population, while the observations from the second population are collected in a truncated sequential fashion. Necessary tables are provided enabling us to select a particular member from the class such that arbitrarily preset power levels are approximately obtained. We also present asymptotic expressions and tables for the expected number of observations from the second population. Finally, comparisons between the asymptotic version of this two-stage median test and the binomial sequential probability ratio test due to W ald (1947) are discussed.     

Effects of Household Consumption Patterns on CO2 Requirements

In order to evaluate the relation between the consumption pattern of various household types and their CO₂ requirements, we combine input-output tables energy flow matrices, CO₂ emissions factors, and national consumer survey statistics into an integrated modelling framework, and relate differences in household types to differences in private consumption and again to differences in CO₂ emissions. We identify household characteristics with a significant influence on CO₂ emissions. Comparing our results with those of other studies reveals that national differences in climate and population density cause differences in the contribution to CO₂ emissions. Finally, national differences in income and expenditure elasticities of both energy and CO₂ are due to differences in the disparity in CO₂ intensities amongst commodities and to the model's assumptions on foreign technology.