Accounting for missing data in M-estimation: a general matching approach

This paper addresses M-estimation of conditional mean functions when observations are missing at random. The usual approach of correcting for missing data, when the missing data mechanism is ignorable, is inverse probability weighting (IPW). An alternative semiparametric M-estimator which involves local polynomial matching techniques is proposed and its asymptotic distribution is derived. Like IPW, the proposed estimation approach has a double robustness property for the estimation of unconditional means. Monte Carlo evidence suggests slightly better finite sample properties of the semiparametric M-estimator relatively to IPW. A version of the proposed estimator is applied to estimate the impact of noncognitive skills on wages in Germany for two different educational treatment regimes.     

Parameter identification in mixed Brownian–fractional Brownian motions using Powell's optimization algorithm

This paper deals with the problem of estimating the parameters of mixed Brownian–fractional Brownian motions with the combination of maximum likelihood approach and Powell's method. The maximum likelihood estimators are obtained based on the approximation by random walks of the driving noise. By adapting the Powell fast optimization algorithm, these estimators can be efficiently computed by computer software. The performance of our method is tested on simulated mixed Brownian–fractional Brownian motion data sets, and is compared with the approach proposed by Filatova (2008).     

A Theoretical Analysis of Trade Union Membership Fees, Bargaining Power, Wage Rate and Unemployment

The positive effect of membership fees on trade unions' cohesion and commitment and the adverse effect of these fees on trade unions' density imply that the effects of membership fees on trade unions' bargaining power, wage rate and unemployment can be depicted by inverted U-shaped curves whose upper bounds are reached when membership fees are set at half the ratio of the upper-bound on members' level of satisfaction from the trade union services to their disposable income rate. The implications of these effects for membership fees are analysed for a trade union that sets its membership fee so as to minimise the loss stemming from missing wage rate and unemployment targets.     

Study of population heterogeneity in innovation diffusion model: Estimation based on simulated annealing

Parameter variability randomness in diffusion (PVRD) models based on random differential equations have recently been developed to study stochastic evolution of adopters. Analysis of such models is found to generate multimodal life cycle patterns (or intervening slumps) besides the conventional unimodal pattern. Application of these models to real data sets necessitate estimation of parameters of the model. Nonlinear least squares estimation problem is formulated to deal with the minimization of high-dimensional cost function. Using the simulated annealing (SA) framework, effectiveness of the estimation approach and the fitting algorithm is demonstrated in terms of “fit statistics.” An important finding from empirical studies reveal that even in unimodal life cycle patterns, parameters of innovation diffusion process are found to possess considerable variability. This finding amply demonstrates the presence of heterogeneity on account of population variability.     

Estimating SUR system with random coefficients: the unbalanced panel data case

A system of regression equations for analyzing panel data with random heterogeneity in intercepts and coefficients, and unbalanced panel data is considered. A maximum likelihood (ML) procedure for joint estimation of all parameters is described. Since its implementation for numerical computation is complicated, simplified procedures are presented. The simplifications essentially concern the estimation of the covariance matrices of the random coefficients. The application and ‘anatomy’ of the proposed algorithm for modified ML estimation are illustrated by using panel data for output, inputs and costs for 111 manufacturing firms observed up to 22 years.     

Forensic Econometrics: Demand Estimation When Data are Missing

Often empirical researchers face many data constraints when estimating models of demand. These constraints can sometimes prevent adequate evaluation of policies. In this article, we discuss two such missing data problems that arise frequently: missing data on prices and missing information on the size of the potential market. We present some ways to overcome these limitations in the context of two recent research projects. Jacobi and Sovinsky (2018), which addresses how to incorporate unobserved price heterogeneity, and Hidalgo and Sovinsky (2018), which focuses on how to use modelling techniques to estimate missing market size. Our aim is to provide a starting point for thinking about ways to overcome common data issues.     

Decomposition-integral: unifying Choquet and the concave integrals

This paper introduces a novel approach to integrals with respect to capacities. Any random variable is decomposed as a combination of indicators. A prespecified set of collections of events indicates which decompositions are allowed and which are not. Each allowable decomposition has a value determined by the capacity. The decomposition-integral of a random variable is defined as the highest of these values. Thus, different sets of collections induce different decomposition-integrals. It turns out that this decomposition approach unifies well-known integrals, such as Choquet, the concave and Riemann integral. Decomposition-integrals are investigated with respect to a few essential properties that emerge in economic contexts, such as concavity (uncertainty-aversion), monotonicity with respect to stochastic dominance and translation-covariance. The paper characterizes the sets of collections that induce decomposition-integrals, which respect each of these properties.     

Imputation of Missing Values in the Fundamental Data: Using MICE Framework

Revolutionary developments in the field of big data analytics and machine learning algorithms have transformed the business strategies of industries such as banking, financial services, asset management, and e-commerce. The most common problems these firms face while utilizing data is the presence of missing values in the dataset. The objective of this study is to impute fundamental data that is missing in financial statements. The study uses ‘Multiple Imputation by Chained Equations’ (MICE) framework by utilizing the interdependency among the variables that wholly comply with accounting rules. The proposed framework has two stages. The initial imputation is based on predictive mean matching in the first stage and resolving financial constraints in the second stage. The MICE framework allows us to incorporate accounting constraints in the imputation process. The performance tests conducted on the imputed dataset indicate that the imputed values for the 177 line items are good and in line with the expectations of subject matter experts.

     

Procedural fairness and the tolerance for income inequality

This paper presents an experiment investigating what cultural and institutional factors underlying a society might render its members more or less tolerant of inequality in favor of greater efficiency. The specific institutional factors we address concern the fairness in the procedures or mechanisms through which individuals believe initial positions or roles in society are determined. Subjects' initial positions (rich vs. poor) are determined based on various criteria (random, meritocratic, arbitrary, and rewarding uncooperative behavior) and individuals' willingness to approve Pareto improvement when the improvement is mainly in favor of the already rich is measured. Our findings show that individuals' willingness to accept higher but more unequal outcomes depends on the source of the initial inequality and random assignment leads to the most tolerance for disadvantageous inequality, generating doubt about commonly held views concerning meritocracy. Moreover, holding the procedures constant, subjects reveal greater tolerance for inequality when self and the opponent share common group identity.     

Robust tests for time-invariant individual heterogeneity versus dynamic state dependence

We derive tests for persistent effects in a general linear dynamic panel data context. Two sources of persistent behavior are considered: time-invariant unobserved factors (captured by an individual random effect) and dynamic persistence or “state dependence” (captured by autoregressive behavior). We will use a maximum likelihood framework to derive a family of tests that help researchers learn whether persistence is due to individual heterogeneity, dynamic effect, or both. The proposed tests have power only in the direction they are designed to perform, that is, they are locally robust to the presence of alternative sources of persistence, and consequently, are able to identify which source of persistence is active. A Monte Carlo experiment is implemented to explore the finite sample performance of the proposed procedures. The tests are applied to a panel data series of real GDP growth for the period 1960–2005.     

基于EM-NB算法的网络调查缺失数据处理方法

将最大期望值算法(EM)与朴素贝叶斯算法(NB)相结合,提出EM-NB算法来填补网络调查中的缺失数据。对比基于处理后的完备数据集的分类统计结果与基于纸质调查得到的分析结果,结果显示,利用EM-NB算法处理缺失数据后的网络调查问卷与纸质调查问卷可得到一致的调查结果。这表明EM-NB算法是一种有效的处理网络调查中缺失数据的方法。     

Radicalness of blockchain: an assessment based on its impact on the payments industry

Blockchain is receiving considerable attention among practitioners and academics. However, a detailed analysis investigating how the impact of blockchain is going to unfold is still missing. Our article sets out to assess the radicalness of blockchain at an early stage of its development and substantiates the analysis with evidence from the payments industry. We start by distilling five key aspects of radical innovation from extant literature. Our analysis of blockchain then focuses on the payments industry, which represents a major business field of banking and the cradle of this technology. The results of a Delphi study provide 17 statements about the current development of blockchain. The statements deliver insights into the impact of blockchain on the payments industry and reveal the radicalness of the technology. Based on these insights, we investigate how blockchain affects the five key aspects of radical innovation and develop contributions from an innovation management’s perspective.     

Do Psychological Fallacies Influence Trading in Financial Markets? Evidence from the Foreign Exchange Market

Research in both economics and psychology suggests that when agents predict the next value of a random series they frequently exhibit two types of biases, which are called the gambler's fallacy (GF) and the hot hand fallacy (HHF). The GF is to expect a negative correlation in a process that is in fact random. The HHF is more or less the opposite of this—to believe that another heads is more likely after a run of heads. The evidence for these fallacies comes largely from situations where they are not punished (lotteries, casinos, and laboratory experiments with random returns). In many real-world situations, such as in financial markets, succumbing to fallacies is costly, which gives an incentive to overcome them. The present study is based on high-frequency data from a market maker in the foreign exchange market. Trading behavior is only partly explained by the rational exploitation of past patterns in the data. There is also evidence of the GF: a tendency to sell the dollar after it has risen persistently or strongly.     

Earnings and Languages in the Family: Second‐Generation Australians*

This paper uses Household, Income and Labour Dynamics in Australia data to assess the performance of second‐generation Australians in full‐time employment in 2007. It examines the role of job mismatch and cultural and linguistic diversity at the individual and family levels. The study accounts for non‐random sample selection. The new evidence shows that: (i) over‐education and over‐skilling carry a wage penalty; (ii) there are significant but heterogeneous second‐generation effects; and (iii) language effects explain most of the disadvantage associated with non‐English‐speaking background.     

MICRO-MACRO LINK FOR GOVERNMENT

In the Dutch statistics on government finance a micro/macro link is established. The paper describes why and how this has been done. It appears to be of relevance to the users of the statistics to present two different data sets: one according to an accounting/administrative point of view and one fitting in the National Accounts. The main features of the way in which these data sets are derived from the underlying bookkeeping documents are given and it is shown how they relate to the accounting and juridical structures of the various government agencies. It appears that in order to arrive at homogeneous data sets, adaptations are in order, mainly bearing on the entries; for the National Account data further transformations, relating to transactions as well as transactors, will appear necessary. It will be enunciated how the relation between these data sets is shown in the statistics on government finance and how, in the same course a micro/macro link is provided for.     

A Hausman test for non-ignorability

Using an empirical likelihood approach, we show that generalized linear models can still be consistently estimated even if dependent variables are not missing at random, and derive a Hausman test by comparing this estimator to the standard one.     

Upper and lower bounds for expected utility

Summary.   The decision-theoretic literature has developed very few techniques to bound the expected utility of a random variable when only simple statistics like its median or mode or mean are known. One reason for this lack of results is that we are missing a convenient way to link probability theory and expected utility. This paper is written to demonstrate a general (and genuinely probabilistic) technique to obtain upper and lower bounds for the expected utility of a lottery. Received: December 14, 1999; revised version: March 8, 2000     

Can Economists Forecast Exchange Rates? The Debate Re‐Visited: The Case of the USD/GBP Market

In the debate on forecasting exchange rates, critics claimed that traditional macroeconomic models could not outperform a random walk in post‐sample forecasts. Perceived deficiencies include inadequate allowance for simultaneity, and expectations hypotheses inconsistent with the structure of models employed. This paper re‐visits the debate, first to address critics' major concerns, and second because in the view of the present authors, the debate closed on an unduly pessimistic note. This paper develops a simultaneous, rational expectations model of the USD/GBP market, with functional relationships for hedgers, speculators and a spot rate equation. The model is estimated with data contemporaneous to the debate, including a period during which the US Commodity Futures Trading Commission did not collect data on traders' open positions. Using the results of post‐debate research on tests for stationarity with missing observations, the model, using only public information, outperforms a random walk in post‐sample forecasts of the spot rate. Recent microstructure models of the exchange rate based on order flow have re‐kindled the forecasting debate. The model developed here, however, is differentiated from these microstructure models, first because order flow utilises both public and private information, and second because the microstructure models do not directly address critics' concerns.     

Structuring the future—application of a scenario-generation procedure

Scenario generation is a fundamental technique of futures research. By providing a series of possible future contexts, it is a valuable tool for decision makers. A number of scenario generation procedures have been proposed and may all be appropriate in particular circumstances. These procedures differ in regard to their approach to structure, the nature of the scenario elements used, their handling of the time dimension, their approach to scenario probabilities, the scope or size of the scenarios, and a few other aspects. A number of suggested techniques are classified in regard to these characteristics and a case is described for a procedure that provides for large numbers of events and trends (over 100) in a multiperiod framework and that can produce a fairly small number of the most likely scenarios that contain a reasonable variety. An approach to this problem is described, and an application in the Western Australian Government Railway organization, Westrail, is presented.     

Technological growth curves : A competition of forecasting models

In order to determine procedures for appropriate model selection of technological growth curves, numerous time series that were representative of growth behavior were collected and categorized according to data characteristics. Nine different growth curve models were each fitted onto the various data sets in an attempt to determine which growth curve models achieved the best forecasts for differing types of growth data. The analysis of the results gives rise to a new approach for selecting appropriate growth curve models for a given set of data, prior to fitting the models, based on the characteristics of the data sets.