Full-information Approach Research Articles

Multinational's Entry: Boon or Bane for Non-Frontier Economies?

Most countries in the world are not at the technological frontier, yet their economies grow and fluctuate. This paper sets up a quantitative model of endogenous growth with business cycle fluctuations to analyze the medium frequency fluctuations in non-frontier countries. The growth mechanism is a Schumpeterian creative destruction framework embedded into a real business cycle dynamic stochastic general equilibrium model, with standard and non-standard features. We allow multinational firms to enter the economy and challenge existing incumbents, which permits us to study the tension between their direct positive productivity contribution and their indirect negative contribution through the expected obsolescence of domestic innovators. We estimate the model using a full-information approach and show that multinationals' entry is both boon and bane for non-frontier economies.

Reduction of tension effects on partner evaluation

PurposeThe purpose of this paper is to examine how tension management as a means of achieving compromise and overcoming minor conflicts reduces the effects of the opportunism and bargaining costs of relationships on the evaluation of business partners. The paper proposes and tests a theoretical model with a full-information approach using structural equation modeling.Design/methodology/approachThe data set was based on 312 observations from a unique survey based on a business-to-business relationship sample in Sweden. The measurements reflect the effects of partner opportunism, bargaining costs, and tension management on partner evaluations.FindingsThe findings corroborate that partner opportunism and bargaining costs have a negative impact on partner evaluation. In addition, the model shows that tension management weakens the negative effects of opportunism and bargaining costs on the evaluation.Originality/valueThis study offers evidence on how negative effects are reduced through intervening constructs. With most studies focusing on the positive side of relationships, this paper makes an important contribution to the literature through not only describing negative effects, but also how these can be decreased.

Multivariate normal maximum likelihood with both ordinal and continuous variables, and data missing at random.

A novel method for the maximum likelihood estimation of structural equation models (SEM) with both ordinal and continuous indicators is introduced using a flexible multivariate probit model for the ordinal indicators. A full information approach ensures unbiased estimates for data missing at random. Exceeding the capability of prior methods, up to 13 ordinal variables can be included before integration time increases beyond 1 s per row. The method relies on the axiom of conditional probability to split apart the distribution of continuous and ordinal variables. Due to the symmetry of the axiom, two similar methods are available. A simulation study provides evidence that the two similar approaches offer equal accuracy. A further simulation is used to develop a heuristic to automatically select the most computationally efficient approach. Joint ordinal continuous SEM is implemented in OpenMx, free and open-source software.

Firm Turnover and Inflation Dynamics

This paper examines the role of firm turnover in explaining inflation dynamics. I augment a New-Keynesian DSGE model with endogenous entry and exogenous stochastic exit and estimate with the Bayesian full information approach for the US economy. Results show that shocks to the entry cost explain more than half of the inflation variance at the business cycle frequencies. When it is cheap to create firms, the number of new firms goes up and inflation increases as labour intensive creation of firms pushes up the demand for labour. Only gradually, when the number of firms is high and the number of new firms goes down again, does inflation fall, as stressed by the standard mechanism for an increasing number of firms

Ranking chain sum orders

Ranking information is an important topic in information sciences, Internet searching, voting systems, and sports. In the full information approach, a ranking is a total order of the candidates. We compare two rankings by pairwise comparisons under the nearest neighbor Kendall tau distance and study the distance and rank aggregation problems.In many settings, the information is incomplete and a ranking is given by a partial order. A bucket order is obtained if a ranking allows ties and treats tied candidates as equivalent. Then the distance and rank aggregation problems can be solved efficiently in almost linear time. A chain sum order is complementary to a bucket order and consists of a set of disjoint total orders. Its width and height is the number and the maximum size of the total orders, respectively.We show that the distance and rank aggregation problems of a total order and a chain sum order of bounded width or of height at most two can be solved in polynomial time and are NP-complete for a total and a chain sum order of height at least 12. Both problems remain NP-complete for a total and a heap order which is the partial order obtained from a single-elimination tournament (in sports). However, the problems are fixed-parameter tractable with respect to the distance and the Ulam distance, but are fixed-parameter intractable with respect to the order dimension.

A flexible full-information approach to the modeling of response styles.

We present a flexible full-information approach to modeling multiple user-defined response styles across multiple constructs of interest. The model is based on a novel parameterization of the multidimensional nominal response model that separates estimation of overall item slopes from the scoring functions (indicating the order of categories) for each item and latent trait. This feature allows the definition of response styles to vary across items as well as overall item slopes that vary across items for both substantive and response style dimensions. We compared the model with similar approaches using examples from the smoking initiative of the Patient-Reported Outcomes Measurement Information System. A small set of simulations showed that the estimation approach is able to recover model parameters, factor scores, and reasonable estimates of standard errors. Furthermore, these simulations suggest that failing to include response style factors (when present in the data generating model) has adverse consequences for substantive trait factor score recovery. (PsycINFO Database Record

A New Approach to Handle Missing Covariate Data in Twin Research : With an Application to Educational Achievement Data.

The often-used ACE model which decomposes phenotypic variance into additive genetic (A), common-environmental (C) and unique-environmental (E) parts can be extended to include covariates. Collection of these variables however often leads to a large amount of missing data, for example when self-reports (e.g. questionnaires) are not fully completed. The usual approach to handle missing covariate data in twin research results in reduced power to detect statistical effects, as only phenotypic and covariate data of individual twins with complete data can be used. Here we present a full information approach to handle missing covariate data that makes it possible to use all available data. A simulation study shows that, independent of missingness scenario, number of covariates or amount of missingness, the full information approach is more powerful than the usual approach. To illustrate the new method, we applied it to test scores on a Dutch national school achievement test (Eindtoets Basisonderwijs) in the final grade of primary school of 990 twin pairs. The effects of school-aggregated measures (e.g. school denomination, pedagogical philosophy, school size) and the effect of the sex of a twin on these test scores were tested. None of the covariates had a significant effect on individual differences in test scores.

Supplemental Material for A Flexible Full-Information Approach to the Modeling of Response Styles

Supplemental Material for A Flexible Full-Information Approach to the Modeling of Response Styles

Estimation of the latent mediated effect with ordinal data using the limited-information and Bayesian full-information approaches.

It is common to encounter latent variables with ordinal data in social or behavioral research. Although a mediated effect of latent variables (latent mediated effect, or LME) with ordinal data may appear to be a straightforward combination of LME with continuous data and latent variables with ordinal data, the methodological challenges to combine the two are not trivial. This research covers model structures as complex as LME and formulates both point and interval estimates of LME for ordinal data using the Bayesian full-information approach. We also combine weighted least squares (WLS) estimation with the bias-corrected bootstrapping (BCB; Efron Journal of the American Statistical Association, 82, 171-185, 1987) method or the traditional delta method as the limited-information approach. We evaluated the viability of these different approaches across various conditions through simulation studies, and provide an empirical example to illustrate the approaches. We found that the Bayesian approach with reasonably informative priors is preferred when both point and interval estimates are of interest and the sample size is 200 or above.

Finding physics signals with shower deconstruction

We introduce shower deconstruction, a method to look for new physics in a hadronic environment. The method aims to be a full information approach using small jets. It assigns to each event a number chi that is an estimate of the ratio of the probability for a signal process to produce that event to the probability for a background process to produce that event. The analytic functions we derive to calculate these probabilities mimic what full event generators like Pythia or Herwig do and can be depicted in a diagrammatic way. As an example, we apply this method to a boosted Higgs boson produced in association with a Z-boson and show that this method can be useful to discriminate this signal from the Z+jets background.

Uninformed citizens and support for free trade

ABSTRACT In less developed countries, the higher individuals’ labor skills are, the more they support free trade. This recurrent statistical finding is problematic for extant analytical models used to explain and interpret people's positions on free trade. This article proposes an analytical framework that challenges the dominant full-information factor-endowment approach to public opinion on free trade. The dominant approach assumes informed individuals who relate expectations about how free trade will affect them to their work skills. We propose that most individuals lack information and that their positions reflect the influence of information, frames, economic vulnerability, and political endorsements. We test this alternative approach with a Spanish survey conducted in May 2009 and the ISSP survey conducted in 2003 in a large number of less developed and more developed countries. The Spanish data demonstrate that the population is largely uninformed and that their ideas about the consequences of free trade do not explain contrasts among different socio-demographic groups. Meanwhile, the ISSP data contradict important aspects of the traditional approach and are consistent our alternative approach.

Health care utilization and self-assessed health: specification of bivariate models using copulas

The discernment of relevant factors driving health care utilization constitutes one important research topic in health economics. This issue is frequently addressed through specification of regression models for health care use (y—often measured by number of doctor visits) including, among other covariates, a measure of self-assessed health (sah). However, the exogeneity of sah within those models has been questioned, due to the possible presence of unobservables influencing both y and sah, and because individuals’ health assessments may depend on the quantity of medical care received. This article addresses the possible simultaneity of (sah,y) by adopting a full information approach, through specification of the bivariate probability function (p.f.) of these discrete variables, conditional on a set of exogenous covariates (x). The approach is implemented with copula functions, which afford separate consideration of each variable margin and their dependence structure. The specification of the joint p.f. of (sah,y) enables estimation of several quantities of potential economic interest, namely features of the conditional p.f. of y given sah and x. The adopted models are estimated through maximum likelihood, with cross-sectional data from the Portuguese National Health Survey of 1998–1999. Estimates of the margins’ parameters do not vary much among different copula models, while, in accordance with theoretical expectations, the dependence parameter is estimated to be negative across the various joint models.

Pricing for water conservation with cost recovery

Variable unit pricing provides incentives for conservation, similar to increasing block rates, while covering system costs. For supply‐limited situations and planning contexts, it is also shown to provide for cost recovery with supply limits and capacity constraints. This paper presents two applications of pricing with supply constraints: (1) an application for Amman, Jordan, showing that VUP satisfying a supply limit can avoid more costly capacity expansion and (2) an example of seasonal pricing for Boulder, Colorado, as an alternative to water restrictions. These two cases also exhibit two methods for generating data needs: (1) a “full information approach” with specified demand and cost functions and (2) “iterative pricing” that uses a previous year's cost and consumption data to set future pricing.

OPTIMAL PORTFOLIO CONSTRUCTION UNDER PARTIAL INFORMATION FOR A BALANCED FUND

The model parameters in optimal asset allocation problems are often assumed to be deterministic. This is not a realistic assumption since most parameters are not known exactly and therefore have to be estimated. We consider investment opportunities which are modeled as local geometric Brownian motions whose drift terms may be stochastic and not necessarily measurable. The drift terms of the risky assets are assumed to be affine functions of some arbitrary factors. These factors themselves may be stochastic processes. They are modeled to have a mean-reverting behavior. We consider two types of factors, namely observable and unobservable ones. The closed-form solution of the general problem is derived. The investor is assumed to have either constant relative risk aversion (CRRA) or constant absolute risk aversion (CARA). The optimal asset allocation under partial information is derived by transforming the problem into a full-information problem, where the solution is well known. The analytical result is empirically tested in a real-world application. In our case, we consider the optimal management of a balanced fund mandate. The unobservable risk factors are estimated with a Kalman filter. We compare the results of the partial-information strategy with the corresponding full-information strategy. We find that using a partial-information approach yields much better results in terms of Sharpe ratios than the full-information approach.

Explaining co-movements between stock markets: The case of US and Germany

We explain co-movements between stock markets by explicitly considering the distinction between interdependence and contagion. We propose and implement a full-information approach on data for US and Germany to provide answers to the following questions: (i) Is there long-term interdependence between US and German stock markets? (ii) Is there short-term interdependence and contagion between US and German stock markets, i.e. do short-term fluctuations of the US share prices spill over to German share prices and is such co-movement unstable over high-volatility episodes? Our answers are, respectively, no to the first question and yes to the second one.

FEEDFORWARD CONTROL FOR DESCRIPTOR SYSTEM WITH UNMEASURABLE DISTURBANCE

This paper develops a design of feedforward control for descriptor system with unmeasurable disturbance which are either nonregular or if it is regular, has impulsive mode. To solve this problem, we will transform descriptor system into regular state space system using the output feedback. First, fault detection and isolation(FDI) approach using sliding mode observer is applied for estimating unmeasurable disturbance. Secondly, with the approximation of disturbance, the feedforward controller which is composed of both tracking the reference model and rejecting an influence is designed by solving a full-information approach for augmented system with a nominal control constraint.

Feedforward control: a full-information approach

The Youla parametrization of two-degree-of-freedom controllers reveals that the design of the prefilter and the design of the regulator can be done independently. In this paper, a unified framework is proposed to address the design of the feedforward part of a two-degree-of-freedom controller. The approach deals both with tracking of a causal reference signal and with rejection of a causal measurable disturbance. It is shown that the determination of a feedforward filter, with a constraint on the nominal control effort, reduces to the solution of a particular full-information problem. The approach is attractive because it leads to a significant reduction in the number of decision variables in the design process. The method also extends naturally to plants in a polytope. Its effectiveness is demonstrated on two numerical examples. In the first example, the design of a linear time-invariant feedforward controller is carried out to reject a measurable disturbance and to follow a reference signal; while in the second example, we address the design of a robust gain scheduled two-degree-of-freedom controller.

The dynamics of spatial pollution: The case of phosphorus runoff from agricultural land

Abstract The paper analyzes, based on a land classification system, the optimal management of negative production externalities while taking into account the intertemporal and spatial aspects of the problem. To incorporate both aspects simultaneously, a two-stage modeling approach is proposed where the solution of the spatial problem (first stage) is optimized over time (second stage). As a result, it is possible to relate long-run and short-run supply and input demand functions. Attention is given to runoffs from agricultural land leading to the contamination of a water body, in particular, the one of phosphorus and the eutrophication of lakes. The employment of a land classification system supports a full-information approach and allows to address the optimal management of mineral fertilizer and manure based on zonal taxes, zonal permits, and zonal standards which all vary over time.

Full-Information Industry Betas

Paul D. Kaplan is Vice President and Chief Economist at Ibbotson Associates. James D. Peterson is Vice President at Charles Schwab and Company. The process of estimating an industry cost of capital is complicated by the fact that many firms operate in multiple industries. Conglomerates are typically excluded from a pure-play industry analysis since their operations span more than one line of business. We argue that excluding these firms introduces and upward bias into industry beta estimates. This is because the betas of large-market-capitalization firms tend to be lower than the betas of small-capitalization firms. Betas are estimated for 66 industries using both a pure-play and a full-information approach. The full-information approach incorporates industry-specific information contained in the betas of conglomerates into the industry beta estimation process. Our results show that full-information beta estimates are substantially smaller than their pure-play counterparts.