Consistent estimation in the bilinear multivariate errors-in-variables model

A bilinear multivariate errors-in-variables model is considered. It corresponds to an overdetermined set of linear equations AXB=C, A∈ℝ^m×n, B∈ℝ^p×q, in which the data A, B, C are perturbed by errors. The total least squares estimator is inconsistent in this case.  An adjusted least squares estimator is constructed, which converges to the true value X, as m →∞, q →∞. A small sample modification of the estimator is presented, which is more stable for small m and q and is asymptotically equivalent to the adjusted least squares estimator. The theoretical results are confirmed by a simulation study. Acknowledgements. We thank two anonymous reviewers for their suggestions and corrections.? A. Kukush is supported by a postdoctoral research fellowship of the Belgian office for Scientific, Technical and Cultural Affairs, promoting Scientific and Technical Collaboration with Central and Eastern Europe.? S. Van Huffel is a full professor with the Katholieke Universiteit Leuven.? I. Markovsky is a research assistant with the Katholieke Universiteit Leuven.? This paper presents research results of the Belgian Programme on Interuniversity Poles of Attraction (IUAP V-22), initiated by the Belgian State, Prime Minister's Office – Federal Office for Scientific, Technical and Cultural Affairs of the Concerted Research Action (GOA) projects of the Flemish Government MEFISTO-666 (Mathematical Engineering for Information and Communication Systems Technology), of the IDO/99/03 project (K.U. Leuven) “Predictive computer models for medical classification problems using patient data and expert knowledge”, of the FWO projects G.0078.01, G.0200.00, and G0.0270.02.? The scientific responsibility is assumed by its authors.     

Inferential Causal Models: Integrating Quality & Quantity

In-depth data analysis plus statistical modeling can produce inferentialcausal models. Their creation thus combines aspects of analysis by close inspection,that is, reason analysis and cross-tabular analysis, with statistical analysis procedures,especially those that are special cases of the generalized linear model (McCullaghand Nelder, 1989; Agresti, 1996; Lindsey, 1997). This paper explores some of the roots of this combined method and suggests some new directions. An exercise clarifies some limitations of classic reason analysis by showing how the cross tabulation of variables with controls for test factors may produce better inferences. Then, given the cross tabulation of several variables, by explicating Coleman effect parameters, logistic regressions, and Poisson log-linear models, it shows how generalized linear models provide appropriate measures of effects and tests of statistical significance. Finally, to address a weakness of reason analysis, a case-control design is proposed and an example is developed.     

COMPUTABLE GENERAL EQUILIBRIUM MODELS FOR DEVELOPMENT POLICY ANALYSIS IN LDCs

Abstract. The use of CGE models has gained much popularity among policy analysts in LDCs and there is a fast growing body of literature on this area of economics. In this survey, the advantages of general equilibrium approach over partial equilibrium approach in analysing a wide range of policy issues are highlighted. The evolution of CGE modelling is discussed and more than 60 CGE applications related to different policy issues in LDCs are surveyed. This comprehensive survey shows that the CGE models have become quite popular analytical tools among policy analysts in LDCs over the last decade or so. The debate in the economic profession regarding the value and appropriateness of using CGE models for policy analysis is examined in the final section of this paper. Some of the criticisms levelled at CGE models are discussed and it is argued that despite this criticism such models are capable of providing insight into important policy problems.     

Bayesian MCMC Mapping of Quantitative Trait Loci in a Half-sib Design: a Graphical Model Perspective

Graphical models provide a powerful and flexible approach to the analysis of complex problems in genetics. While task-specific software may be extremely efficient for any particular analysis, it is often difficult to adapt to new computational challenges. By viewing these genetic applications in a more general framework, many problems can be handled by essentially the same software. This is advantageous in an area where fast methodological development is essential. Once a method has been fully developed and tested, problem-specific software may then be required. The aim of this paper is to illustrate the potential use of a graphical model approach to genetic analyses by taking a very simple and well-understood problem by way of example.     

IntraLATA long-distance demand: carrier choice, usage demand and price elasticities

Competition in the long-distance market in the US continues to intensify; the 1996 Telecommunications Act has led to increased competition in long-distance telephony especially as the Regional Bell Operating Companies have begun to gain entry to long-haul, long-distance markets. In order to better understand the implications of having increased service offerings, models of how customers choose between carriers (and the impact of this choice on subsequent usage) will be useful. We develop the first publicly available models that simultaneously estimate choice and usage for intraLATA long-distance in the US. Utilizing a generalized Tobit model, the price responsiveness of usage and carrier choice are estimated. The results are generally consistent with expectations both in terms of theory and of practical experience in the industry.     

Environmental protection versus economic growth

This paper presents and analyses the differences in the eco-models implemented worldwide （such as whether and how carbon taxes being ＂recycled＂）, or in their efficiency parameters （inconsistent parameter values that account for different results）. This is the assumption that a real tradeoff exists between the production of environmental goods. The present article empirically proves that something must be given up in order to gain something else, and once equations are specified to trace out the path of the economy over time, the natural economic formulation of such equations will embody the notion of economic and bio-tradeoffs.     

The adequacy of asymptotic approximations in the near-integrated autoregressive model with dependent errors

We consider the normalized least squares estimator of the parameter in a nearly integrated first-order autoregressive model with dependent errors. In a first step we consider its asymptotic distribution as well as asymptotic expansion up to order O_p(T⁻¹). We derive a limiting moment generating function which enables us to calculate various distributional quantities by numerical integration. A simulation study is performed to assess the adequacy of the asymptotic distribution when the errors are correlated. We focus our attention on two leading cases: MA(1) errors and AR(1) errors. The asymptotic approximations are shown to be inadequate as the MA root gets close to −1 and as the AR root approaches either −1 or 1. Our theoretical analysis helps to explain and understand the simulation results of Schwert (1989) and DeJong, Nankervis, Savin, and Whiteman (1992) concerning the size and power of Phillips and Perron's (1988) unit root test. A companion paper, Nabeya and Perron (1994), presents alternative asymptotic frameworks in the cases where the usual asymptotic distribution fails to provide an adequate approximation to the finite-sample distribution.     

Analysing Perceived Downside Risk: the Component Value‐at‐Risk Framework

Multinational companies face increasing risks arising from external risk factors, e.g. exchange rates, interest rates and commodity prices, which they have learned to hedge using derivatives. However, despite increasing disclosure requirements, a firm's net risk profile may not be transparent to shareholders. We develop the ‘Component Value‐at‐Risk (VaR)’ framework for companies to identify the multi‐dimensional downside risk profile as perceived by shareholders. This framework allows for decomposing downside risk into components that are attributable to each of the underlying risk factors. The firm can compare this perceived VaR, including its composition and dynamics, to an internal VaR based on net exposures as it is known to the company. Any differences may lead to surprises at times of earnings announcements and thus constitute a litigation threat to the firm. It may reduce this information asymmetry through targeted communication efforts.     

Use of Statistics in the Monetary Policy of the Czech National Bank: The Case of a Country in Transition

In the presented text the authors judge the importance of statistics in the monetary policy of the Czech National Bank (CNB) over the course of the economic transformation process, with particular consideration of changing statistical needs and the possibilities and limits of statistical data exploitation in the monetary analyses. The importance of statistics lies on the level of collection and processing of statistical information and on the level of use of statistical methods to analyse data. Since the start of the 1990s the requirements for statistics were significantly influenced by monetary policy. In the period 1990–1997, monetary targeting was the primary influential factor. Since 1998, the monetary policy is influenced by inflation targeting. Statistical priorities switched from monetary data to economy and financial market data. Much progress has been made in the use of statistical methods for analysing data. Statistics available at present cover the CNB's standard monetary-policy requirements and are on par with those in developed countries. Its further development will reflect the standard changes taking place in the more advanced countries.