Adaptive Sequential Posterior Simulators for Massively Parallel Computing Environments

The three general approaches to empirical work in economics are structural, experimentalist, and descriptive. This paper provides an overview of how empirical work in regional economics fits into these three categories. In particular, I examine a single issue in the field, the nature of agglomeration benefits and the productivity gains from agglomeration, and analyze the advantages and drawbacks of following each of these three empirical approaches. I also discuss potentially fruitful ways empirical work in regional economics might advance.

The linear regression model is widely used in empirical work in Economics, Statistics, and many other disciplines. Researchers often include many covariates in their linear model specification in an attempt to control for confounders. We give inference methods that allow for many covariates and heteroskedasticity. Our results are obtained using high-dimensional approximations, where the number of included covariates are allowed to grow as fast as the sample size. We find that all of the usual versions of Eicker-White heteroskedasticity consistent standard error estimators for linear models are inconsistent under this asymptotics. We then propose a new heteroskedasticity consistent standard error formula that is fully automatic and robust to both (conditional) heteroskedasticity of unknown form and the inclusion of possibly many covariates. We apply our findings to three settings: parametric linear models with many covariates, linear panel models with many fixed effects, and semiparametric semi-linear models with many technical regressors. Simulation evidence consistent with our theoretical results is also provided. The proposed methods are also illustrated with an empirical application.

ABSTRACTThe linear regression model is widely used in empirical work in economics, statistics, and many other disciplines. Researchers often include many covariates in their linear model specification in an attempt to control for confounders. We give inference methods that allow for many covariates and heteroscedasticity. Our results are obtained using high-dimensional approximations, where the number of included covariates is allowed to grow as fast as the sample size. We find that all of the usual versions of Eicker–White heteroscedasticity consistent standard error estimators for linear models are inconsistent under this asymptotics. We then propose a new heteroscedasticity consistent standard error formula that is fully automatic and robust to both (conditional) heteroscedasticity of unknown form and the inclusion of possibly many covariates. We apply our findings to three settings: parametric linear models with many covariates, linear panel models with many fixed effects, and semiparametric semi-linear models with many technical regressors. Simulation evidence consistent with our theoretical results is provided, and the proposed methods are also illustrated with an empirical application. Supplementary materials for this article are available online.

Historians have long warned of the perils of accepting various classifications in social statistics as delivering up unproblematic facts. They have examined how census classifications such as “household” and “race” have been subject to ideological manipulation, or have been developed in an environment of bureaucratic infighting or indecision. Many others – philosophers, literary historians, sociologists, anthropologists, political scientists, accountants, and economists – have also examined the contingent nature of various categories of social statistics as measures of difference.2 This essay offers additional thoughts on how economists might explore questions, epistemological and otherwise, about the “facts” of “difference” in social statistics. By “difference” I mean the outcome of the banal exercise of discrimination that distinguishes an object as something and not something else in social statistics, an exercise vital to those who do theoretical and empirical work in economics. I also mean the perspective that holds social categories as “being revealed by statistics” (Desrosieres 1998: 238), which highlights the meanings, moral and aesthetic, one imparts to difference. This essay is about classification, but it is also about facts and interpretations. Taxonomies supply stories about facts. Different groups – social scientists, politicians, bureaucrats, and the public – have struggled to define and value the facts and categories of social life, and their struggles entail social and political consequences.

Fully sequential ranking-and-selection (R&S) procedures to find the best from a finite set of simulated alternatives are often designed to be implemented on a single processor. However, parallel computing environments, such as multi-core personal computers and many-core servers, are becoming ubiquitous and easily accessible for ordinary users. In this paper, we propose two types of fully sequential procedures that can be used in parallel computing environments. We call them vector-filling procedures and asymptotic parallel selection procedures, respectively. Extensive numerical experiments show that the proposed procedures can take advantage of multiple parallel processors and solve large-scale R&S problems.

Nowadays, research and development for optimal setting control (OSC) of complex industrial processes is becoming a hot topic. However, there is no configurable software for OSC design. In this paper, a novel software platform is proposed. The major superiority is that it not only integrates intelligent and data-driven mainstream algorithms within a unified framework, but also provides a powerful controller design tool and employs a Petri net engine to schedule control units in a parallel computation environment. The overall framework and key technologies are introduced in detail. The platform is verified and validated through a case of application to the design and development of OSC system of grinding process in a hardware-in-the-loop simulation environment.

In this rigorous and well-crafted work, Kenneth Wolpin examines the role of theory in inferential empirical work in economics and the social sciences in general--that is, any research that uses raw data to go beyond the mere statement of fact or the tabulation of statistics. He considers in particular the limits that eschewing the use of theory places on inference. Wolpin finds that the absence of theory in inferential work that addresses microeconomic issues is pervasive. That theory is unnecessary for inference is exemplified by the expression Olet the data speak for themselves.O This approach is often called Oreduced form.O A more nuanced view is based on the use of experiments or quasi-experiments to draw inferences. Atheoretical approaches stand in contrast to what is known as the structuralist approach, which requires that a researcher specify an explicit model of economic behavior--that is, a theory. Wolpin offers a rigorous examination of both structuralist and nonstructuralist approaches. He first considers ex ante policy evaluation, highlighting the role of theory in the implementation of parametric and nonparametric estimation strategies. He illustrates these strategies with two examples, a wage tax and a school attendance subsidy, and summarizes the results from applications. He then presents a number of examples that illustrate the limits of inference without theory: the effect of unemployment benefits on unemployment duration; the effect of public welfare on womenOs labor market and demographic outcomes; the effect of school attainment on earnings; and a famous field experiment in education dealing with class size. Placing each example within the context of the broader literature, he contrasts them to recent work that relies on theory for inference.

Chapter 50 - Security Considerations in the Distributed Parallel Computation Environment

List of Boxes. Preface. 1. Introduction. 1.1 What This Book Provides. 1.2 Looking around Corners. 1.3 Ideological Stance. 1.4 Economics Embedded in Philosophy and Sociology. 1.5 My Own Biases. 1.6 What Is to Come?. Part I: The Subject of Economics:. 2. What Economists Do. 2.1 What Economics Encompasses. 2.2 How Economists Work. 2.3 Some Characteristics of Economics. Appendix 2.1: Unrealistic Assumptions a Good Theory Make. Appendix 2.2: Is the Stock Market Efficient?. 3. How Well Do They Do It?. 3.1 Forecasting. 3.2 Choosing the Right Policies: The Problem of Value Judgments. 3.3 Implementing Economic Policies. 3.4 Biases. 3.5 Disagreement among Economists. Appendix 3.1: A Survey of American Academic Economists' Opinions. Part II: Some Infrastructure:. 4. Beguiling Words. 4.1 Some Widely Used Terms and Phrases. 4.2 Some Terms and Phrases Relating to Economics and Politics. 5. Important Economic Ideas: Often Misunderstood. 5.1 Levels versus Rates of Change. 5.2 Stocks versus Flows. 5.3 Real versus Nominal. 5.4 Future versus Current Dollars. 5.5 The Price Level versus Relative Prices. 5.6 Stable Prices versus Low Prices. 5.7 Gross versus Net and Single versus Double Counting. 5.8 Opportunity Costs versus Monetary Costs. 5.9 Cost--Benefit Analysis. 5.10 Wage Rates versus Labor Costs. 5.11 Capital Goods versus Human Capital. 5.12 Marginal versus Average Utility. 5.13 Marginal versus Average Costs. 5.14 Big Business versus Market Power. 5.15 Pro-market versus Pro-business. 6.16 and Can versus Will. 5.17 Comparative versus Absolute Advantage. Part III: Economic Theory and Policy:. 6. The Crown Jewel of Economics: The Price Mechanism. 6.1 An Overview of the Price Mechanism. 6.2 Equilibrium. 6.3 Shortages. 6.4 A Digression: The Equilibrium of the Firm. 6.5 What Happens in the Absence of a Price Mechanism?. 6.6 The Dark Side of the Price Mechanism. 6.7 The Upshot. Appendix 6.1: More on Supply and Demand. 7. Risk Taking and Incentives. 7.1 Risk Bearing and Entrepreneurship. 7.2 Incentives. 8. Looking around Corners. 8.1 Do Easier Bankruptcy Laws Help the Poor?. 8.2 Should Artists Receive a Share of the Capital Gain When Their Work Is Resold?. 8.3 Do We Want Fewer People Exploiting the Poor?. 8.4 Cut the Gas Tax to Slow the Rise of Soaring Gas Prices?. 8.5 A Costless Way to Alleviate Destitution in Third World Countries?. 8.6 Ban or Fine All Speculators?. 8.7 Is Limiting Tobacco Advertising an Effective Policy?. 8.8 Would Importing Pharmaceuticals from Canada Substantially Lower Drug Prices?. 8.9 An Efficient and Humane Way to Reduce Cocaine Production?. 8.10 Protecting Lives by Requiring Use of Seatbelts?. 8.11 Do You Really Want Your Bid to Win?. 8.12 Is Fast Chinese Growth Bad for the United States?. 8.13 In Emergencies Should Prices Ration Supply, Thus Letting the Rich Outbid the Poor?. 8.14 Is Gaining from Someone's Misfortune a Moral Wrong?. 8.15 Should Those Close at Hand Have a Special Responsibility to Alleviate Misery?. 8.16 Seven Short Ones. 8.17 On the Other Hand. 9. Natural Resources and Environmental Economics. 9.1 Running Out of Privately Owned Natural Resources. 9.2 The Tragedy of the Commons. 9.3 Pollution Permits and Taxes on Pollutants. 9.4 Tail-pipe Pollution. 9.5 Protecting Endangered Species. Part IV: Looking at Data:. 10. Empirical Economics. 10.1 Role of Empirical Work in Economics. 10.2 Types of Empirical Evidence. 10.3 What to Watch Out for in Statistics: Some General Problems. Appendix 10.1: Survey Data. Appendix 10.2: Measuring GDP, Savings Rates, Price Indexes, Employment, and Poverty. 11. Some Simple (?) Ways of Presenting Data: Percentages, Figures, and Graphs. 11.1 Percentages. 11.2 Figures and graphs. 12. Samples and Their Problems. 12.1 Selecting a Representative Sample. 12.2 Watching for Inappropriate Samples. 12.3 The Regression Fallacy: Building a Bias into the Sample. 12.4 Sample Size and Coincidence. 12.5 Sampling Error, Confidence Intervals, and Significance. 12.6 Some Warnings about Significance Tests. Appendix 12.1: The Normal Distribution. 13. Regressions: The Workhorse of Empirical Economics. 13.1 The Regression Coefficient. 13.2 How Well Does the Regression Fit the Data?. 13.3 What Should You Look at in a Regression?. 13.4 A Summing Up. 13.5 Looking the Workhorse in the Mouth. 13.6 In Conclusion. 14. The Workhorse in Action: Some Examples of Empirical Economics. 14.1 Consumption and Saving. 14.2 Should You Choose a Large Mutual Fund?. 14.3 The Slave Trade and African Economic Development. 14.4 The Effect of Tax Changes on Real GDP. Epilogue. Index

PurposeIn a recent paper, Yoon and Lee (2019) (YL hereafter) propose a weighted Fama and MacBeth (FMB hereafter) two-step panel regression procedure and provide evidence that their weighted FMB procedure produces more efficient coefficient estimators than the usual unweighted FMB procedure. The purpose of this study is to supplement and improve their weighted FMB procedure, as they provide neither asymptotic results (i.e. consistency and asymptotic distribution) nor evidence on how close their standard error estimator is to the true standard error.Design/methodology/approachFirst, asymptotic results for the weighted FMB coefficient estimator are provided. Second, a finite-sample-adjusted standard error estimator is provided. Finally, the performance of the adjusted standard error estimator compared to the true standard error is assessed.FindingsIt is found that the standard error estimator proposed by Yoon and Lee (2019) is asymptotically consistent, although the finite-sample-adjusted standard error estimator proposed in this study works better and helps to reduce bias. The findings of Yoon and Lee (2019) are confirmed even when the average R2 over time is very small with about 1% or 0.1%.Originality/valueThe findings of this study strongly suggest that the weighted FMB regression procedure, in particular the finite-sample-adjusted procedure proposed here, is a computationally simple but more powerful alternative to the usual unweighted FMB procedure. In addition, to the best of the authors’ knowledge, this is the first study that presents a formal proof of the asymptotic distribution for the FMB coefficient estimator.

In this chapter, we give more detailed review of on-going research activities in virtual computing environment. The projects to be reviewed are not by no means a comprehensive list, but can be considered as a representative of the main project in this area.

A numerical method is developed for solving parabolic partial differential equations with integral boundary conditions. The method is moderately sixth-order accurate due to merging of sixth order finite difference scheme and fifth order Pade’s approximation. Simpson’s 1/3 rule is used to approximate integral conditions. The method does not involve the use of complex arithmetic and optimizes the results. It is observed that this numerical method can be easily coded on serial as well as parallel computers.

We describe total congestion 1 embeddings of complete binary trees into three dimensional grids with expansion ratios 1.172 and 1.25. That is, we give a one-to-one embedding of any complete binary tree into a hexahedron shaped grid such that: the number of nodes in the grid is at most 1.172 (1.25) times the number of nodes in the tree; and no tree nodes or edges occupy the same grid positions. The first strategy embeds trees into cube shaped 3D grids. That is, 3D grids in which all dimensions are roughly equal in size. The second strategy embeds trees into flat 3D grid shapes. That is, it maps complete binary trees into 3D grids with a fixed, small number of layers. An application suggests which embedding to use. For simulations in parallel computer environments, or possibly graph drawing, a cube shaped 3D grid is appropriate. For the sake of VLSI, or other graph drawing applications, embeddings with a small number of layers are better.

During the development and parametrization of 2D image-processing algorithms for surface inspection uses, you need to test a huge amount of image-data for each modification of the algorithms or parameters. For algorithm runtimes up to several seconds, this will take a long time. To speed up this process it is recommended to distribute the computation in a parallel computation environment. Compute Grids, which use the unused resources of existing hardware are the most cost efficient way to solve this problem. The most existing Grid-Concepts are based on flat connection structures with a scheduler on the top; for high job-rates the scheduler becomes the bottleneck of the whole system. Concepts to solve this problem organize the nodes in tree-structures to discharge the central scheduler. In heterogeneous Desktop-Grids where the different nodes are widely distributed the usually used random arrangement of the nodes in the tree-structure can be counterproductive, because the bandwidthes and latencies in a Grid can be varying. In this paper we will show a solution to arrange the nodes of the grid optimized by bandwidth and latency, using modified spanning-tree algorithms, so that the average response time is reduced and in result of this the job-throughput of the Compute-Grid is increased.

Neurons are highly interconnected with each other and are communicating via sending and receiving electrochemical signals, thus composing sophisticated network of interconnected and communicating neurons. This paper discuss the structure of the neural network function approximator and the time series forecasting with neural network, the results could help us to obtain the optimal solutions to higher complexity of the problem.