Primary Surplus Behavior and Risks to Fiscal Sustainability in Emerging Market Countries: A "Fan-Chart" Approach

This paper proposes a probabilistic approach to public debt sustainability analy-sis (DSA) using "fan charts." These depict the magnitude of risks-upside and downside-surrounding public debt projections as a result of uncertain economic conditions and policies. We propose a simulation algorithm for the path of public debt under realistic shock configurations, combining pure economic disturbances (to growth, interest rates, and exchange rates), the endogenous policy response to these, and the possible shocks arising from fiscal policy itself. The paper empha-sizes the role of fiscal behavior, as well as the structure of disturbances facing the economy and due to fiscal policy, in shaping the risk profile of public debt. Fan charts for debt are derived from the "marriage" between the pattern of shocks on the one hand and the endogenous response of fiscal policy on the other. Applications to Argentina, Brazil, Mexico, South Africa, and Turkey are used to illustrate the approach and its limitations. Copyright 2006, International Monetary Fund

Comparisons of Neural Networks, Shewhart ‾x, and CUSUM Control Charts Under the Condition of Nonnormality

In this study, neural networks are developed under conditions of nonnormality as alternatives to standard control charts, and their performance is compared with those of standard ‾x and CUSUM control charts

Process Capability Calculations with Nonnormal Data in the Medical Device Manufacturing Industry

U.S. Food and Drug Administration (FDA) recalls of medical devices are at historically high levels despite efforts by manufacturers to meet stringent agency requirements to ensure quality and patient safety. A factor in the release of potentially dangerous devices might be the interpretations of nonnormal test data by statistically unsophisticated engineers. The purpose of this study was to test the hypothesis that testing by lot provides a better indicator of true process behavior than process capability indices (PCIs) calculated from the mixed lots that often occur in a typical production situation. The foundations of this research were in the prior work of Bertalanffy, Kane, Shewhart, and Taylor. The research questions examined whether lot traceability allows the decomposition of the combination distribution to allow more accurate calculations of PCIs used to monitor medical device production. The study was semiexperimental, using simulated data. While the simulated data were random, the study was a quasiexperimental design because of the control of the simulated data through parameter selection. The results of this study indicate that decomposition does not increase the accuracy of the PCI. The conclusion is that a systems approach using the PCI, additional statistical tools, and expert knowledge could yield more accurate results than could decomposition alone. More accurate results could ensure the production of safer medical devices by correctly identifying noncapable processes (i.e., processes that may not produce required results), while also preventing needless waste of resources and delays in potentially life-savings technology, reaching patients in cases where processes evaluate as noncapable when they are actually capable

New Single Variables Control Charts Based On The Double Ewma Statistics

In Statistical Process Control (SPC) monitoring situations, there is a tendency for both the process mean and process variability to shift simultaneously. Traditionally, two separate control charts, each for the mean and variance are used concurrently to monitor the process mean and process variance. However, in many real life process monitoring situations, a simultaneous control of the process mean and process variance is necessary. This has motivated us to develop single DEWMA (called Double Exponentially Weighted Moving Average) charts which are capable of monitoring simultaneous shifts in both the process mean and process variance, when the underlying distribution of the process is normal. The DEWMA statistics are based on the approach of performing exponential smoothing twice on the original statistics of the underlying process. The objective of this study is to propose three single DEWMA charts, namely the DEWMA-Max (called the DEWMA maximum), Max-DEWMA (called the maximum DEWMA) and SS-DEWMA (called the sum of squares of DEWMA) charts

New Single Variables Control Charts Based On The Double Ewma Statistics

In Statistical Process Control (SPC) monitoring situations, there is a tendency for both the process mean and process variability to shift simultaneously. Traditionally, two separate control charts, each for the mean and variance are used concurrently to monitor the process mean and process variance. However, in many real life process monitoring situations, a simultaneous control of the process mean and process variance is necessary. This has motivated us to develop single DEWMA (called Double Exponentially Weighted Moving Average) charts which are capable of monitoring simultaneous shifts in both the process mean and process variance, when the underlying distribution of the process is normal. The DEWMA statistics are based on the approach of performing exponential smoothing twice on the original statistics of the underlying process. The objective of this study is to propose three single DEWMA charts, namely the DEWMA-Max (called the DEWMA maximum), Max-DEWMA (called the maximum DEWMA) and SS-DEWMA (called the sum of squares of DEWMA) charts

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 192

This bibliography lists 247 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1979