Physician location and distribution: a social systems approach

A social systems model of the health services system serving the state of Indiana is presented. The model specifies the causal relationships hypothesized as existing among a set of social, demographic and economic variables known to be related to the supply of health manpower and facilities. Inclusion of feedback into the model as well as lagged values of physician supply variables permits the examination of the dynamic behavior of the social system over time. Estimates of the model parameters are based on data obtained from the U.S. census, the American Medical Association's physician distribution series and the annual guide issue of Hospitals, the journal of the American Hospital Association.Methods for deriving the reduced form and the final form of the structural model are presented along with the reduced and final form equations. These equations provide valuable information for policy decisions regarding the likely consequences of changes in the structure of the population and in the supply of health manpower and facilities. The structural and reduced forms of the model have been used to examine the likely consequences of several proposed policies that would affect the delivery of health services in the state of Indiana.     

Structural evolution of the postwar U.S. economy

We consider a time-varying parameter vector autoregressive model with stochastic volatility and mixture innovations to study the empirical relevance of the Lucas critique for the postwar U.S. economy. The model allows blocks of parameters to change at endogenously estimated points of time. Contrary to the Lucas critique, there are large changes at certain points of time in the parameters associated with monetary policy that do not correspond to changes in “reduced-form” parameters for inflation or the unemployment rate. However, the structure of the U.S. economy has evolved considerably over the postwar period, with an apparent reduction in the late 1980s in the impact of monetary policy shocks on inflation, though not on the unemployment rate. Related, we find changes in the Phillips curve tradeoff between inflation and cyclical unemployment (measured as the deviation from the time-varying steady-state unemployment rate implied by the model) in the 1970s and especially since the mid-1990s.     

Social Security and the Retirement and Savings Behavior of Low Income Households

In this paper, we develop and estimate a model of retirement and savings incorporating limited borrowing, stochastic wage offers, health status and survival, social security benefits, Medicare and employer-provided health insurance coverage, and intentional bequests. The model is estimated on a sample of relatively poor households from the first three waves of the Health and Retirement Study (HRS), for whom we would expect social security income to be of particular importance. The estimated model is used to simulate the responses to changes in social security rules, including changes in benefit levels, in the payroll tax, in the social security earnings tax and in early and normal retirement ages. Welfare and budget consequences are estimated.     

Achieving runtime adaptability through automated model evolution and variant selection

Dynamically adaptive systems propose adaptation by means of variants that are specified in the system model at design time and allow for a fixed set of different runtime configurations. However, in a dynamic environment, unanticipated changes may result in the inability of the system to meet its quality requirements. To allow the system to react to these changes, this article proposes a solution for automatically evolving the system model by integrating new variants and periodically validating the existing ones based on updated quality parameters. To illustrate this approach, the article presents a BPEL-based framework using a service composition model to represent the functional requirements of the system. The framework estimates quality of service (QoS) values based on information provided by a monitoring mechanism, ensuring that changes in QoS are reflected in the system model. The article shows how the evolved model can be used at runtime to increase the system's autonomic capabilities and delivered QoS.     

Utilization of ambulatory care studies: testing the assumptions

A linear regression procedure is usually used to estimate the effect of a set of predictors on utilization of ambulatory health care. The implicit assumptions embedded in the linear regression model have never been examined. Here, with utilization data of a sample of 48292 patients from the file of the Québec National Health Plan, four implicit hypotheses embedded in the linear regression model are tested: (1) the transition from the state of utilization to the state of no utilization, and vice-versa, depends on the level of the transition rates, (2) the effect of independent variables depends on the transitions being predicted from or to the state of utilization, (3) the transition is time dependent, and (4) the system of transitions from one state to another is not at equilibrium. The analysis shows that the first three hypotheses cannot be rejected. Thus, the use of the familiar linear regression procedure in this study to estimate the effect of a set of factors on utilization would have yielded biased estimates.     

Markov models and the utilization of mental health services: a study of endogenously depressed patients.

Patients in the mental health care system typically make more or less irregularly spaced visits to psychiatrists, both within and between episodes of a given illness. A Markov model is constructed which can predict the utilization of psychiatric services for such patients. Unlike previous Markov models of utilization, the current model takes as its starting point a model of an actual disease, specifically, endogenous depression. It is shown how one can estimate the parameters of both the model of utilization and the model of depression using data which were collected for clinical research purposes. The models provide reasonable fits to the data. Several applications of the models are worked out. In addition to predicting the utilization of mental health services, the models can be used to predict incidence, prevalence and recovery rates and to predict the changes in utilization which parallel changes in treatment regimens.     

A proposed method for the analysis of public health policy in less developed countries

The study of public health policy in the less developed countries (LDCs) is handicapped by both lack of adequate data and gaps in conceptual apparatus. A new conceptual model is proposed that would be used for future policy analyses to assess what changes in health status of population in LDCs can be expected as direct functions of increased health service resources, and of improved environmental and econosociocultural conditions.While direct policy analysis is ill-advised at this time due to data inadequacy, the model is illustrated using data presently available for twenty-five relatively homogeneous sub-Sahara African countries. Within the limitations of available data, the findings suggest that reductions in mortality in the region might best be achieved by making appropriate health resources available to the populations. Study findings also suggest that physicians contributed more during the study period to the variance in physical health status than did medical assistants.     

Credit in the euro area: An empirical investigation using aggregate data

This paper uses multivariate cointegration techniques to estimate a model of aggregate bank lending in the euro area. The model provides a quantitative benchmark for assessing conjunctural developments in loans to the area-wide private sector. Large and protracted deviations of realised loans from the paths implied by the model may reveal information about the emergence of financial imbalances as well as about the state of the economy, particularly about the strength of inflationary pressures. A specific application of the model shows that its error-correction term contains information on future changes in inflation over forecast horizons of relevance for monetary policy.     

Modeling and forecasting the time series of US fertility: age distribution,range, and ultimate level

This discussion of modeling focuses on the difficulties in longterm, time-series forecasting of US fertility. Four possibilities are suggested. One difficulty with the traditional approach of using high or low bounds on fertility and mortality is that forecast errors are perfectly correlated over time, which means there are no cancellation of errors over time. The shape of future fertility intervals first increases, then stabilizes, and then decreases instead of remaining stable. This occurs because the number of terms being averaged increases with horizontal length. Alho and Spencer attempted to reduce these errors in time-series. Other difficulties are the idiosyncratic behavior of age specific fertility over time, biological bounds for total fertility rates (TFR) of 16 and zero, the integration of knowledge about fertility behavior that narrows the bounds, the unlikelihood of some probability outcomes of stochastic models with a normally distributed error term, the small relative change in TFR between years, a US fertility cycle of about 40 years, unimportant extrapolation of past trends in child and infant mortality, and the unlikelihood of reversals in mortality and contraceptive use trends. Another problem is the unsuitability of longterm forecasts. New methods include a model which estimates a one parameter family of fertility schedules and then forecasts that single parameter. Another method is a logistic transformation to account for prior information on the bounds on fertility estimates; this method is similar to Bayesian methods for ARMA models developed by Monahan. Models include information on the ultimate level of fertility and assume that the equilibrium level is a stochastic process trending over time. The horizon forecast method is preferred unless the effects of the outliers are known. Estimates of fertility are presented for the equilibrium constrained and logistic transformed model. Forecasts of age specific fertility rates can be calculated from forecasts of the fertility index (a single time varying parameter). The model of fertility fits poorly at older ages but captures some of the wide swings in the historical pattern. Age variations are not accounted for very well. Longterm forecasts tell a great deal about the uncertainty of forecast errors. Estimates are too sensitive to model specification for accuracy and ignore the biological and socioeconomic context.     

SIPP and health care issues.

There are three major areas in health care policy which can be addressed by the data elements in the Survey of Income and Program Participation (SIPP): (1) health insurance coverage and the stability of insurance over time; (2) health disability and its relationship to socioeconomic variables, including unemployment, over time; and (3) health care utilization. The survey does not, however, include information on health care expenditures or on details of visits to providers.     

Autocorrelation of the trade process: Evidence from the Helsinki Stock Exchange

This study investigates how duration-based trading intensity modifies the first-order autocorrelation and the transitory variance of the trade process. Because prices are conditional expected values, a structural model in which the trade duration represents the rate at which prices incorporate new information is developed. This refined model is an extension of the one developed by Madhavan, Richardson, and Roomans (1997) and allows parameters characterizing the arrival rate of new information to be derived. Testing this model with data from the Helsinki Stock Exchange, I was able to determine that a model ignoring trading intensity effects on price changes would underestimate the transitory effects of the trade process. This finding suggests that trade duration captures neglected elements of implicit trading costs that increase with market microstructure effects.     

The argumentative salience of technology frames of reference: An analysis of argumentative discourse in the development of a health information exchange initiative

This study examines the impact that the argumentative salience of technology frames of references has on the execution of complex IS implementation projects. It employs Toulmin's argument model to develop argument maps that depict the structure of argumentation that took place during the development and implementation of an interorganizational health information exchange initiative (HIE) that took place in southeast USA. Toulmin's argument model faciliated the portrayal of frame salience in terms of three structural properties (i.e., blindness, indifference, and ownership). The study used these properties to show how the breadth, depth and conspicuity of frame structures changed during the course of the project and demonstrated how these changes impacted both the level of contestation observed in the project as well as project outcomes. In addition to lending insights that are specific to this case, our conceptualization of frame structure lends itself to cross-case comparisons and future theory building as the impact of these argument structures can be evaluated in a multitude of different contexts.     

The dynamics of utility in the neoclassical OLG model

This paper develops a method to study how life-cycle utility of a sequence of cohorts converges to its steady state level in the neoclassical 2-period overlapping generations model. This method allows to characterize utility changes associated with marginal variations in exogenous policy parameters along the entire transition path between two steady states. At the same time, it is not more complicated than a pure steady state analysis. Moreover, it can be applied to economies for which an explicit solution of the transition path is not available.     

Planning horizon results for the dynamic warehouse location problem

In the last decade, a number of models for the dynamic facility location problem have been proposed. The various models contain differing assumptions regarding the revenues and costs realized in the opening, operation, and closure of a facility as well as considering which of the facility sites are candidates for acquisition or disposal at the beginning of a time period. Since the problem becomes extremely large for practical applications, much of the research has been directed toward developing efficient solution techniques. Most of the models and solutions assume that the facilities will be disposed of at the end of the time horizon since distant future conditions usually can't be forecasted with any reasonable degree of accuracy. The problem with this approach is that the “optimal” solution is optimal for only one hypothesized post horizon facility configuration and may become nonoptimal under a different configuration. Post-optimality analysis is needed to assure management that the “optimal” decision to open or close a facility at a given point in time won't prove to be “nonoptimal” when the planning horizon is extended or when design parameters in subsequent time periods change. If management has some guarantee that the decision to open or close a facility in a given time period won't change, it can safely direct attention to the accuracy of the design parameters within that time period.This paper proposes a mixed integer linear programming model to determine which of a finite set of warehouse sites will be operating in each time period of a finite planning horizon. The model is general in the sense that it can reflect a number of acquisition alternatives—purchase, lease or rent. The principal assumptions of the model are: a) Warehouses are assumed to have infinite capacity in meeting customer demand, b) In each time period, any non-operating warehouse is a candidate for becoming operational, and likewise any operating warehouse is a candidate for disposal, c) During a given time period, the fixed costs of becoming operational at a site are greater than the disposal value at that site to reflect the nonrecoverable costs involved in operating a warehouse. These costs are separate from the acquisition and liquidation values of the site. d) During a time period the operation of a warehouse incurs overhead and maintenance costs as well as a depreciation in the disposal value.To solve the model, it is first simplified and a partial optimal solution is obtained by the iterative examination by both lower and upper bounds on the savings realized if a site is opened in a given time period. An attempt is made to fix each warehouse open or closed in each time period. The bounds are based on the delta and omega tests proposed by Efroymson and Ray (1966) and Khumawala (1972) with adjustment for changes in the value of the warehouse between the beginning and end of a time period. A complete optimal solution is obtained by solving the reduced model with Benders' decomposition procedure. The optimal solution is then tested to determine which time periods contain “tentative” decisions that may be affected by post horizon data by analyzing the relationship between the lower (or upper) bounds used in the model simplification time period. If the warehouse decisions made in a time period satisfy these relationships and are thus unaffected by data changes in subsequent time periods, then the decisions made in earlier time periods will also be unaffected by future changes.     

Commuter Responses to Travel Time Uncertainty under Congested Conditions: Expected Costs and the Provision of Information

A simulation methodology for analyzing policies for reducing traffic congestion and the effect of providing commuters with pre-trip information is developed. This combines a model incorporating travel time uncertainty to determine optimal home departure times with a supply-side congestion model of a highway facility. Congestion is endogenously generated for different scenarios using a randomly generated normal distribution of work start times and previously estimated parameters. The results suggest that greater reductions in the expected costs of commuting may be achievable by policies that reduce travel time variance rather than just travel times. Policy simulations which analyze the congestion effects of providing commuters with pre-trip information are found to not necessarily provide many benefits to those using the information, although gains may be minor because the only behavioral response is limited to departure time changes.     

Is there Complete,Partial, or No Recovery from Childhood Malnutrition? – Empirical Evidence from Indonesia*

This article uses a dynamic panel data model to identify the impact of early nutritional deficiencies on individuals’ health status in later ages. We find that poor nutrition at young ages causes some, but not severe retardation in the growth of future height indicating partial recovery from chronic malnourishment. The results also indicate that – younger children, stunted children, and children who live in communities with six or more health posts exhibit larger recovery. The estimation strategy used here is especially attractive as it relies on weaker stochastic assumptions compared to earlier work in the literature.     

A market model of analysts’ opinions to explain changes in the dispersion of opinions

The demand for and supply of analysts’ opinions in this model yield an equilibrium that demonstrates how the information content of the opinions reacts to changes in exogenous parameters. The model also shows how changes in the parameters make analysts’ opinions more or less dispersed; for example, a decline in investor risk aversion, a decrease in market volatility, and an increase in information costs can lead to analysts’ opinions becoming more similar. Recognizing how exogenous factors can affect the supply and demand of analysts’ opinions gives additional insights into questions concerning what may appear to be herd behavior by analysts and also the relationship between forecast dispersion and information content. (JEL: G29, C71)     

Discrete-time delay dynamics of boundedly rational monopoly

This paper discusses the delay dynamics of monopoly with discrete timescales. It is assumed that a monopoly has delayed and limited information on demand. It is also assumed that the firm wants to react to an average of past data instead of reacting to sudden market changes and this average is used to determine the marginal profit. In the case of one-step delay, the output of the previous time period is selected. In the cases of two and three delays where data at one, two, and three previous time periods are available, it is shown that the steady state undergoes to complex dynamics through either a period-doubling or a Neimark–Sacker bifurcation, depending on the specified values of the parameters. Numerical examples illustrate the theoretical results. Finally, the case of geometric delay is also analyzed to show the birth of the period-doubling bifurcation.     

Modelling the spatiotemporal distribution of the incidence of resident foreign population

Many European countries have recently experienced a substantial increase in the proportion of immigrants in their populations. The incidence of resident foreigners calculated at a national level does not provide information on the local spatial and temporal distribution of the phenomenon. This information may be of crucial importance for planning local policies. In this article, we suggest a tool for practitioners to provide spatiotemporal maps representing the local distribution of the incidence of resident foreigners in the territory, and changes in spatial trends over time. We illustrate this with Italian data at a municipal level, for the period 2003–2008. To account for spatiotemporal interactions in the data, we propose using a generalized additive model incorporating a smoother of the time and space dimensions. Specifically, we set up a tensor product smoother combining a cubic regression spline basis for time and a soap film spline basis for space. This approach provides a consistent framework to produce spatiotemporal maps which could be effectively used by policy makers to decide the allocation of economic resources at a local level.     

DSGE Models in the Frequency Domains

We use frequency domain techniques to estimate a medium‐scale dynamic stochastic general equilibrium (DSGE) model on different frequency bands. We show that goodness of fit, forecasting performance and parameter estimates vary substantially with the frequency bands over which the model is estimated. Estimates obtained using subsets of frequencies are characterized by significantly different parameters, an indication that the model cannot match all frequencies with one set of parameters. In particular, we find that: (i) the low‐frequency properties of the data strongly affect parameter estimates obtained in the time domain; (ii) the importance of economic frictions in the model changes when different subsets of frequencies are used in estimation. This is particularly true for the investment adjustment cost and habit persistence: when low frequencies are present in the estimation, the investment adjustment cost and habit persistence are estimated to be higher than when low frequencies are absent. Copyright © 2014 John Wiley & Sons, Ltd.