Control Function Methods

The controllability function method, introduced by V. I. Korobov in late 1970s, is known to be an efficient tool for control systems design. It is developed for both linear/nonlinear and finite/infinite dimensional systems. This paper bridges the method with the homogeneity theory popular today. The standard homogeneity known since 18th century is a symmetry of function with respect to uniform scaling of its argument. Some generalizations of the standard homogeneity were introduced in 20th century. This paper shows that the so-called homogeneous norm is a controllability function of the linear autonomous control system and the corresponding closed-loop system is homogeneous in the generalized sense. This immediately yields many useful properties known for homogeneous systems such as robustness (Input-to-State Stability) with respect to a rather large class of perturbations, in particular, with respect to bounded additive measurement noises and bounded additive exogenous disturbances. The main theorem presented in this paper slightly refines the design of the controllability function for a multiply-input linear autonomous control systems. The design procedure consists in solving subsequently a linear algebraic equation and a system of linear matrix inequalities. The homogeneity itself and the use of the canonical homogeneous norm essentially simplify the design of a controllability function and the analysis of the closed-loop system. Theoretical results are supported with examples. The further study of homogeneity-based design of controllability functions seems to be a promising direction for future research.

Aiming at the problem of high energy consumption in domestic marine central cooling water system, the characteristics of seawater pump have been analyzed in detail. A variable-flow pressure control method has been developed to control the speed of seawater pump. The control function method is given for optimal design of ship control system. The total seawater flow can be evaluated according to the load of main engine and the inlet temperature of seawater. The speed ratio is calculated by the control function. The range of speed ratio is determined by the efficiency of frequency converter and pump. The results show that the improved controlling function reduces the energy consumption of the system significantly.

Censoring is a common problem with medical cost data. Methods from traditional survival analysis are not directly applicable to estimate medical costs since patients accumulate costs with different rate functions over time, leading to negatively biased estimates. Heckman's two-step estimator results in large variances when identical explanatory variables that influence selection are included in the structural equation, i.e. when there are no exclusion restrictions. This paper provides a systematic treatment of the correction for nonrandom sample selection bias of medical cost data where the selection rule is described by a censored regression model. The proposed method first uses the duration of time a patient is tracked for the selection, rather than a binary variable, namely whether or not the duration is censored. Second, using Tobit residuals instead of the inverse Mills ratio in the structural equation allows us to decrease large variances introduced by the Heckman model when there are no exclusion restrictions. We show that the resulting estimators are consistent and asymptotically normal. Simulation studies confirmed our results. Moreover, we derive a simple test to determine possible sample selection bias due to censoring. Data from a study on the medical cost of breast, prostate, colon, and lung cancer is used as an application of the method.

The Costs and Returns of Human Migration

Introduction The residential neighbourhood is thought to affect residents because of presumed neighbourhood effects; the independent effects of a neighbourhood’s characteristics on the life chances of its residents. An enormous body of research has tried to measure neighbourhood effects, however, there are no clear conclusions on how much, if any, effect the neighbourhood has on its residents. There is non-random selection of people into neighbourhoods which causes a bias in the modelling of neighbourhood effects. Any correlation found between neighbourhood characteristics and individual outcomes might be explained by selection bias and can therefore not prove the existence of a causal neighbourhood effect. The question is; do poor neighbourhoods make people poor, or do poor people live in unattractive neighbourhoods because they cannot afford to live elsewhere (Cheshire, 2007). Therefore, insight in selection is important to gain more insight in neighbourhood effects (Van Ham and Manley, 2012). For neighbourhood effects research it is important to study selective mobility and neighbourhood choice and to combine neighbourhood effects research with neighbourhood selection research (Doff, 2010a; Van Ham and Manley, 2012; Van Ham et al., 2012; Galster, 2003; Hedman, 2011). The aim of this thesis therefore is to gain more insight in both the causes and the consequences of segregation and thus to study both individual residential mobility and neighbourhood selection and neighbourhood effects. Besides the neighbourhood effects literature, also the segregation literature will benefit from better insights in selective residential mobility because selective residential mobility is one of the main driving forces of segregation. There are two main research questions for this thesis. Firstly, I try to give insight in selective mobility and neighbourhood choice and thus to study where, when and why which people move. What is the effect of personal characteristics, neighbourhood characteristics and macro level housing market developments on individual neighbourhood satisfaction, moving wishes, moving behaviour and neighbourhood selection and on macro level selective mobility patterns and segregation? Secondly, I will test presumed neighbourhood effect mechanisms. Concentration areas of ethnic minorities are seen as undesirable, because their residents are thought to have less contact with the native majority which might hamper their integration and their life chances. It is, however, unclear to what extent social contact is affected by the residential neighbourhood. The second research question therefore asks whether ethnic minorities have less contact with the native majority if they live in minority concentration neighbourhoods. Segregation Segregation is defined as the population composition of neighbourhoods in relation to each other; that is, the concentration or underrepresentation of population groups in neighbourhoods compared to a city or national level average. Selective residential mobility is one of the main driving forces of segregation. Households move to a certain neighbourhood, either because they choose to live there, or because they are constrained in their choice options. Therefore segregation can be both voluntary and involuntary. This thesis focuses on selective residential mobility as cause of segregation, therefore it tries to understand why and where people move. According to residential mobility theory, personal characteristics determine residential preferences and if the residential situation is not in line with these preferences this will lead to dissatisfaction and a desire to move (Brown and Moore, 1970). Whether a dissatisfied household succeeds in moving to a dwelling and neighbourhood more in line with their preferences, depends on their personal resources and restrictions and macro level opportunities and constraints (Mulder and Hooimeijer, 1999). Successful households will move to a neighbourhood more in line with their preferences. There are, however, differences between households in which neighbourhoods are open to choice. Low income households will only be able to select neighbourhoods in which inexpensive dwellings are available. Similarly, households who depend on the social housing sector, or on the owner-occupied sector will only be able to select neighbourhoods where dwellings of this tenure are available. In addition, there are differences between households in which neighbourhood is (deemed) most attractive. People prefer to live among others who are similar to themselves and also facilities directed towards specific groups will make especially concentration neighbourhoods of the own ethnic or income group attractive. Besides residential preferences, also other factors will affect neighbourhood selection; population groups will differ in access to information on neighbourhood attractiveness or housing opportunities and discrimination, or fear of discrimination, can limit the opportunities of minority groups on the housing market. Neighbourhood effects It is typically assumed in European and American urban policy and academic research that spatial concentrations of low income households or ethnic minorities have negative effects on their inhabitants (Friedrichs et al., 2003). An enormous body of research has tried to measure neighbourhood effects; the independent effect of a neighbourhood on its residents when controlling for individual characteristics (see for a review Dietz, 2002; Ellen and Turner, 1997; Sharkey and Faber, 2014; Van Ham et al., 2012). The research attention for neighbourhood effects started with the seminal work of Wilson (1987). He argued that living in concentration areas of the jobless lowest class, isolated from role models, mainstream values and norms, and informal job networks and social contacts with employed, has a negative effect on your life chances. Neighbourhoods can affect their residents via a number of mechanisms (Ellen and Turner, 1997; Erbring and Young, 1979; Galster, 2012). Firstly, the geographical location determines job access and thereby labour market opportunities. Secondly, pollution, noise and disturbance affect health and (thereby) life chances via environmental mechanisms . In addition, neighbourhood stigmatisation can reduce life chances because others have prejudiced ideas and low expectations of the residents of stigmatised neighbourhoods. Also, the quality of institutions such as schools, museums, libraries and sport facilities will generally be lower in disadvantaged neighbourhoods, thereby reducing the life chances of residents. Finally, neighbourhood effects can transpire via social interactions with neighbours. Through collective socialisation and peer pressure people are thought to conform to local social norms (Jencks and Mayer, 1990). Positive role models and social network contacts with individuals with more social capital can help people advance in their work or educational career (Ellen and Turner, 1997). For ethnic minorities it can be important to have social interactions with the native majority to have the opportunity to learn the majority language, standards and values (Lazear, 1999) and to have bridging network ties that can provide access to valuable information not present within the own ethnic network (Buck, 2001). Many neighbourhood effect researchers believe that living in concentrated poverty has negative effects on individuals, and policymakers try to create mixed neighbourhoods to prevent these negative neighbourhood effects. However, although “it is perfectly plausible that poor people are made poorer by the characteristics of the neighbourhoods in which they live” (…) “a close examination of the best research available does not reveal any clear evidence to support it” (Cheshire, 2007: p. ix). Almost all neighbourhood effect studies struggle with selection bias (Cheshire, 2007). Any relation found between neighbourhood characteristics and individual outcomes might be a selection effect and therefore cannot prove the existence of a causal neighbourhood effect. Neighbourhood effects researchers have tried to reduce or eliminate selection bias. Firstly this is done by using quasi-experimental study designs, using households whose residential neighbourhood is determined by external factors (Sampson et al., 2002). Secondly, advanced statistical methods such as sibling studies, fixed effects studies, instrumental variables and propensity score matching are used to reduce selection bias or to control for selection (Harding, 2003). These advancements in methodology have improved our insight in selection bias and in neighbourhood effects, however, since there are no methods that can completely eliminate selection bias, there is still no clear evidence of causal neighbourhood effects. Generally, methods that apply more controls for selection bias find smaller neighbourhood effects. Instead of trying to eliminate selection bias, this thesis tries to provide insight in selection, in why and where which people move. In addition, this thesis tests presumed neighbourhood effect mechanisms. Social interactive mechanisms assume that neighbourhood effects transpire because the population composition of the residential neighbourhood affects with whom you interact (Ellen and Turner, 1997; Galster, 2012). Therefore I test whether the ethnic composition of the neighbourhood affects interethnic contact. Individual differences in determinants of residential satisfaction<

Epidemiological evidence on biosimilars' real-world performance is limited. On July 18th, 2017, a biosimilar health policy was implemented in British Columbia (BC), Canada, mandating all patients initiating a new biologic medication to be prescribed a biosimilar (if/when available). Exploiting a policy change as a natural experiment, we assessed the real-world impact of biosimilar use for inflammatory arthritis (IA) on health resource utilization as a surrogate marker of real-world effectiveness and safety. Using administrative health data, we identified all incident etanercept users for IA in BC with initiation dates between 2014 and 2020 (n=3004) [63·6% female; mean (S.D.) age at IA disease diagnosis 52·5 (16·6) years]. Healthcare utilization over three years after initiation was assessed using outcomes including - physician visits (PV), all-cause hospitalizations (ACH), infection-related hospitalizations (IRH), length of hospital stays (LOS), and emergency room visits (ERV). Using regression discontinuity design, we compared healthcare utilization risk in patients initiating etanercept immediately before/after policy-change date, representing the intention-to-treat effect. Additionally, we estimated the complier average causal effect of biosimilar use with instrumental variable (IV) control function method. Intention-to-treat analyses showed no significant impact of biosimilar policy implementation on PV, HOSP, IRH, LOS, or ERV, with respective adjusted RRs of 0·96 (95% CI: 0·82-1·12), 0·84 (95% CI: 0·49-1·44), 0·91 (95% CI: 0·21-3·86), 0·94 (95% CI: 0·41-2·15), and 0·91 (95% CI: 0·44-1·88). IV analyses indicated biosimilar use in routine settings did not significantly change healthcare utilization, compared to originator etanercept. No significant impact of biosimilar policy or actual biosimilar use on healthcare utilization was observed, suggesting equivalent real-world effectiveness and safety of biosimilars to originators and no unintended consequences of the policy change. CHIR and NSERC.

Chapter 60 Evaluating Social Programs with Endogenous Program Placement and Selection of the Treated

References and Further Reading

&lt;p&gt;Female earnings are underrepresented in the earnings and earnings dynamics literature. This underrepresentation is largely a result of the di erences in participation rates between male and female workers. Female workers tend to have more frequent changes in employment status, and more periods of unemployment than their male counterparts. These periods of unemployment result in observations with zero earnings, and common transformations such as the logarithm are not de ned for zero values. This means that any analysis of the logarithm of earnings is forced to exclude periods where an individual does not work, and cannot take into account the e ect of moving into or out of employment. The higher rate of unemployment in female workers also increases the risk of sample selection bias. If selection into employment is non-random, then estimating earnings equations based on only workers will result in biased estimates. This thesis takes a novel approach by focusing on the annual earnings of females, and in doing so introduces two methods for addressing the issues associated with zero earnings observations. First, the Inverse Hyperbolic Sine (IHS) function is introduced as an alternative to the logarithm. The IHS is de ned for zero values, allowing for the creation of descriptive statistics that take into account periods of unemployment and changes in employment status. While the IHS has many properties that are useful when working with annual earnings, this thesis also highlights a number of estimation issues that can arise when using the function that have not previously been mentioned in the literature. Second, a new correction for sample selection bias that has been proposed by Semykina and Wooldridge (2013) is used to model the annual earnings of female workers. Both the sample selection bias correction and the IHS are applied to data on prime aged females from the Survey of Families, Income, and Employment (SoFIE) data set.&lt;/p&gt;

Regression, matching, control function and instrumental variables methods for recovering the impact of education on individual earnings are reviewed for single treatment and sequential multiple treatments with and without heterogeneous returns. The sensitivity of the estimates once applied to a common dataset is then explored. We show the importance of correcting for detailed test score and family background differences and of allowing for (observable) heterogeneity in returns. We find an average return of 27% for those completing higher education versus anything less. Compared to stopping at 16 without qualifications, we find an average return to O-levels of 18%, to A-levels of 24% and to higher education of 48%.

Research summary: The use of Heckman models by strategy scholars to resolve sample selection bias has increased by more than 700 percent over the last decade, yet significant inconsistencies exist in how they have applied and interpreted these models. In view of these differences, we explore the drivers of sample selection bias and review how Heckman models alleviate it. We demonstrate three important findings for scholars seeking to use Heckman models: First, the independent variable of interest must be a significant predictor in the first stage of a model for sample selection bias to exist. Second, the significance of lambda alone does not indicate sample selection bias. Finally, Heckman models account for sample‐induced endogeneity, but are not effective when other sources of endogeneity are present . Managerial summary: When nonrandom samples are used to test statistical relationships, sample selection bias can lead researchers to flawed conclusions that can, in turn, negatively impact managerial decision‐making. We examine the use of Heckman models, which were designed to resolve sample selection bias, in strategic management research and highlight conditions when sample selection bias is present as well as when it is not. We also distinguish sample selection bias, a form of omitted variable ( OV ) bias, from more traditional OV bias, emphasizing that it is possible for models to have sample selection bias, traditional OV bias, or both. Accurately identifying the type(s) of OV bias present is essential to effectively correcting it. We close with several recommendations to improve practice surrounding the use of Heckman models . Copyright © 2015 John Wiley &amp; Sons, Ltd.

Geographical AnalysisVolume 13, Issue 2 p. 95-116 Free Access Maximum Entropy Spectra and the Spatial and Temporal Dimensions of Economic Fluctuations in an Urban System Claude Marchand, Claude Marchand The cartographic work of Jennifer Wilcox, University of Toronto, is gratefully acknowledged. Claude Marchand is assistant professor of geography, University of Montreal.Search for more papers by this author Claude Marchand, Claude Marchand The cartographic work of Jennifer Wilcox, University of Toronto, is gratefully acknowledged. Claude Marchand is assistant professor of geography, University of Montreal.Search for more papers by this author First published: April 1981 https://doi.org/10.1111/j.1538-4632.1981.tb00719.xCitations: 17 Claude Marchand is assistant professor of geography, University of Montreal. The cartographic work of Jennifer Wilcox, University of Toronto, is gratefully acknowledged. AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat LITERATURE CITED Bartels, C. P. A. (1977). “The structure of Regional Unemployment in the Netherlands. Regional Science and Urban Economics, 7, 103– 5. Bartels, C. P. A., M. Booleman, and W. H. Peters (1978). “A Statistical Analysis of Regional-Unemployment Series. Environment and Planning A, 10, 937– 54. Bennett, R. J. (1979). Spatial Time Series: Analysis—Forecasting—Control. London: Pion. Brechling, F. (1967). “Trends and Cycles in British Regional Unemployment. 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Spectral Analysis and its Applications, San Francisco: Holden-Day. Kanasewich, E. R. (1973). Time Sequence Analysis in Geophysics. Edmonton: The University of Alberta Press. King, L., E. Casetti, and D. Jeffrey (1969). “Economic Impulses in a Regional System of Cities: A Study of Spatial Interaction. Regional Studies, 3, 213– 18. King, L., E. Casetti, and D. Jeffrey (1974). “Cyclical Fluctuations in Unemployment Levels in the U.S. Metropolitan Areas. Tidjdschrift Voor Economische en Social Geographie, 50, 345– 52. King, L. J., and G. R. Clark (1978). “Regional Unemployment Patterns and the Spatial Dimensions of Macro-Economic Policy: The Canadian Experience 1966-1975. Regional Studies, 12, 283– 96. Lacoss, R. T. (1971). “Data Adaptative Spectral Analysis Methods. Geophysics, 36, 661– 75. Schumpeter, J. (1935). “The Analysis of Economic Change. Review of Economic Statistics, 17, 1– 10. Thompson, W. R. (1965). A Preface to Urban Economics. Baltimore: Johns Hopkins University Press. Thompson, W. R. (1975). “ Internal and External Factors in the Development of Urban Economies.” In Regional Policy: Readings in Theory and Applications, edited by J. Friedman and W. Alonso, pp. 210– 20. Cambridge, Mass.: M. I. T. Press. Tiebout, T. M. (1975). “ Exports and Regional Economic Growth.” In Regional Policy: Readings in Theory and Applications, edited by J. Friedman and W. Alonso, pp. 348– 52. Cambridge, Mass.: M. I. T. Press. Citing Literature Volume13, Issue2April 1981Pages 95-116 ReferencesRelatedInformation

Two-step estimation of panel data models with censored endogenous variables and selection bias

We estimated the economic benefits of the Chesapeake Bay TMDL to commercial fish harvesters and consumers, recreational anglers, and other outdoor recreators. To forecast the impacts of the TMDL on harvested fish and shellfish stocks in the bay and connected Atlantic coast waters, we used a summary of judgments from an expert panel and a multi-species model of Chesapeake Bay fisheries. We estimated benefits to consumers in commercial fish markets using a multi-stage inverse demand system, which models price as a function of exogenous supply and accounts for substitution possibilities between 13 different species and as many as five regions. Models were estimated using monthly harvest data from the years 1991 to 2011. The estimated parameters of the inverse demand systems were then used to calculate compensating and equivalent variation from the changes in harvests between the baseline and TMDL scenarios. To estimate producer surplus changes, we assumed that fishing effort will remain fixed at recent levels in each fishery, so harvesting costs do not increase due to the TMDL. The resulting estimates of commercial fishing benefits range between $3 and $26 million per year. We also examined the implications of alternative assumptions about the management regime in each fishery, including fixed effort, open access, and maximum sustainable surplus. We calculated benefits to recreational anglers using a linked participation and site-choice recreation demand model. The model was estimated using angler intercept survey data from the Marine Recreational Fisheries Statistics Survey (MRFSS). Catch rates were calculated using historic reported catch from the MRFSS. We accounted for the sample selection bias caused by the non-random intercept survey sampling design using weights based on historic visitation frequencies at each intercept site. The intercept data were used to estimate a random utility site-choice model, and counts of trips from respondent zip codes were used to estimate a negative binomial participation model conditional on the inclusive value of all sites as estimated by the site-choice model. The resulting estimates of recreational fishing benefits range between $5 and $59 million per year. We used a separate recreation demand model to estimate the benefits associated with other outdoor recreation activities. The model was estimated using aggregate data on the total number of visitors to national and state parks in Maryland, Virginia, and Delaware. The aggregate visitation data alone are insufficient to estimate all parameters of the model, so these data were supplemented with survey data on the number of recreation trips taken to the Chesapeake Bay collected from a random sample of individuals in the study area. The marginal effects of water quality on recreators’ site choices were estimated in a second-stage regression, using estimates of site-specific constants from the first-stage site-choice model as the dependent variable and measures of average water quality conditions and other fixed site attributes as explanatory variables. The central estimates of the outdoor recreation benefits (exclusive of recreational fishing) are between $105 to $280 million per year.

Alternative value estimates of owner-occupied housing: Evidence on sample selection bias and systematic errors