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Ordinary Least Squares (ordinary + least_square)
Terms modified by Ordinary Least Squares Selected AbstractsThe Impact of R&D Intensity on Demand for Specialist Auditor Services,CONTEMPORARY ACCOUNTING RESEARCH, Issue 1 2005JAYNE M. GODFREY Abstract The audit fee research literature argues that auditors' costs of developing brand name reputations, including top-tier designation and recognition for industry specialization, are compensated through audit fee premiums. Audited firms reduce agency costs by engaging high-quality auditors who monitor the levels and reporting of discretionary expenditures and accruals. In this study we examine whether specialist auditor choice is associated with a particular discretionary expenditure - research and development (R&D). For a large sample of U.S. companies from a range of industries, we find strong evidence that R&D intensity is positively associated with firms' choices of auditors who specialize in auditing R&D contracts. Additionally, we find that R&D intensive firms tend to appoint top-tier auditors. We use simultaneous equations to control for interrelationships between dependent variables in addition to single-equation ordinary least squares (OLS) and logistic regression models. Our results are particularly strong in tests using samples of small firms whose auditor choice is not constrained by the need to appoint a top-tier auditor to ensure the auditor's financial independence from the client. [source] FEAR, TV NEWS, AND THE REALITY OF CRIME,CRIMINOLOGY, Issue 3 2000TED CHIRICOS Data from a 1997 survey of 2, 250 Florida residents are used to assess whether and how the reality of crime influences the relationship between watching TV news and fear of crime. Local crime rates, victim experience, and perceived realism of crime news operationalize the reality of crime and are included in ordinary least squares (OLS) estimates of the TV news and fear of crime relationship. These measures of reality are also used as contexts for disaggregating the analysis. Local and national news are related to fear of crime independent of the effects of the reality of crime and other controls. Local news effects are stronger, especially for people who live in high crime places or have recent victim experience. This contextual pattern of findings is consistent with a conclusion that TV news is most influential when it resonates the experience or crime reality of respondents. [source] Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regressionECOGRAPHY, Issue 2 2009L. Mauricio Bini A major focus of geographical ecology and macroecology is to understand the causes of spatially structured ecological patterns. However, achieving this understanding can be complicated when using multiple regression, because the relative importance of explanatory variables, as measured by regression coefficients, can shift depending on whether spatially explicit or non-spatial modeling is used. However, the extent to which coefficients may shift and why shifts occur are unclear. Here, we analyze the relationship between environmental predictors and the geographical distribution of species richness, body size, range size and abundance in 97 multi-factorial data sets. Our goal was to compare standardized partial regression coefficients of non-spatial ordinary least squares regressions (i.e. models fitted using ordinary least squares without taking autocorrelation into account; "OLS models" hereafter) and eight spatial methods to evaluate the frequency of coefficient shifts and identify characteristics of data that might predict when shifts are likely. We generated three metrics of coefficient shifts and eight characteristics of the data sets as predictors of shifts. Typical of ecological data, spatial autocorrelation in the residuals of OLS models was found in most data sets. The spatial models varied in the extent to which they minimized residual spatial autocorrelation. Patterns of coefficient shifts also varied among methods and datasets, although the magnitudes of shifts tended to be small in all cases. We were unable to identify strong predictors of shifts, including the levels of autocorrelation in either explanatory variables or model residuals. Thus, changes in coefficients between spatial and non-spatial methods depend on the method used and are largely idiosyncratic, making it difficult to predict when or why shifts occur. We conclude that the ecological importance of regression coefficients cannot be evaluated with confidence irrespective of whether spatially explicit modelling is used or not. Researchers may have little choice but to be more explicit about the uncertainty of models and more cautious in their interpretation. [source] Analysis of determinants of mammalian species richness in South America using spatial autoregressive modelsECOGRAPHY, Issue 4 2004Marcelo F. Tognelli Classically, hypotheses concerning the distribution of species have been explored by evaluating the relationship between species richness and environmental variables using ordinary least squares (OLS) regression. However, environmental and ecological data generally show spatial autocorrelation, thus violating the assumption of independently distributed errors. When spatial autocorrelation exists, an alternative is to use autoregressive models that assume spatially autocorrelated errors. We examined the relationship between mammalian species richness in South America and environmental variables, thereby evaluating the relative importance of four competing hypotheses to explain mammalian species richness. Additionally, we compared the results of ordinary least squares (OLS) regression and spatial autoregressive models using Conditional and Simultaneous Autoregressive (CAR and SAR, respectively) models. Variables associated with productivity were the most important at determining mammalian species richness at the scale analyzed. Whereas OLS residuals between species richness and environmental variables were strongly autocorrelated, those from autoregressive models showed less spatial autocorrelation, particularly the SAR model, indicating its suitability for these data. Autoregressive models also fit the data better than the OLS model (increasing R2 by 5,14%), and the relative importance of the explanatory variables shifted under CAR and SAR models. These analyses underscore the importance of controlling for spatial autocorrelation in biogeographical studies. [source] POLITICAL SELECTION OF FIRMS INTO PRIVATIZATION PROGRAMS.ECONOMICS & POLITICS, Issue 3 2010EVIDENCE FROM ROMANIAN COMPREHENSIVE DATA Exploiting a unique institutional feature of early Romanian privatization, when a group of firms was explicitly barred from privatization and another was partially privatized by management,employee buyouts, we test how politicians select firms into privatization programs. Using comprehensive firm data, we estimate the relation between preprivatization firm characteristics , the information known to politicians at the time of decision-making , and the effect of privatization on employment, efficiency, and wages. With the estimated coefficients we simulate the effect of privatization on non-privatizable and privatizable firms. We find that politicians expected privatization to increase employment in the privatizable group by 7%,10%, while to decrease it in the non-privatizable group by 10%,30%, depending on the first-stage estimation method, ordinary least squares with or without matching. We do not find such discrepancies in the expected change in firm efficiency; the simulated efficiency effect of privatization is large and positive for both groups of firms, and it is 52%,65% for non-privatizable and 41%,43% for privatizable firms. The analysis does not support the hypothesis that wages played an important role in privatization decisions. Our study suggests that employment concerns played the key role in selecting firms for privatization, even if efficiency gains had to be sacrificed. [source] Comparing weighted and unweighted analyses applied to data with a mix of pooled and individual observationsENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 5 2010Sarah G. Anderson Abstract Smaller organisms may have too little tissue to allow assaying as individuals. To get a sufficient sample for assaying, a collection of smaller individual organisms is pooled together to produce a simple observation for modeling and analysis. When a dataset contains a mix of pooled and individual organisms, the variances of the observations are not equal. An unweighted regression method is no longer appropriate because it assumes equal precision among the observations. A weighted regression method is more appropriate and yields more precise estimates because it incorporates a weight to the pooled observations. To demonstrate the benefits of using a weighted analysis when some observations are pooled, the bias and confidence interval (CI) properties were compared using an ordinary least squares and a weighted least squares t -based confidence interval. The slope and intercept estimates were unbiased for both weighted and unweighted analyses. While CIs for the slope and intercept achieved nominal coverage, the CI lengths were smaller using a weighted analysis instead of an unweighted analysis, implying that a weighted analysis will yield greater precision. Environ. Toxicol. Chem. 2010;29:1168,1171. © 2010 SETAC [source] Fiscal Policy, Business Cycles and Economic Stabilisation: Evidence from Industrialised and Developing Countries,FISCAL STUDIES, Issue 4 2007Young Lee This paper empirically investigates the responsiveness of fiscal policy to business cycles and the effectiveness of fiscal policy in reducing economic fluctuations. From regressions on the responsiveness of fiscal policy to business cycles, we find that the government's current expenditures and subsidies & transfers move counter-cyclically, whereas taxes and capital expenditures move pro-cyclically. Using economic fluctuations in neighbouring countries as an instrumental variable, we show that ordinary least squares (OLS) estimates understate the responsiveness of fiscal policy to economic fluctuations. We also find that fiscal policy responds asymmetrically over economic fluctuations. In investigating the effectiveness of fiscal policy in reducing economic fluctuations, we mitigate omitted variable bias by adding four important factors - military expenditures, oil production, economic fluctuations in neighbouring countries and fiscal policy responsiveness to business cycles. The results of effectiveness regressions are consistent with the responsiveness regressions, highlighting the importance of current expenditures, especially subsidies and transfers, in responding to business cycles and stabilising the economy. [source] Gamma regression improves Haseman-Elston and variance components linkage analysis for sib-pairsGENETIC EPIDEMIOLOGY, Issue 2 2004Mathew J. Barber Abstract Existing standard methods of linkage analysis for quantitative phenotypes rest on the assumptions of either ordinary least squares (Haseman and Elston [1972] Behav. Genet. 2:3,19; Sham and Purcell [2001] Am. J. Hum. Genet. 68:1527,1532) or phenotypic normality (Almasy and Blangero [1998] Am. J. Hum. Genet. 68:1198,1199; Kruglyak and Lander [1995] Am. J. Hum. Genet. 57:439,454). The limitations of both these methods lie in the specification of the error distribution in the respective regression analyses. In ordinary least squares regression, the residual distribution is misspecified as being independent of the mean level. Using variance components and assuming phenotypic normality, the dependency on the mean level is correctly specified, but the remaining residual coefficient of variation is constrained a priori. Here it is shown that these limitations can be addressed (for a sample of unselected sib-pairs) using a generalized linear model based on the gamma distribution, which can be readily implemented in any standard statistical software package. The generalized linear model approach can emulate variance components when phenotypic multivariate normality is assumed (Almasy and Blangero [1998] Am. J. Hum Genet. 68: 1198,1211) and is therefore more powerful than ordinary least squares, but has the added advantage of being robust to deviations from multivariate normality and provides (often overlooked) model-fit diagnostics for linkage analysis. Genet Epidemiol 26:97,107, 2004. © 2004 Wiley-Liss, Inc. [source] Contrasting response of native and alien plant species richness to environmental energy and human impact along alpine elevation gradientsGLOBAL ECOLOGY, Issue 6 2009Lorenzo Marini ABSTRACT Aim, We tested whether the species,energy and species,human relationships vary between native and both naturalized and casual alien species richness when other environmental variables had been taken into account. Location, Trento Province, a region (c. 6200 km2) on the southern border of the European Alps (Italy), subdivided into 156 contiguous (c. 37.5 km2) cells and ranging in elevation from 66 to 3769 m. Methods, Data were separated into three subsets, representing richness of natives, naturalized aliens and casual aliens and separately related to temperature, human population and various environmental correlates of plant species diversity. We applied ordinary least squares and simultaneous autoregressive regressions to identify potential contrasting responses of the three plant status subsets and hierarchical partitioning to evaluate the relative importance of the predictor variables. Results, Variation in alien plant species richness along the region was almost entirely explained by temperature and human population density. The relationships were positive but strongly curvilinear. Native species richness was less strongly related to either factor but was positively related to the presence of calcareous bedrock. Native species richness had a decelerating positive relationship with temperature (R2= 55%), whereas naturalized and casual aliens had a positive accelerating relationship explaining 86% and 62% of the variation in richness, respectively. Native species richness had a positive decelerating relationship with population density (R2= 42%), whilst both alien subsets had a positive accelerating relationship. Main conclusions, Alien species richness was higher in areas with the most rich and diverse assemblages of native species. Areas at high altitudes are not especially prone to alien invasion due to energy constraints, low propagule pressure and disturbance, even considering a potential increased in temperature. Thus, if we consider future environmental change, we should expect a stronger response of aliens than natives in the currently warm, urbanized, low-altitude areas than in cold, high-altitude areas where human population density is low. [source] Richness patterns, species distributions and the principle of extreme deconstructionGLOBAL ECOLOGY, Issue 2 2009Levi Carina Terribile ABSTRACT Aim, To analyse the global patterns in species richness of Viperidae snakes through the deconstruction of richness into sets of species according to their distribution models, range size, body size and phylogenetic structure, and to test if environmental drivers explaining the geographical ranges of species are similar to those explaining richness patterns, something we called the extreme deconstruction principle. Location, Global. Methods, We generated a global dataset of 228 terrestrial viperid snakes, which included geographical ranges (mapped at 1° resolution, for a grid with 7331 cells world-wide), body sizes and phylogenetic relationships among species. We used logistic regression (generalized linear model; GLM) to model species geographical ranges with five environmental predictors. Sets of species richness were also generated for large and small-bodied species, for basal and derived species and for four classes of geographical range sizes. Richness patterns were also modelled against the five environmental variables through standard ordinary least squares (OLS) multiple regressions. These subsets are replications to test if environmental factors driving species geographical ranges can be directly associated with those explaining richness patterns. Results, Around 48% of the total variance in viperid richness was explained by the environmental model, but richness sets revealed different patterns across the world. The similarity between OLS coefficients and the primacy of variables across species geographical range GLMs was equal to 0.645 when analysing all viperid snakes. Thus, in general, when an environmental predictor it is important to model species geographical ranges, this predictor is also important when modelling richness, so that the extreme deconstruction principle holds. However, replicating this correlation using subsets of species within different categories in body size, range size and phylogenetic structure gave more variable results, with correlations between GLM and OLS coefficients varying from ,0.46 up to 0.83. Despite this, there is a relatively high correspondence (r = 0.73) between the similarity of GLM-OLS coefficients and R2 values of richness models, indicating that when richness is well explained by the environment, the relative importance of environmental drivers is similar in the richness OLS and its corresponding set of GLMs. Main conclusions, The deconstruction of species richness based on macroecological traits revealed that, at least for range size and phylogenetic level, the causes underlying patterns in viperid richness differ for the various sets of species. On the other hand, our analyses of extreme deconstruction using GLM for species geographical range support the idea that, if environmental drivers determine the geographical distribution of species by establishing niche boundaries, it is expected, at least in theory, that the overlap among ranges (i.e. richness) will reveal similar effects of these environmental drivers. Richness patterns may be indeed viewed as macroecological consequences of population-level processes acting on species geographical ranges. [source] Power law relationships among hierarchical taxonomic categories in algae reveal a new paradox of the planktonGLOBAL ECOLOGY, Issue 5 2006Sophia I. Passy ABSTRACT Aim, In this continental-scale study, the biodiversity of benthic and planktonic algal communities was explored. A recent analysis of extinct and extant tree communities by Enquist et al. (2002) showed that richness of higher taxa was a power function of species richness, invariant across temporal and spatial scales. Here we examined whether the relationships between algal richness at hierarchical taxonomic levels conform to power laws as seen for trees, and if these relationships differ between benthic and planktonic habitats. Location, Streams from more than 50 major watersheds in the United States. Method, A total of 3698 samples were collected from 1277 locations by the National Water-Quality Assessment Program. Three types of stream habitat were sampled: richest targeted habitats, depositional targeted habitats, and phytoplankton. The relationships between taxonomic richness at the species level vs. all higher categories from genus to phylum across the three habitats were examined by ordinary least squares (OLS) regressions after ln-transformation of all variables. The slopes, b, of these regressions represent the exponents of the power functions that scaled the richness of higher taxonomic levels (T) to species richness (S) in the form: T,Sb. Results, Algal richness at hierarchical taxonomic categories (genus to phylum) is a power function of species richness. The scaling exponent of this function, which captures the diversification of higher taxa, i.e. the rate of increase of their richness with the increase of species richness, is significantly different across environments. Main conclusions, The differential algal diversification in the three studied habitats emphasizes the fundamental role of the environment in structuring the communities of simple organisms such as algae. The finding that the diversification of higher taxa is greater in the seemingly homogeneous planktonic environment, when compared to benthic habitats, encompassing an array of ecological niches, poses a new paradox of the plankton. [source] The Impact of Welfare Reform on Insurance Coverage before Pregnancy and the Timing of Prenatal Care InitiationHEALTH SERVICES RESEARCH, Issue 4 2007Norma I. Gavin Objective. This study investigates the impact of welfare reform on insurance coverage before pregnancy and on first-trimester initiation of prenatal care (PNC) among pregnant women eligible for Medicaid under welfare-related eligibility criteria. Data Sources. We used pooled data from the Pregnancy Risk Assessment Monitoring System for eight states (AL, FL, ME, NY, OK, SC, WA, and WV) from 1996 through 1999. Study Design. We estimated a two-part logistic model of insurance coverage before pregnancy and first-trimester PNC initiation. The impact of welfare reform on insurance coverage before pregnancy was measured by marginal effects computed from coefficients of an interaction term for the postreform period and welfare-related eligibility and on PNC initiation by the same interaction term and the coefficients of insurance coverage adjusted for potential simultaneous equation bias. We compared the estimates from this model with results from simple logistic, ordinary least squares, and two-stage least squares models. Principal Findings. Welfare reform had a significant negative impact on Medicaid coverage before pregnancy among welfare-related Medicaid eligibles. This drop resulted in a small decline in their first-trimester PNC initiation. Enrollment in Medicaid before pregnancy was independent of the decision to initiate PNC, and estimates of the effect of a reduction in Medicaid coverage before pregnancy on PNC initiation were consistent over the single- and two-stage models. Effects of private coverage were mixed. Welfare reform had no impact on first-trimester PNC beyond that from reduced Medicaid coverage in the pooled regression but separate state-specific regressions suggest additional effects from time and income constraints induced by welfare reform may have occurred in some states. Conclusions. Welfare reform had significant adverse effects on insurance coverage and first-trimester PNC initiation among our nation's poorest women of childbearing age. Improved outreach and insurance options for these women are needed to meet national health goals. [source] Patch occupancy of North American mammals: is patchiness in the eye of the beholder?JOURNAL OF BIOGEOGRAPHY, Issue 8 2003Robert K. Swihart Abstract Aim Intraspecific variation in patch occupancy often is related to physical features of a landscape, such as the amount and distribution of habitat. However, communities occupying patchy environments typically exhibit non-random distributions in which local assemblages of species-poor patches are nested subsets of assemblages occupying more species-rich patches. Nestedness of local communities implies interspecific differences in sensitivity to patchiness. Several hypotheses have been proposed to explain interspecific variation in responses to patchiness within a community, including differences in (1) colonization ability, (2) extinction proneness, (3) tolerance to disturbance, (4) sociality and (5) level of adaptation to prevailing environmental conditions. We used data on North American mammals to compare the performance of these ,ecological' hypotheses and the ,physical landscape' hypothesis. We then compared the best of these models against models that scaled landscape structure to ecologically relevant attributes of individual species. Location North America. Methods We analysed data on prevalence (i.e. proportion of patches occupied in a network of patches) and occupancy for 137 species of non-volant mammals and twenty networks consisting of four to seventy-five patches. Insular and terrestrial networks exhibited significantly different mean levels of prevalence and occupancy and thus were analysed separately. Indicator variables at ordinal and family levels were included in models to correct for effects caused by phylogeny. Akaike's information criterion was used in conjunction with ordinary least squares and logistic regression to compare hypotheses. Results A patch network's physical structure, indexed using patch area and isolation, received the greatest support among models predicting the prevalence of species on insular networks. Niche breadth (diet and habitat) received the greatest support for predicting prevalence of species occupying terrestrial networks. For both insular and terrestrial systems, physical features (patch area and isolation) received greater support than any of the ecological hypotheses for predicting species occupancy of individual patches. For terrestrial systems, scaling patch area by its suitability to a focal species and by individual area requirements of the species, and scaling patch isolation by species-specific dispersal ability and niche breadth, resulted in models of patch occupancy that were superior to models relying solely on physical landscape features. For all selected models, unexplained levels of variation were high. Main conclusions Stochasticity dominated the systems we studied, indicating that random events are probably quite important in shaping local communities. With respect to deterministic factors, our results suggest that forces affecting species prevalence and occupancy may differ between insular and terrestrial systems. Physical features of insular systems appeared to swamp ecological differences among species in determining prevalence and occupancy, whereas species with broad niches were disproportionately represented in terrestrial networks. We hypothesize that differential extinction over long time periods in highly variable networks has driven nestedness of mammalian communities on islands, whereas differential colonization over shorter time-scales in more homogeneous networks probably governed the local structure of terrestrial communities. Our results also demonstrate that integration of a species' ecological traits with physical features of a patch network is superior to reliance on either factor separately when attempting to predict the species' probability of patch occupancy in terrestrial systems. [source] A comparison of methods for analysing regression models with both spectral and designed variablesJOURNAL OF CHEMOMETRICS, Issue 10 2004Kjetil Jørgensen Abstract In many situations one performs designed experiments to find the relationship between a set of explanatory variables and one or more responses. Often there are other factors that influence the results in addition to the factors that are included in the design. To obtain information about these so-called nuisance factors, one can sometimes measure them using spectroscopic methods. The question then is how to analyze this kind of data, i.e. a combination of an orthogonal design matrix and a spectroscopic matrix with hundreds of highly collinear variables. In this paper we introduce a method that is an iterative combination of partial least squares (PLS) and ordinary least squares (OLS) and compare its performance with other methods such as direct PLS, OLS and a combination of principal component analysis and least squares. The methods are compared using two real data sets and using simulated data. The results show that the incorporation of external information from spectroscopic measurements gives more information from the experiment and lower variance in the parameter estimates. We also find that the introduced algorithm separates the information from the spectral and design matrices in a nice way. It also has some advantages over PLS in showing lower bias and being less influenced by the relative weighting of the design and spectroscopic variables. Copyright © 2005 John Wiley & Sons, Ltd. [source] Prediction intervals in linear regression taking into account errors on both axesJOURNAL OF CHEMOMETRICS, Issue 10 2001F. Javier del Río Abstract This study reports the expressions for the variances in the prediction of the response and predictor variables calculated with the bivariate least squares (BLS) regression technique. This technique takes into account the errors on both axes. Our results are compared with those of a simulation process based on six different real data sets. The mean error in the results from the new expressions is between 4% and 5%. With weighted least squares, ordinary least squares, the constant variance ratio approach and orthogonal regression, on the other hand, mean errors can be as high as 85%, 277%, 637% and 1697% respectively. An important property of the prediction intervals calculated with BLS is that the results are not affected when the axes are switched. Copyright © 2001 John Wiley & Sons, Ltd. [source] Partial least squares path modelling for relations between baseline factors and treatment outcomes in periodontal regenerationJOURNAL OF CLINICAL PERIODONTOLOGY, Issue 11 2009Yu-Kang Tu Abstract Background: Some clinical outcome variables in periodontal research are mathematically coupled, and it is not feasible to include all the mathematically coupled variables in an ordinary least squares (OLS) regression analysis. The simplest solution to this problem is to drop at least one of the mathematically coupled variables. However, this solution is not satisfactory when the mathematically coupled variables have distinctive clinical implications. Material and Methods: Partial least squares (PLS) methods were used to analyse data from a study on guided tissue regeneration. Relationships between characteristics of baseline lesions and treatment outcomes after 1 year were analysed using PLS, and the results were compared with those from OLS regression. Results: PLS analysis suggested that there were multiple dimensions in the characteristics of baseline lesion: vertical dimension was positively associated with probing pocket depth (PPD) reduction and clinical attachment level (CAL) gain, whilst horizontal dimension was negatively associated with the outcome. Baseline gingival recession had a negative association with PPD reduction but a small positive one with CAL gain. Conclusion: PLS analysis provides new insights into the relationships between baseline characteristics of infrabony defects and periodontal treatment outcomes. The hypothesis of multiple dimensions in baseline lesions needs to be validated by further analysis of different datasets. [source] The Evolving Food Chain: Competitive Effects of Wal-Mart's Entry into the Supermarket IndustryJOURNAL OF ECONOMICS & MANAGEMENT STRATEGY, Issue 4 2009Emek Basker We analyze the effect of Wal-Mart's entry into the grocery market using a unique store-level price panel data set. We use ordinary least squares and two instrumental-variables specifications to estimate the effect of Wal-Mart's entry on competitors' prices of 24 grocery items across several categories. Wal-Mart's price advantage over competitors for these products averages approximately 10%. On average, competitors' response to entry by a Wal-Mart Supercenter is a price reduction of 1,1.2%, mostly due to smaller-scale competitors; the response of the "Big Three" supermarket chains (Albertson's, Safeway, and Kroger) is less than half that size. Low-end grocery stores, which compete more directly with Wal-Mart, cut their prices more than twice as much as higher-end stores. We confirm our results using a falsification exercise, in which we test for Wal-Mart's effect on prices of services that it does not provide, such as movie tickets and dry-cleaning services. [source] Local to unity, long-horizon forecasting thresholds for model selection in the AR(1)JOURNAL OF FORECASTING, Issue 7 2004John L. Turner Abstract This article introduces a novel framework for analysing long-horizon forecasting of the near non-stationary AR(1) model. Using the local to unity specification of the autoregressive parameter, I derive the asymptotic distributions of long-horizon forecast errors both for the unrestricted AR(1), estimated using an ordinary least squares (OLS) regression, and for the random walk (RW). I then identify functions, relating local to unity ,drift' to forecast horizon, such that OLS and RW forecasts share the same expected square error. OLS forecasts are preferred on one side of these ,forecasting thresholds', while RW forecasts are preferred on the other. In addition to explaining the relative performance of forecasts from these two models, these thresholds prove useful in developing model selection criteria that help a forecaster reduce error. Copyright © 2004 John Wiley & Sons, Ltd. [source] Smallholders, institutional services, and commercial transformation in EthiopiaAGRICULTURAL ECONOMICS, Issue 2009Berhanu Gebremedhin Smallholders; Institutions; Commercial transformation Abstract This article examines the role of institutional services of credit, input supply, and extension in the overall commercial transformation process of smallholder agriculture in Ethiopia. Survey data collected in 2006 from 309 sample households in three districts of Ethiopia are used for the analyses. Tobit regression models are used to measure the effect of access to services on the intensity of inputs use for fertilizer and agrochemicals. A probit model is used to measure these effects on the adoption of improved seeds. Intensity of use of seeds is analyzed using an ordinary least squares model. Logarithmic Cobb,Douglass functions are estimated to analyze the effect of access to services on crop productivity. Heckman's two-stage estimation is used to examine determinants of household market participation and the extents of participation. Results show that access to institutional support services plays a significant role in enhancing smallholder productivity and market orientation. Our results imply that expanding and strengthening the institutional services is critical for the intensification and market orientation of smallholder agriculture in Ethiopia. In particular, appropriate incentives and regulatory systems are urgently needed to encourage the involvement of the private sector in the provision of agricultural services. [source] Parents' Union Dissolution and Adolescents' School Performance: Comparing Methodological ApproachesJOURNAL OF MARRIAGE AND FAMILY, Issue 3 2007Michelle L. Frisco We use data from the National Longitudinal Study of Adolescent Health and the Adolescent Health and Academic Achievement Study to estimate how parents' union dissolution influences changes in adolescents' mathematics course work gains, overall grade point average, and course failure rates during a window of approximately 1 year (N =2,629). A primary purpose of this study is demonstrating the utility of propensity score matching techniques for studying topics such as ours that pose methodological challenges such as dealing with endogeneity and selection bias. We compare propensity score matching techniques to ordinary least squares (OLS) regression methods to show and discuss comparability of results obtained using these different procedures. Findings suggest that associations between parents' union dissolution and achievement may be causal, regardless of method used. [source] Sparse partial least squares regression for simultaneous dimension reduction and variable selectionJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 1 2010Hyonho Chun Summary., Partial least squares regression has been an alternative to ordinary least squares for handling multicollinearity in several areas of scientific research since the 1960s. It has recently gained much attention in the analysis of high dimensional genomic data. We show that known asymptotic consistency of the partial least squares estimator for a univariate response does not hold with the very large p and small n paradigm. We derive a similar result for a multivariate response regression with partial least squares. We then propose a sparse partial least squares formulation which aims simultaneously to achieve good predictive performance and variable selection by producing sparse linear combinations of the original predictors. We provide an efficient implementation of sparse partial least squares regression and compare it with well-known variable selection and dimension reduction approaches via simulation experiments. We illustrate the practical utility of sparse partial least squares regression in a joint analysis of gene expression and genomewide binding data. [source] On the non-negative garrotte estimatorJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 2 2007Ming Yuan Summary., We study the non-negative garrotte estimator from three different aspects: consistency, computation and flexibility. We argue that the non-negative garrotte is a general procedure that can be used in combination with estimators other than the original least squares estimator as in its original form. In particular, we consider using the lasso, the elastic net and ridge regression along with ordinary least squares as the initial estimate in the non-negative garrotte. We prove that the non-negative garrotte has the nice property that, with probability tending to 1, the solution path contains an estimate that correctly identifies the set of important variables and is consistent for the coefficients of the important variables, whereas such a property may not be valid for the initial estimators. In general, we show that the non-negative garrotte can turn a consistent estimate into an estimate that is not only consistent in terms of estimation but also in terms of variable selection. We also show that the non-negative garrotte has a piecewise linear solution path. Using this fact, we propose an efficient algorithm for computing the whole solution path for the non-negative garrotte. Simulations and a real example demonstrate that the non-negative garrotte is very effective in improving on the initial estimator in terms of variable selection and estimation accuracy. [source] Generalized least squares with misspecified serial correlation structuresJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 3 2001Sergio G. Koreisha Summary. The regression literature contains hundreds of studies on serially correlated disturbances. Most of these studies assume that the structure of the error covariance matrix , is known or can be estimated consistently from data. Surprisingly, few studies investigate the properties of estimated generalized least squares (GLS) procedures when the structure of , is incorrectly identified and the parameters are inefficiently estimated. We compare the finite sample efficiencies of ordinary least squares (OLS), GLS and incorrect GLS (IGLS) estimators. We also prove new theorems establishing theoretical efficiency bounds for IGLS relative to GLS and OLS. Results from an exhaustive simulation study are used to evaluate the finite sample performance and to demonstrate the robustness of IGLS estimates vis-à-vis OLS and GLS estimates constructed for models with known and estimated (but correctly identified) ,. Some of our conclusions for finite samples differ from established asymptotic results. [source] Spectral Regression For Cointegrated Time Series With Long-Memory InnovationsJOURNAL OF TIME SERIES ANALYSIS, Issue 6 2000D. Marinucci Spectral regression is considered for cointegrated time series with long-memory innovations. The estimates we advocate are shown to be consistent when cointegrating relationships among stationary variables are investigated, while ordinary least squares are inconsistent due to correlation between the regressors and the cointegrating residuals; in the presence of unit roots, these estimates share the same asymptotic distribution as ordinary least squares. As a corollary of the main result, we provide a functional central limit theorem for quadratic forms in non-stationary fractionally integrated processes. [source] TESTING WAGE AND PRICE PHILLIPS CURVES FOR THE UNITED STATESMETROECONOMICA, Issue 4 2007Peter Flaschel ABSTRACT This paper demonstrates how the labour and product markets interact in determining as outcome a generalized reduced-form price Phillips curve. For the labour market we consider a wage Phillips curve and for the product market a price Phillips curve. We estimate separately the wage and price Phillips curves for the USA, using ordinary least squares, non-parametric estimation and three-stage least squares techniques. The finding is that wages are always more flexible than prices with respect to their respective demand pressure and that price inflation responds somewhat more to a medium-run cost pressure than does wage inflation. The implications for macroeconomic stability are demonstrated. We also show,as a link between product and labour markets,that employment is related to output as Okun's law states. In comparing linear and non-linear estimates of the wage and price Phillips curves we find furthermore that for some relationships non-linearities are important while not for others. Although overall the non-linear estimates tend to confirm our linear estimates, non-linearities in some relationships of the Phillips curve are important as well. [source] Hierarchical Linear Modeling in Salary Equity StudiesNEW DIRECTIONS FOR INSTITUTIONAL RESEARCH, Issue 117 2003Jane W. Loeb A hierarchical linear model is compared with an ordinary least squares model for conducting salary-equity studies. [source] New perspectives for estimating body condition from mass/length data: the scaled mass index as an alternative methodOIKOS, Issue 12 2009Jordi Peig Body condition is assumed to influence an animal's health and fitness. Various non-destructive methods based on body mass and a measure of body length have been used as condition indices (CIs), but the dominant method amongst ecologists is currently the calculation of residuals from an ordinary least squares (OLS) regression of body mass against length. Recent studies of energy reserves in small mammals and starlings claimed to validate this method, although we argue that they did not include the most appropriate tests since they compared the CI with the absolute size of energy reserves. We present a novel CI (the ,scaled mass index') based on the central principle of scaling, with important methodological, biological and conceptual advantages. Through a reanalysis of data from small mammals, starlings and snakes, we show that the scaled mass index is a better indicator of the relative size of energy reserves and other body components than OLS residuals, performing better in all seven species and in 19 out of 20 analyses. We also present an empirical and theoretical comparison of the scaled mass index and OLS residuals as CIs. We argue that the scaled mass index is a useful new tool for ecologists. [source] An Extension of the Traditional Theory of Customer Discrimination: Customers Versus CustomersAMERICAN JOURNAL OF ECONOMICS AND SOCIOLOGY, Issue 2 2003Stephanie O. Crofton This study provides an extension on the traditional theory of customer discrimination. The traditional theory looks at customer discrimination via a case in which customers discriminate against a certain type of employee. This paper considers a case of customer discrimination in which customers discriminate against another group of customers. This paper argues that if women choose to attend an all-women college, they are engaging in this previously unexamined form of customer discrimination. Economic theory predicts that firms catering to customers who discriminate will charge higher prices. Thus, this study tests for the existence of customer discrimination by estimating a tuition equation at women's colleges and coeducational schools using ordinary least squares and a dummy-interaction technique. This study finds that, all else held constant, women's colleges do charge higher tuition rates. [source] Use and misuse of the reduced major axis for line-fittingAMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY, Issue 3 2009Richard J. Smith Abstract Many investigators use the reduced major axis (RMA) instead of ordinary least squares (OLS) to define a line of best fit for a bivariate relationship when the variable represented on the X -axis is measured with error. OLS frequently is described as requiring the assumption that X is measured without error while RMA incorporates an assumption that there is error in X. Although an RMA fit actually involves a very specific pattern of error variance, investigators have prioritized the presence versus the absence of error rather than the pattern of error in selecting between the two methods. Another difference between RMA and OLS is that RMA is symmetric, meaning that a single line defines the bivariate relationship, regardless of which variable is X and which is Y, while OLS is asymmetric, so that the slope and resulting interpretation of the data are changed when the variables assigned to X and Y are reversed. The concept of error is reviewed and expanded from previous discussions, and it is argued that the symmetry-asymmetry issue should be the criterion by which investigators choose between RMA and OLS. This is a biological question about the relationship between variables. It is determined by the investigator, not dictated by the pattern of error in the data. If X is measured with error but OLS should be used because the biological question is asymmetric, there are several methods available for adjusting the OLS slope to reflect the bias due to error. RMA is being used in many analyses for which OLS would be more appropriate. Am J Phys Anthropol, 2009. © 2009 Wiley-Liss, Inc. [source] Unemployment Hysteresis in Australian States and Territories: Evidence from Panel Data Unit Root TestsTHE AUSTRALIAN ECONOMIC REVIEW, Issue 2 2003Russell Smyth This article tests for hysteresis by applying panel data unit root tests to quarterly unemployment rates for Australian states and territories between 1982:2 and 2002:1. Panel tests proposed by Levin and Lin (1992) using ordinary least squares and O'Connell (1998) using feasible generalised least squares (which assume that under the alternative hypothesis of stationarity, all labour markets revert to the natural rate at the same speed) provide evidence in support of the natural rate hypothesis. However, the panel test proposed by Im, Pesaran and Shin (1997), which does not assume that all cross-sectional units converge towards the equilibrium value at the same speed under the alternative and is therefore less restrictive than the other two panel tests, finds evidence of hysteresis. Given the advantages of the Im et al. (1997) test over the other two panel tests the results can be interpreted as being consistent with the existence of hysteresis in unemployment [source] | |||||||||||||||||||||||||