Inconsistent Estimates Research Articles

Prevalence of chronic kidney disease among young people living with HIV in Sub Saharan Africa: A systematic review and meta-analysis.

Globally, the prevalence of chronic kidney disease (CKD) is increasing among young people living with HIV (YPLHIV), with inconsistent estimates. Aggregated data on the prevalence of CKD are needed in sub-Saharan Africa (SSA) to inform strategies for early diagnosis and management. We conducted a systematic review and meta-analysis to estimate the pooled prevalence of CKD among YPLHIV in SSA. We searched Medline/PubMed, EMBASE, African Index Medicus, and African Journals Online for articles reporting the prevalence of CKD among YPLHIV in SSA using predefined search strategies up to 15th January 2024. The reference lists of identified articles were checked for additional eligible studies. The eligibility criteria were studies among YPLHIV aged 10-24 years reporting CKD prevalence defined by either glomerular filtration rate (GFR), albumin-to-creatinine ratio (ACR) or proteinuria. We used a narrative synthesis to report differences between the included studies. The DerSimonian-Laird random effects model was used to pool the CKD prevalence, and heterogeneity was assessed using the Cochrane Q-test and I-squared values. We assessed the risk of bias in each article using the Joanna Briggs Institute checklist and publication bias in a funnel plot and Egger's test. Of 802 retrieved articles, 15 fulfilled the eligibility criteria and were included in the meta-analysis. Of these, 12 (80%) were cross-sectional studies that used estimated GFR to diagnose CKD. Only one study followed the standard definition of CKD. The pooled CKD prevalence from 15 studies was 12% (95% CI 6.0-19.5%), ranging from 0.8% to 53.1% according to the definition used, with a high degree of heterogeneity (I2 = 97.7%, p<0.001). The included studies were of moderate quality, with no evidence of publication bias. Sensitivity analysis showed that the findings were robust to the methodological and analytic approach. CKD prevalence among YPLHIV is moderately high and highly heterogeneous across SSA. The standard definition of CKD should be used to enable estimation of CKD prevalence in different studies and settings. HIV programs enrolling YPLHIV should routinely screen for CKD to ensure early diagnosis and management. PROSPERO registration number: CRD42022347588.

Solar convective velocities: Updated helioseismic constraints

Modeling heat transport by convection is one of the most challenging aspects of solar and stellar physics. The literature currently provides apparently inconsistent observational estimates of the strength of large-scale convective flows in the upper layers of the solar convection zone. In addition, the large-scale convective flows predicted from numerical simulations are substantially stronger than some of the observational inferences in the literature. The current work aims to provide a consistent presentation of some of the main results in the literature both from observations and simulations. To achieve this aim, we carry out an analysis of published estimates of the strength of solar convection at different spatial scales. In particular, we employ a consistent set of conventions to compute the kinetic energy density in the east-west flows. This establishes a clear baseline for future work. The main conclusion is that there are inconsistencies between different observational results and also differences between observations and simulations. This conclusion is important as it demonstrates a need to determine the sources of the inconsistencies between different observational inferences and also to determine the missing ingredients in simulations of solar subsurface convection.

The Long-Run Effects of Monetary Policy

Abstract We document that the real effects of monetary shocks last for over a decade. Our approach relies on (1) identification of exogenous and non-systematic monetary shocks using the trilemma of international finance; (2) merged data from two new international historical cross-country databases; and (3) econometric methods robust to long-horizon inconsistent estimates. Notably, the capital stock and total factor productivity (TFP) exhibit greater hysteresis than labor. When we allow for asymmetry, we find these effects with tightening shocks, but not with loosening shocks. When extending the horizon of the responses reported in several recent studies that use alternative monetary shocks, we find similarly persistent real effects, thus supporting our main findings.

Optimal PMU placement in the Southern Cameroon interconnected grid considering the effect of a group of ZIBs for complete observability and dynamic stability

In this paper, a solution for optimal PMU placement in the southern Cameroon interconnected grid (SIG) is proposed to the issue of dynamic stability within the network. The power system under consideration relies on SCADA parameter monitoring, which offers unsynchronized measurements and inconsistent state estimation of the grid. Moreover, SCADA system's data scan rate hinders its efficiency in capturing the dynamic and transient behaviours in the power grid. The Approach in this research is based on the integer linear programming (ILP) considering the effect of a group of zero-injection buses (ZIBs). Subsequently, a comparative analysis is conducted to evaluate the performance of the ILP method in terms of the number of PMUs, the number of buses observable from one or more nodes, and the sum of redundancy index (SORI) in comparison to other optimization algorithms. The results obtained using MATLAB 2020a demonstrate that the ILP method, considering the effect of a group of ZIBs and the SORI, provides the most optimal solution. Furthermore, a PMU-based dynamic stability assessment is performed, validating the improvement of the visibility of voltage signals.

Testing for Equal Average Forecast Accuracy in Possibly Unstable Environments *

We consider the issue of testing the null of equal average forecast accuracy in a model where the forecast error loss differential series has a potentially non-constant mean function over time. We show that when time variation is present in the loss differential mean, the standard Diebold and Mariano (1995) test, which was proposed for evaluating forecasts in a stable environment, has an asymptotic size of zero, and, whilst consistent, can have reduced local power. This arises due to inconsistent estimation of the implicit long run variance estimator, which diverges under a time varying mean. We suggest a modified statistic that replaces the standard long run variance estimator based on full-sample demeaning of the loss differential series with one based on nonparametric local demeaning. The new long run variance estimator is consistent under both the null and alternative when the mean function is time varying or constant, and in both cases, the modified test recovers the asymptotic size and power properties associated with the original test in the constant mean case. The modified test therefore provides a robust method for testing the equal average forecast accuracy null, allowing for instability in the loss differential mean. The benefits of our test are demonstrated via Monte Carlo simulation and two empirical applications.

Carbohydrate Estimation Accuracy of Two Commercially Available Smartphone Applications vs Estimation by Individuals With Type 1 Diabetes: A Comparative Study.

Despite remarkable progress in diabetes technology, most systems still require estimating meal carbohydrate (CHO) content for meal-time insulin delivery. Emerging smartphone applications may obviate this need, but performance data in relation to patient estimates remain scarce. The objective is to assess the accuracy of two commercial CHO estimation applications, SNAQ and Calorie Mama, and compare their performance with the estimation accuracy of people with type 1 diabetes (T1D). Carbohydrate estimates of 53 individuals with T1D (aged ≥16 years) were compared with those of SNAQ (food recognition + quantification) and Calorie Mama (food recognition + adjustable standard portion size). Twenty-six cooked meals were prepared at the hospital kitchen. Each participant estimated the CHO content of two meals in three different sizes without assistance. Participants then used SNAQ for CHO quantification in one meal and Calorie Mama for the other (all three sizes). Accuracy was the estimate's deviation from ground-truth CHO content (weight multiplied by nutritional facts from recipe database). Furthermore, the applications were rated using the Mars-G questionnaire. Participants' mean ± standard deviation (SD) absolute error was 21 ± 21.5 g (71 ± 72.7%). Calorie Mama had a mean absolute error of 24 ± 36.5 g (81.2 ± 123.4%). With a mean absolute error of 13.1 ± 11.3 g (44.3 ± 38.2%), SNAQ outperformed the estimation accuracy of patients and Calorie Mama (both P > .05). Error consistency (quantified by the within-participant SD) did not significantly differ between the methods. SNAQ may provide effective CHO estimation support for people with T1D, particularly those with large or inconsistent CHO estimation errors. Its impact on glucose control remains to be evaluated.

The Influence of Different Solar System Planetary Ephemerides on Pulsar Timing

Pulsar timing offers a comprehensive avenue for exploring diverse topics in physics and astrophysics. High-precision solar system planetary ephemeris is crucial for pulsar timing as it provides the positions and velocities of solar system planets including the Earth. However, it is inevitable that inherent inconsistencies exist in these ephemerides. Differences between various ephemerides can significantly impact pulsar timing and parameter estimations. Currently, pulsar timing highly depends on the JPL DE ephemeris, for instance, the Pulsar Timing Array data analysis predominantly utilizes DE436. In this study, we examine inconsistencies across various ephemeris series, including JPL DE, EPM, and INPOP. Notably, discrepancies emerge particularly between the current ephemeris DE436 and the earliest released ephemeris DE200, as well as the most recent ephemerides, e.g., DE440, INPOP21A, and EPM2021. Further detailed analysis of the effects of ephemeris on geometric correction procedures for the conversion of measured topocentric times of arrival is presented in this study. Our researches reveal that variations in the Roemer delays across different ephemerides lead to distinct differences. The timing residuals and the fact that these discrepancies can be readily incorporated into the subsequent pulsar parameters, leading to inconsistent fitting estimates, suggest that the influence of errors in the ephemeris on the timing process might currently be underappreciated.

Econometric Advances in the Estimation of Housing Price Determinants

This paper aims to help improve the estimations produced by researchers who rely on conventional housing market pricing models to determine housing prices. The widespread use of panel data in estimating housing prices is justified by the richness of cross-sectional regional or metropolitan data analysed over several periods. Unfortunately, panel data has slope coefficient heterogeneity and cross-sectional dependence, producing inconsistent and misleading estimates of the coefficients using the Ordinary Least Squares (OLS) estimator. Recent advances in econometrics address these panel data limitations, producing better estimates. We analysed the empirical application of these new estimators on housing market panel data, showing that the Fully Modified Ordinary Least Squares Augmented Mean Group (FMOLS-MG) estimator produces the best estimates of the long-term housing market equilibrium and that the Dynamic Common Correlated Effects Mean Group (DCCE-MG) estimator produces the best estimates of the housing market's short-term dynamics. Adopting a trending methodology like Difference-in-Differences (DID) in housing market research to explain the effects of policy decisions on housing prices also has complications related to using the OLS estimator with fixed effects when the data has serial correlation. We show these problems can be overcome using the Feasible Generalised Least Squares estimator in a Seemingly Unrelated Regression Equations (FGLS-SURE) system. Recent econometric developments produce more accurate housing price determinant estimates than conventional econometric methods. These new methodologies can help researchers better estimate the effects of fundamental economic changes and policy decisions on housing prices, which can, in turn, support policymakers in implementing better housing policies.

Study of Spatial Autoregressive Regression With Heteroskedasticity Using the Generalized Method of Moments and Bayesian Approach

Spatial dependence and spatial heteroskedasticity are problems in spatial regression. Spatial autoregressive regression (SAR) concerns only to the dependence on lag. The estimation of SAR parameters containing heteroskedasticity using the maximum likelihood estimation (MLE) method provides biased and inconsistent estimators. The alternative method that can be used are generalized method of moments (GMM) and Bayesian method. GMM uses a combination of linear and quadratic moment functions simultaneously so that the computation is easier than MLE. Bayesian method solves heteroskedasticity by modeling the structure of variance-covariance matrix. The bias are used to evaluate the GMM and Bayes in estimating parameters of SAR model with heteroskedasticity disturbances in simulation data. The results show that GMM and Bayes provides the bias of parameter estimates relatively consistent and smaller with larger number of observations. GMM and Bayes methods are applied to district/city GRDP data in Indonesia. The result show GMM method with Eksponential Distance Weights (EDW) matrix produces the minimum variance and the largest pseudo-R2

Empirical uncertainty evaluation for the pose of a kinematic LiDAR-based multi-sensor system

Abstract Kinematic multi-sensor systems (MSS) describe their movements through six-degree-of-freedom trajectories, which are often evaluated primarily for accuracy. However, understanding their self-reported uncertainty is crucial, especially when operating in diverse environments like urban, industrial, or natural settings. This is important, so the following algorithms can provide correct and safe decisions, i.e. for autonomous driving. In the context of localization, light detection and ranging sensors (LiDARs) are widely applied for tasks such as generating, updating, and integrating information from maps supporting other sensors to estimate trajectories. However, popular low-cost LiDARs deviate from other geodetic sensors in their uncertainty modeling. This paper therefore demonstrates the uncertainty evaluation of a LiDAR-based MSS localizing itself using an inertial measurement unit (IMU) and matching LiDAR observations to a known map. The necessary steps for accomplishing the sensor data fusion in a novel Error State Kalman filter (ESKF) will be presented considering the influences of the sensor uncertainties and their combination. The results provide new insights into the impact of random and systematic deviations resulting from parameters and their uncertainties established in prior calibrations. The evaluation is done using the Mahalanobis distance to consider the deviations of the trajectory from the ground truth weighted by the self-reported uncertainty, and to evaluate the consistency in hypothesis testing. The evaluation is performed using a real data set obtained from an MSS consisting of a tactical grade IMU and a Velodyne Puck in combination with reference data by a Laser Tracker in a laboratory environment. The data set consists of measurements for calibrations and multiple kinematic experiments. In the first step, the data set is simulated based on the Laser Tracker measurements to provide a baseline for the results under assumed perfect corrections. In comparison, the results using a more realistic simulated data set and the real IMU and LiDAR measurements provide deviations about a factor of five higher leading to an inconsistent estimation. The results offer insights into the open challenges related to the assumptions for integrating low-cost LiDARs in MSSs.

Height & income: Labor returns of health in Mexico from 2000 to 2018.

Investment in health has been proposed as a mechanism to promote upward social mobility. Previous analyses have reported inconsistent estimates of the returns to investment in health in Mexico based on different models for different years. We aim to estimate returns for Mexico using data from four time points Adult height and labor income are drawn from the periodical national health and nutrition surveys-a group of relatively standardized surveys-that are representative of individuals living in the country in 2000, 2006, 2012 & 2018. These surveys collect anthropometric measurements and information on individuals' labor income. We estimated Mincerian models separately for men and women using OLS, Heckman, instrumental variables, and Heckman with instrumental variables models. Our results indicate significant and positive returns to health for the four surveys, similar in magnitude across years for women and with variations for men. By 2018, returns to health were about 7.4% per additional centimeter in height for females and 9.3% for males. Investments in health and nutrition during childhood and adolescence that increase health capital-measured as adult height-may promote social mobility in Mexico and similar countries to the extent that these investments differentially increase health capital among the poor.

Remote Sensing and Modeling of the Cryosphere in High Mountain Asia: A Multidisciplinary Review

Over the past decades, the cryosphere has changed significantly in High Mountain Asia (HMA), leading to multiple natural hazards such as rock–ice avalanches, glacier collapse, debris flows, landslides, and glacial lake outburst floods (GLOFs). Monitoring cryosphere change and evaluating its hydrological effects are essential for studying climate change, the hydrological cycle, water resource management, and natural disaster mitigation and prevention. However, knowledge gaps, data uncertainties, and other substantial challenges limit comprehensive research in climate–cryosphere–hydrology–hazard systems. To address this, we provide an up-to-date, comprehensive, multidisciplinary review of remote sensing techniques in cryosphere studies, demonstrating primary methodologies for delineating glaciers and measuring geodetic glacier mass balance change, glacier thickness, glacier motion or ice velocity, snow extent and water equivalent, frozen ground or frozen soil, lake ice, and glacier-related hazards. The principal results and data achievements are summarized, including URL links for available products and related data platforms. We then describe the main challenges for cryosphere monitoring using satellite-based datasets. Among these challenges, the most significant limitations in accurate data inversion from remotely sensed data are attributed to the high uncertainties and inconsistent estimations due to rough terrain, the various techniques employed, data variability across the same regions (e.g., glacier mass balance change, snow depth retrieval, and the active layer thickness of frozen ground), and poor-quality optical images due to cloudy weather. The paucity of ground observations and validations with few long-term, continuous datasets also limits the utilization of satellite-based cryosphere studies and large-scale hydrological models. Lastly, we address potential breakthroughs in future studies, i.e., (1) outlining debris-covered glacier margins explicitly involving glacier areas in rough mountain shadows, (2) developing highly accurate snow depth retrieval methods by establishing a microwave emission model of snowpack in mountainous regions, (3) advancing techniques for subsurface complex freeze–thaw process observations from space, (4) filling knowledge gaps on scattering mechanisms varying with surface features (e.g., lake ice thickness and varying snow features on lake ice), and (5) improving and cross-verifying the data retrieval accuracy by combining different remote sensing techniques and physical models using machine learning methods and assimilation of multiple high-temporal-resolution datasets from multiple platforms. This comprehensive, multidisciplinary review highlights cryospheric studies incorporating spaceborne observations and hydrological models from diversified techniques/methodologies (e.g., multi-spectral optical data with thermal bands, SAR, InSAR, passive microwave, and altimetry), providing a valuable reference for what scientists have achieved in cryosphere change research and its hydrological effects on the Third Pole.

Urbanization and economic growth in China: empirical evidence based on a SIVQR approach

PurposeWe seek to quantify the relationship between urbanization and economic growth in China using recent advances in econometric techniques.Design/methodology/approachThis study adopts a smoothed instrumental variables quantile regression (SIVQR) estimator to obtain consistent estimates of the effects of urbanization on economic growth in China. Our approach accounts for the differential impacts of urbanization across the conditional distribution of economic growth while allowing for an identification strategy that addresses the endogeneity of urbanization. Our main findings reveal that ignoring urbanization endogeneity leads to inconsistent estimates of urbanization effects. Further, we find a positive relationship between urbanization and growth resembling an inverted U-shape. This supports the hypothesis that the beneficial effects of urbanization intensify at initial stages while diminishing beyond a certain threshold, due perhaps to weakening scale economies.FindingsOur main results indicate that the individual productivity gains brought by urbanization outweigh the negative effects thereof that impede productivity, thus contributing to the economic growth in China. Further, we find that ignoring differential impacts of urbanization underestimates the beneficial effects of urbanization for provinces whose quality of governance is in the vicinity of the center of quality distribution. Ignoring the endogeneity of urbanization generates inconsistent estimates of the elasticity of economic growth with respect to urbanization. Finally, we estimate an inverted U-shape resembling relationship between urbanization and growth.Research limitations/implicationsFirst, future studies would benefit from incorporating more data as provinces further east on the mainland become more urbanized and urbanization runs its course. Second, controlling the barriers to rural-urban mobility would contribute to the robustness of the estimated relationship between urbanization and growth once such data became available. Unveiling the impact of government-imposed barriers is key to designing optimal policies that help fuel economic growth in the country. Finally, future research could benefit from information on urbanization sources not considered here such as inter-provincial migration, as such data become publicly available.Practical implicationsQuantifying the beneficial effects of urbanization on economic growth can help guide the government in China to further fuel the growth through a set of relevant policy tools that promote urbanization.Social implicationsRural-urban migration in China lays the groundwork for economic advancement in recipient cities and economies, as it may induce scale economies. This can benefit both the economy at large and the migrants.Originality/valueThe SIVQR estimator accounts for potential heterogeneous effects of urbanization across the entire conditional distribution of growth while allowing for an identification strategy that addresses the endogeneity of urbanization. An additional distinguishing feature of the current study is our use of the most recent novel, provincial-level data obtained from the National Bureau of Statistics of China. Our focus on a single country allows sidestepping issues arising from the inconsistency of the definition of urban across different countries while accounting for intra-country urbanization drivers intrinsic to China, such as natural features and geographic characteristics. Therefore, our approach has the potential to sidestep the bias resulting from the differences in mechanisms behind urbanization-growth relationships across different countries.

A bi-endpoint expectation-maximisation algorithm for re-estimating sample size for the time-to-event endpoint under the blind condition

Abstract The expectation-maximisation (EM) algorithm can be used to adjust the sample size for the time-to-event endpoint without unblinding. Nevertheless, censoring or unreliable initial estimates may render inconsistent estimates by the EM algorithm. To address these limitations, we propose a bi-endpoint EM algorithm that incorporates the time-to-event endpoint and another endpoint, which can encompass various endpoint types and is not limited to efficacy indicators, during the EM iterations. Additionally, we suggest 2 approaches for choosing initial estimates. The application conditions are as follows: (i) at least one endpoint’s initial estimate is reliable and (ii) the influence of this endpoint on the posterior distribution of the latent variable exceeds that of another endpoint.

In situ stress estimation in quantitative micro-elastography.

In quantitative micro-elastography (QME), a pre-characterized compliant layer with a known stress-strain curve is utilized to map stress at the sample surface. However, differences in the boundary conditions of the compliant layer when it is mechanically characterized and when it is used in QME experiments lead to inconsistent stress estimation and consequently, inaccurate elasticity measurements. Here, we propose a novel in situ stress estimation method using an optical coherence tomography (OCT)-based uniaxial compression testing system integrated with the QME experimental setup. By combining OCT-measured axial strain with axial stress determined using a load cell in the QME experiments, we can estimate in situ stress for the compliant layer, more accurately considering its boundary conditions. Our proposed method shows improved accuracy, with an error below 10%, compared to 85% using the existing QME technique with no lubrication. Furthermore, demonstrations on hydrogels and cells indicate the potential of this approach for improving the characterization of the micro-scale mechanical properties of cells and their interactions with the surrounding biomaterial, which has potential for application in cell mechanobiology.

Constraining Trifluoromethane (HFC‐23) Emission in Eastern China Based on Two‐Year Online Measurements

AbstractTrifluoromethane (CHF3, HFC‐23) is one of the most potent greenhouse gases (GHGs), and eastern China has been recognized as a major source region, yet highly inconsistent emission estimates warrant more extensive studies. In this study, we conducted 2‐year online measurements of halocarbons during 2021–2023 at an urban site in the Pearl River Delta region. The observed average HFC‐23 mixing ratios of 43.6 ± 7.8 ppt were ∼20% enhanced compared to observations at the Mace Head global baseline station. Unlike other halocarbons with significant local emissions, HFC‐23 mixing ratios showed distinct seasonal variations with peak values in winter and bottom values in summer. Similar seasonal patterns and highly significant correlation were observed between HFC‐23 and perfluorocyclobutane (c‐C4F8, PFC‐318), a co‐product from manufacturing PTFE using chlorodifluoromethane (CHClF2, HCFC‐22) as feedstock. The weighted potential source contribution function analysis also demonstrated eastern China was the major source area for both HFC‐23 and PFC‐318. Tracer‐based estimation revealed HFC‐23 emissions of 6.7 ± 3.1 Gg yr−1 (equivalent to 98 Tg‐CO2 yr−1 or 0.7% China's annual GHG emissions in 2021) in eastern China. Our results highlight the importance of HFC‐23 emission control in eastern China for China's GHG and F‐gases emission reductions, and suggest that greater attention should be given to HFC‐23 emissions from PTFE production, which is emerging as a more substantial source than previously anticipated.

Burden of Chronic Nonhealing Wounds: An Overview of the Worldwide Humanistic and Economic Burden to the Healthcare System.

Chronic wounds have long been a significant public health concern, but the true impact of these wounds is unknown since research designs and measuring techniques vary, leading to inconsistent estimates. The definition of a wound is a loss of epithelial continuity caused by damage to the tissue. The following conditions can cause chronic wounds: panniculitis, pyoderma gangrenosum, traumatic, neurological, metabolic, hematologic, neoplastic, or infection-related. The growing global incidence of diabetes and the aging population necessitate greater attention to chronic wounds. Regrettably, it is sad that significant healthcare institutions have overlooked wound research. The study of health-related illnesses and occurrences in particular populations, including their distribution, frequency, and determinants, and the application of this research to control health problems.

Multi-centre evaluation of variation in cumulative dose assessment in reirradiation scenarios

Background and PurposeSafe reirradiation relies on assessment of cumulative doses to organs at risk (OARs) across multiple treatments. Different clinical pathways can result in inconsistent estimates. Here, we quantified the consistency of cumulative dose to OARs across multi-centre clinical pathways. Material and MethodsWe provided DICOM planning CT, structures and doses for two reirradiation cases: head & neck (HN) and lung. Participants followed their standard pathway to assess the cumulative physical and EQD2 doses (with provided α/β values), and submitted DVH metrics and a description of their pathways. Participants could also submit physical dose distributions from Course 1 mapped onto the CT of Course 2 using their best available tools. To assess isolated impact of image registrations, a single observer accumulated each submitted spatially mapped physical dose for every participating centre. ResultsCumulative dose assessment was performed by 24 participants. Pathways included rigid (n = 15), or deformable (n = 5) image registration-based 3D dose summation, visual inspection of isodose line contours (n = 1), or summation of dose metrics extracted from each course (n = 3). Largest variations were observed in near-maximum cumulative doses (25.4 – 41.8 Gy for HN, 2.4 – 33.8 Gy for lung OARs), with lower variations in volume/dose metrics to large organs. A standardised process involving spatial mapping of the first course dose to the second course CT followed by summation improved consistency for most near-maximum dose metrics in both cases. ConclusionLarge variations highlight the uncertainty in reporting cumulative doses in reirradiation scenarios, with implications for outcome analysis and understanding of published doses. Using a standardised workflow potentially including spatially mapped doses improves consistency in determination of accumulated dose in reirradiation scenarios.

A Problem with Distance Variables and Alternatives for Their Use

Researchers commonly use distance variables to: (i) estimate the direct influence of a landmark on an outcome of interest, such as a neighborhood park on home price; or (ii) control for omitted spatial influences that affect predictions of key policy variables. While both uses continue, the use of distance as a control, such as distance to Central Business District (CBD), is now more common. Using distance to a given position such as CBD is added to multivariate analysis as a method to capture all remaining, or omitted spatial effects that influence the dependent variable. We show that there is a latent and inherent identification problem with the distance variable; and we show that this extends to the use of distance as a control. These biases affect more than the distance variable. They generate inconsistent estimates for all other spatially distributed variables in a model. We then introduce an alternative control that captures unmodeled influences that vary across space, and we show that this fully stabilizes all model parameter estimates and measures of model efficiency.

The Biases in Applying Static Demand Models Under Dynamic Demand

This article investigates the mechanisms that underlie the biases in price elasticities of demand in applying static demand models under dynamic demand, which have been pointed out by previous empirical studies. It studies three sources of biases: disregard of state variables (affecting short-run elasticity); inconsistent utility parameter estimates; and changing expectations of consumers (affecting long-run elasticity). Disregard of state variables, such as durable product holdings, which is not negligible but not paid much attention to in the literature, leads to an overestimate of short-run own elasticities. Inconsistent utility parameter estimates arises due to the failure to account for consumers’ future expectations and unobserved state variables. Changing expectations of consumers are not explicitly specified in the static model, and this also leads to biased results when applying static models. Regarding the magnitude of the biases, the first and the third sources of biases might induce large biases in price elasticities, especially when the focus is on the large conditional choice probability products. Possible remedies for the use of static demand models are also discussed.