26 research outputs found

    THE LINEAR REGRESSION MODEL WITH AUTOCORRELATED ERRORS: JUST SAY NO TO ERROR AUTOCORRELATION

    Get PDF
    This paper focuses on the practice of serial correlation correcting of the Linear Regression Model (LRM) by modeling the error. Simple Monte Carlo experiments are used to demonstrate the following points regarding this practice. First, the common factor restrictions implicitly imposed on the temporal structure of yt and xt appear to be completely unreasonable for any real world application. Second, when one compares the Autocorrelation-Corrected LRM (ACLRM) model estimates with estimates from the (unrestricted) Dynamic Linear Regression Model (DLRM) encompassing the ACLRM there is no significant gain in efficiency! Third, as expected, when the common factor restrictions do not hold the LRM model gives poor estimates of the true parameters and estimation of the ACLRM simply gives rise to different misleading results! On the other hand, estimates from the DLRM and the corresponding VAR model are very reliable. Fourth, the power of the usual Durbin Watson test (DW) of autocorrelation is much higher when the common factor restrictions do hold than when they do not. But, a more general test of autocorrelation is shown to perform almost as well as the DW when the common factor restrictions do hold and significantly better than the DW when the restrictions do not hold. Fifth, we demonstrate how simple it is to, at least, test the common factor restrictions imposed and we illustrate how powerful this test can be.Research Methods/ Statistical Methods,

    A CLASS OF SEPARABILITY FLEXIBLE FUNCTIONAL FORMS

    Get PDF
    Quadratic flexible forms, such as the translog and generalized Leontief, are separability inflexible. That is, separability restrictions render them inflexible with regard to separable structures. A class functional forms is proposed that is flexible with regard to general production structures and remains flexible regarding weakly separable structures when separability restrictions are imposed, thus permitting tests of the separability hypothesis. Additionally, the restricted forms are parsimonious; that is they contain the minimum number of parameters with which flexibility can be achieved.Research Methods/ Statistical Methods,

    INTRODUCING FOODS PRODUCED USING BIOTECHNOLOGY: THE CASE OF BOVINE SOMATOTROPIN

    Get PDF
    A mailed questionnaire was used to assess consumer concerns and potential consumption response attributable to the introduction of bovine somatotropin (bST). Responses from 605 households in Virginia are described and analyzed. Logit models were estimated to identify which issues shape consumersÂ’ decisions to alter milk purchases contingent on the introduction of bST and to determine whether socioeconomic characteristics explain consumersÂ’ attitudes toward these issues. Estimates based on survey responses point toward sizable reductions in fluid milk purchases if bST is introduced. Large retail price reductions are predicted to be insufficient to offset these estimated decreases. Consumer education and marketing strategies are discussed.Food Consumption/Nutrition/Food Safety, Research and Development/Tech Change/Emerging Technologies,

    Spatial Econometrics Revisited: A Case Study of Land Values in Roanoke County

    Get PDF
    Omitting spatial characteristics such as proximity to amenities from hedonic land value models may lead to spatial autocorrelation and biased and inefficient estimators. A spatial autoregressive error model can be used to model the spatial structure of errors arising from omitted spatial effects. This paper demonstrates an alternative approach to modeling land values based on individual and joint misspecification tests using data from Roanoke County in Virginia. Spatial autocorrelation is found in land value models of Roanoke County. Defining neighborhoods based on geographic and socioeconomics characteristics produces better estimates of neighborhood effects on land values than simple distance measures. Implementing a comprehensive set of individual and joint misspecification tests results in better correction for misspecification errors compared to existing practices.Land Economics/Use,

    Residential Land Values in Urbanizing Areas

    Get PDF
    Zoning decisions related to residential lot size and density affect residential land value. Effects of size on residential parcel value in Roanoke County, VA, are estimated with fixed effects hedonic models. Parcel size; elevation; soil permeability; proximity to urban areas, malls, and roads; and location influence parcel value, but the effects vary by value of construction and development status. Parcel value per square meter declines with increasing parcel size. The estimated relationships could be used to evaluate zoning decisions in terms of land values and tax revenues if model estimation uncertainties and responses by developers to zoning strategies are considered.development, fixed effects, hedonic model, property values, residential density, spatial econometrics, Agribusiness, Land Economics/Use, Q24, C25, C52,

    THE LINEAR REGRESSION MODEL WITH AUTOCORRELATED ERRORS: JUST SAY NO TO ERROR AUTOCORRELATION

    No full text
    This paper focuses on the practice of serial correlation correcting of the Linear Regression Model (LRM) by modeling the error. Simple Monte Carlo experiments are used to demonstrate the following points regarding this practice. First, the common factor restrictions implicitly imposed on the temporal structure of yt and xt appear to be completely unreasonable for any real world application. Second, when one compares the Autocorrelation-Corrected LRM (ACLRM) model estimates with estimates from the (unrestricted) Dynamic Linear Regression Model (DLRM) encompassing the ACLRM there is no significant gain in efficiency! Third, as expected, when the common factor restrictions do not hold the LRM model gives poor estimates of the true parameters and estimation of the ACLRM simply gives rise to different misleading results! On the other hand, estimates from the DLRM and the corresponding VAR model are very reliable. Fourth, the power of the usual Durbin Watson test (DW) of autocorrelation is much higher when the common factor restrictions do hold than when they do not. But, a more general test of autocorrelation is shown to perform almost as well as the DW when the common factor restrictions do hold and significantly better than the DW when the restrictions do not hold. Fifth, we demonstrate how simple it is to, at least, test the common factor restrictions imposed and we illustrate how powerful this test can be

    Incentives and Constraints in the Transformation of Punjab Agriculture

    No full text
    The introduction of modern crops varieties in the mid-1960s caused a dramatic change, known as the “green revolution”, in agricultural production in Asia, as elsewhere. However, in spite of their yields, the process of adoption of these varieties has taken a long time, and even today traditional varieties are still widely grown. Various reasons, such as imperfect information, uncertainty, inadequate human capital, and institutional constraints, have been given for this slow diffusion. This research during 1960-79 emphasizes the role of economic incentives and resource availability in determining the pace of technology adoption. Only three years after their introduction, the modern wheat varieties accounted for 70 percent of the wheat area in Punjab. Thereafter, their spread wad more gradual. From this pattern, the authors conclude that the main determinant of the pace of adoption could not have been uncertainty or lack of information. The modern varieties perform best under irrigation and heavy doses of fertilizer, and therefore their expansion of these inputs required mobilization of resources from other activities. The improvement in yield increased the rate of returns to investment in irrigation and fertilizer production and thus generated a gradual expansion in their supply. Since total resources are scarce, such a shift is time-consuming. This explanation illustrates what the authors see as a general and important aspect of the implementation of new technology. When the resource requirements of the new technology are different from those of the existing technology, the pace of the implementation will be determined by the speed at which the resources can be shifted to the new technology. This speed depends on the difference in productivity between the new and existing techniques and on prices and overall resource availability. This identification of the process has far- reaching implications for policies directed toward agricultural growth. This study is part of IFPRI’s continuing research efforts in analyzing the nature and economic consequences of technological change and follows earlier work on the green revolution

    REVISITING ERROR AUTOCORRELATION CORRECTION: COMMON FACTOR RESTRICTIONS AND GRANGER CAUSALITY

    No full text
    This paper demonstrates that linear regression models with an AR(1) error structure implicitly assume that y{t} does not Granger cause any of the exogenous variables in X{t}. An indirect test of the common factor restrictions based on this Granger non-causality is proposed and shown to outperform existing tests

    bST & Milk: Benefit or Bane?

    No full text
    Bovine somatotropin (bST), a genetically engineered hormone for dairy cows that could increase milk yields by as much as 10 to 25 percent, is currently in the final phases of the U.S. Food and Drug Administration (FDA) approval process. Anticipating its ultimate approval economists and industry analysts have concentrated their studies on the potential impact of bST on individual farmers, as well as on the dairy industry as a whole-the supply effects. In contrast, demand aspects have been largely ignored. But they shouldn't be because consumer backlash to bST in terms of lower demand could be substantial

    REVISITING ERROR AUTOCORRELATION CORRECTION: COMMON FACTOR RESTRICTIONS AND GRANGER CAUSALITY

    No full text
    This paper demonstrates that linear regression models with an AR(1) error structure implicitly assume that y{t} does not Granger cause any of the exogenous variables in X{t}. An indirect test of the common factor restrictions based on this Granger non-causality is proposed and shown to outperform existing tests.Research Methods/ Statistical Methods,
    corecore