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Journal articles on the topic 'Bioproducts; Structural change; Models'

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Published: 4 June 2021
Last updated: 1 February 2022

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1

Perron, Pierre, and Zhongjun Qu. "Estimating restricted structural change models." Journal of Econometrics 134, no. 2 (2006): 373–99. http://dx.doi.org/10.1016/j.jeconom.2005.06.030.

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2

Chong, Terence Tai-Leung. "STRUCTURAL CHANGE IN AR(1) MODELS." Econometric Theory 17, no. 1 (2001): 87–155. http://dx.doi.org/10.1017/s0266466601171045.

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This paper investigates the consistency of the least squares estimators and derives their limiting distributions in an AR(1) model with a single structural break of unknown timing. Let β1 and β2 be the preshift and postshift AR parameter, respectively. Three cases are considered: (i) |β1| < 1 and |β2| < 1; (ii) |β1| < 1 and β2 = 1; and (iii) β1 = 1 and |β2| < 1. Cases (ii) and (iii) are of particular interest but are rarely discussed in the literature. Surprising results are that, in both cases, regardless of the location of the change-point estimate, the unit root can always be consistently estimated and the residual sum of squares divided by the sample size converges to a discontinuous function of the change point. In case (iii), [circumflex over beta]2 does not converge to β2 whenever the change-point estimate is lower than the true change point. Further, the limiting distribution of the break-point estimator for shrinking break is asymmetric for case (ii), whereas those for cases (i) and (iii) are symmetric. The appropriate shrinking rate is found to be different in all cases.
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3

RAYKOV, TENKO. "On structural models for analyzing change." Scandinavian Journal of Psychology 33, no. 3 (1992): 247–65. http://dx.doi.org/10.1111/j.1467-9450.1992.tb00914.x.

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4

Kramer, Walter, Werner Ploberger, and Raimund Alt. "Testing for Structural Change in Dynamic Models." Econometrica 56, no. 6 (1988): 1355. http://dx.doi.org/10.2307/1913102.

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5

Su, Liangjun, and Halbert White. "TESTING STRUCTURAL CHANGE IN PARTIALLY LINEAR MODELS." Econometric Theory 26, no. 6 (2010): 1761–806. http://dx.doi.org/10.1017/s0266466609990788.

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We consider two tests of structural change for partially linear time-series models. The first tests for structural change in the parametric component, based on the cumulative sums of gradients from a single semiparametric regression. The second tests for structural change in the parametric and nonparametric components simultaneously, based on the cumulative sums of weighted residuals from the same semiparametric regression. We derive the limiting distributions of both tests under the null hypothesis of no structural change and for sequences of local alternatives. We show that the tests are generally not asymptotically pivotal under the null but may be free of nuisance parameters asymptotically under further asymptotic stationarity conditions. Our tests thus complement the conventional instability tests for parametric models. To improve the finite-sample performance of our tests, we also propose a wild bootstrap version of our tests and justify its validity. Finally, we conduct a small set of Monte Carlo simulations to investigate the finite-sample properties of the tests.
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6

Pang, Tianxiao, Terence Tai-Leung Chong, Danna Zhang, and Yanling Liang. "STRUCTURAL CHANGE IN NONSTATIONARY AR(1) MODELS." Econometric Theory 34, no. 5 (2017): 985–1017. http://dx.doi.org/10.1017/s0266466617000317.

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This article revisits the asymptotic inference for nonstationary AR(1) models of Phillips and Magdalinos (2007a) by incorporating a structural change in the AR parameter at an unknown time k0. Consider the model ${y_t} = {\beta _1}{y_{t - 1}}I\{ t \le {k_0}\} + {\beta _2}{y_{t - 1}}I\{ t > {k_0}\} + {\varepsilon _t},t = 1,2, \ldots ,T$, where I{·} denotes the indicator function, one of ${\beta _1}$ and ${\beta _2}$ depends on the sample size T, and the other is equal to one. We examine four cases: Case (I): ${\beta _1} = {\beta _{1T}} = 1 - c/{k_T}$, ${\beta _2} = 1$; (II): ${\beta _1} = 1$, ${\beta _2} = {\beta _{2T}} = 1 - c/{k_T}$; (III): ${\beta _1} = 1$, ${\beta _2} = {\beta _{2T}} = 1 + c/{k_T}$; and case (IV): ${\beta _1} = {\beta _{1T}} = 1 + c/{k_T}$, ${\beta _2} = 1$, where c is a fixed positive constant, and kT is a sequence of positive constants increasing to ∞ such that kT = o(T). We derive the limiting distributions of the t-ratios of ${\beta _1}$ and ${\beta _2}$ and the least squares estimator of the change point for the cases above under some mild conditions. Monte Carlo simulations are conducted to examine the finite-sample properties of the estimators. Our theoretical findings are supported by the Monte Carlo simulations.
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7

Vaninsky, Alexander. "Structural change optimization in input-output models." Journal of Interdisciplinary Mathematics 12, no. 6 (2009): 839–61. http://dx.doi.org/10.1080/09720502.2009.10700668.

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8

Hansen, Bruce E. "Testing for structural change in conditional models." Journal of Econometrics 97, no. 1 (2000): 93–115. http://dx.doi.org/10.1016/s0304-4076(99)00068-8.

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9

Wang, Shaoping, Guowei Cui, and Kunpeng Li. "Factor-augmented regression models with structural change." Economics Letters 130 (May 2015): 124–27. http://dx.doi.org/10.1016/j.econlet.2015.03.020.

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10

Phillips, Peter C. B., Degui Li, and Jiti Gao. "Estimating smooth structural change in cointegration models." Journal of Econometrics 196, no. 1 (2017): 180–95. http://dx.doi.org/10.1016/j.jeconom.2016.09.013.

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11

Zeileis, Achim, Friedrich Leisch, Christian Kleiber, and Kurt Hornik. "Monitoring structural change in dynamic econometric models." Journal of Applied Econometrics 20, no. 1 (2005): 99–121. http://dx.doi.org/10.1002/jae.776.

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12

Andrews, Donald W. K., and Ray C. Fair. "Inference in Nonlinear Econometric Models with Structural Change." Review of Economic Studies 55, no. 4 (1988): 615. http://dx.doi.org/10.2307/2297408.

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13

Carrasco, Marine. "Misspecified Structural Change, Threshold, and Markov-switching models." Journal of Econometrics 109, no. 2 (2002): 239–73. http://dx.doi.org/10.1016/s0304-4076(02)00112-4.

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14

Thoma, Mark. "Structural change and lag length in VAR models." Journal of Macroeconomics 30, no. 3 (2008): 965–76. http://dx.doi.org/10.1016/j.jmacro.2007.08.001.

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15

Bai, Jushan, and Pierre Perron. "Computation and analysis of multiple structural change models." Journal of Applied Econometrics 18, no. 1 (2003): 1–22. http://dx.doi.org/10.1002/jae.659.

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16

Zeileis, Achim, and Christian Kleiber. "Validating multiple structural change models-a case study." Journal of Applied Econometrics 20, no. 5 (2005): 685–90. http://dx.doi.org/10.1002/jae.856.

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17

Su, Liangjun, and Zhijie Xiao. "Testing structural change in time-series nonparametric regression models." Statistics and Its Interface 1, no. 2 (2008): 347–66. http://dx.doi.org/10.4310/sii.2008.v1.n2.a12.

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18

Park, Joon Y., and Jaewhan Sung. "Testing for Unit Roots in Models with Structural Change." Econometric Theory 10, no. 5 (1994): 917–36. http://dx.doi.org/10.1017/s0266466600008926.

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This paper considers the unit root tests in models with structural change. Particular attention is given to their dependency on the limiting ratios of the subsample sizes between breaks. The dependency is analyzed in detail, and the invariant testing procedure based on a transformed model is developed. The required transformation is essentially identical to the generalized least-squares correction for heteroskedasticity. The limiting distributions of the new tests do not depend on the relative sizes of the subsamples and are shown to be simple mixtures of the limiting distributions of the corresponding tests from the independent unit root models without structural change.
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19

Raykov, Tenko. "Structural models for studying correlates and predictors of change." Australian Journal of Psychology 44, no. 2 (1992): 101–12. http://dx.doi.org/10.1080/00049539208260150.

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20

Harvey, David I., and Stephen J. Leybourne. "Break Date Estimation for Models with Deterministic Structural Change." Oxford Bulletin of Economics and Statistics 76, no. 5 (2013): 623–42. http://dx.doi.org/10.1111/obes.12037.

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21

Kim, Jae-Hee. "Multiple Structural Change-Point Estimation in Linear Regression Models." Communications for Statistical Applications and Methods 19, no. 3 (2012): 423–32. http://dx.doi.org/10.5351/ckss.2012.19.3.423.

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22

Kim, Jae-Hee. "Comparison of Structural Change Tests in Linear Regression Models." Korean Journal of Applied Statistics 24, no. 6 (2011): 1197–211. http://dx.doi.org/10.5351/kjas.2011.24.6.1197.

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23

Huallacháin, B. Ó. "Statistical Approaches to Structural Change in Regional Interindustry Models." Environment and Planning A: Economy and Space 18, no. 9 (1986): 1189–207. http://dx.doi.org/10.1068/a181189.

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The conventional approach to assessing structural change in regional input – output tables is to measure the impact of coefficient change on the estimation of outputs and multipliers. The methods developed and tested in this paper focus exclusively on the coefficients. Univariate and multivariate statistical analyses can be used to identify and measure various types of changes ranging from coefficient instability to changes in interindustry relationships as a system. A distinction is made between structural changes in input relationships and those in output relationships. The methods are tested by using Washington State data for the years 1963 and 1967. The results are compared with previous analyses of change in these data.
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24

Kang Hao and Brett Inder. "Diagnostic test for structural change in cointegrated regression models." Economics Letters 50, no. 2 (1996): 179–87. http://dx.doi.org/10.1016/0165-1765(95)00750-4.

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25

Ichim, Daniela, Ilaria Peroni, and Fernando Sparasci. "Structural Change Models with an Application in Cryogenic Thermometry." Journal of Mathematical Modelling and Algorithms 4, no. 3 (2005): 253–64. http://dx.doi.org/10.1007/s10852-005-9002-5.

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26

Barker, David H., Diana Rancourt, and Elissa Jelalian. "Flexible Models of Change: Using Structural Equations to Match Statistical and Theoretical Models of Multiple Change Processes." Journal of Pediatric Psychology 39, no. 2 (2013): 233–45. http://dx.doi.org/10.1093/jpepsy/jst082.

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27

García, Marta, Ángeles Alonso, María Luisa Tello, et al. "Editorial: Identifying agri-food research priorities for Spain - 2017 results." Spanish Journal of Agricultural Research 16, no. 3 (2018): e0001. http://dx.doi.org/10.5424/sjar/2018163-13587.

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Among other functions, the INIA is involved in national and international cooperation in the field of agri-food research. The process of identifying and classifying gaps in our knowledge forms an essential part of this effort. This article describes that process, the tools and the materials used to achieve the final objective, namely, the identification of research priorities in the Spanish agri-food sector in order to deal with the societal challenges posed by society and the stakeholders involved. These challenges, within the context of the bioeconomy, are the sustainability of primary and forestry production systems, the safety and quality of food and bioproducts as well as the competitiveness of farmers and companies in this sector. It is necessary to optimize resource management and means of production along with improved efficiency to guarantee sustainability throughout the value chain process. The main goal, under the current scenario of climate change, is to develop models which lead to a balance between food quality and production costs (competitiveness and economic sustainability), ecosystem conservation and mitigation of the environmental impacts (environmental sustainability) while maintaining the population in rural areas (social sustainability). These models will be based on new technology in both intensive and extensive production systems. They should support the improvement and valuation of traditional products together with the formulation and development of foods with new functionalities and quality while at the same time ensuring safety. As well as satisfying consumer demand, improved knowledge must lead to a more efficient use of our own resources and by-products within the framework of a circular economy, including the development of bioproducts, eco-innovation and eco-design.
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28

van Straten, Gerrit. "Models for water quality management: the problem of structural change." Water Science and Technology 37, no. 3 (1998): 103–11. http://dx.doi.org/10.2166/wst.1998.0185.

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This paper challenges the common belief that a calibrated and validated model is sufficient to warrant technically sound application to guide water quality planning measures. The reason is that the planning of measures intrinsically implies that the present situation is going to be changed. Consequently, the model is going to be used outside its original domain, which from a strict systems theoretic point of view is not allowed, unless experience shows that the underlying laws are universal. Due to the complexity of water systems and their contained ecology, the structure of the developed models is almost surely not universal enough to cover changed conditions, and the prediction ability will deteriorate. Various remedies to mitigate the situation are discussed, among them over-specification combined with knowledge from similar systems, cybernetic modelling, educated speculation, and flexibility by feed-back.
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29

Vogt, Michael. "TESTING FOR STRUCTURAL CHANGE IN TIME-VARYING NONPARAMETRIC REGRESSION MODELS." Econometric Theory 31, no. 4 (2014): 811–59. http://dx.doi.org/10.1017/s0266466614000565.

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In this paper, we consider a nonparametric model with a time-varying regression function and locally stationary regressors. We are interested in the question whether the regression function has the same shape over a given time span. To tackle this testing problem, we propose a kernel-based L2-test statistic. We derive the asymptotic distribution of the statistic both under the null and under fixed and local alternatives. To improve the small sample behavior of the test, we set up a wild bootstrap procedure and derive the asymptotic properties thereof. The theoretical analysis of the paper is complemented by a simulation study and a real data example.
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30

Kim, Dongcheol, and Stanley J. Kon. "Structural change and time dependence in models of stock returns." Journal of Empirical Finance 6, no. 3 (1999): 283–308. http://dx.doi.org/10.1016/s0927-5398(99)00005-5.

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31

Boehlje, Michael. "Alternative models of structural change in agriculture and related industries." Agribusiness 8, no. 3 (1992): 219–31. http://dx.doi.org/10.1002/1520-6297(199205)8:3<219::aid-agr2720080303>3.0.co;2-t.

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32

Emerson, M. Jarvin. "Regional structural change: a comparison of two input-output models." Socio-Economic Planning Sciences 23, no. 5 (1989): 241–49. http://dx.doi.org/10.1016/0038-0121(89)90018-9.

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33

Dufour, Jean-Marie, and Jan F. Kiviet. "Exact tests for structural change in first-order dynamic models." Journal of Econometrics 70, no. 1 (1996): 39–68. http://dx.doi.org/10.1016/0304-4076(94)01683-6.

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34

Phillips, G. D. A., and B. P. M. McCabe. "A sequential approach to testing for structural change in econometric models." Empirical Economics 14, no. 2 (1989): 151–65. http://dx.doi.org/10.1007/bf01980594.

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35

Fiteni, Inmaculada. "τ-estimators of regression models with structural change of unknown location". Journal of Econometrics 119, № 1 (2004): 19–44. http://dx.doi.org/10.1016/s0304-4076(03)00153-2.

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36

Doukhan, Paul, and William Kengne. "Inference and testing for structural change in general Poisson autoregressive models." Electronic Journal of Statistics 9, no. 1 (2015): 1267–314. http://dx.doi.org/10.1214/15-ejs1038.

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37

Seo, Byeongseon. "TESTS FOR STRUCTURAL CHANGE IN COINTEGRATED SYSTEMS." Econometric Theory 14, no. 2 (1998): 222–59. http://dx.doi.org/10.1017/s0266466698142044.

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This paper considers tests for structural change of the cointegrating vector and the adjustment vector in the error correction model with an unknown change point. This paper derives new tests for structural change, which are applicable to maximum likelihood estimation. Our tests for structural change of the cointegrating vector have the same nonstandard asymptotic distributions that have been found by Hansen (1992a, Journal of Business and Economic Statistics 10, 321–335). In contrast, the tests on the adjustment vector have the same asymptotic distributions that have been found by Andrews and Ploberger (1994, Econometrica 62, 1383–1414) for models with stationary variables. Asymptotic critical values are provided.
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38

Goodwin, Barry K. "Forecasting Cattle Prices in the Presence of Structural Change." Journal of Agricultural and Applied Economics 24, no. 2 (1992): 11–22. http://dx.doi.org/10.1017/s0081305200018331.

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AbstractRecent empirical research and developments in the cattle industry suggest several reasons to suspect structural change in economic relationships determining cattle prices. Standard forecasting models may ignore structural change and may produce biased and misleading forecasts. Vector autoregressive (VAR) models that allow parameters to vary with time are used to forecast quarterly cattle prices. The VAR procedures are flexible in that they allow the identification of structural change that begins at an a priori unknown point and occurs gradually. The results indicate that the lowest RMSE for out-of-sample forecasts of cattle prices is obtained using a gradually switching VAR model. However, differences between the gradually switching VAR model and a univariate ARIMA model are not strongly significant. Impulse response functions indicate that adjustments of cattle prices to new information have become faster in recent years.
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39

Santos, E. R. O., V. S. Pereira, J. R. F. Arruda, and J. M. C. Dos Santos. "Structural Damage Detection Using Energy Flow Models." Shock and Vibration 15, no. 3-4 (2008): 217–30. http://dx.doi.org/10.1155/2008/176954.

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The presence of a crack in a structure modifies the energy dissipation pattern. As a consequence, damaged structures can present high localized damping. Experimental tests have revealed that crack nucleation and growth increase structural damping which makes this phenomenon useful as a damage locator. This paper examines the energy flow patterns caused by localized damping in rods, beams and plates using the Energy Finite Element Method (EFEM), the Spectral Element Method (SEM) and the Energy Spectral Element Method (ESEM) in order to detect and locate damage. The analyses are performed at high frequencies, where any localized structural change has a strong influence in the structural response. Simulated results for damage detection in rods, beams, and their couplings calculated by each method and using the element loss factor variation to model the damage, are presented and compared. Results for a simple thin plate calculated with EFEM are also discussed.
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40

Hosseini, S., F. Tabib Mahmoudi, and A. Aboutalebi. "BUILT UP CHANGE DETECTION BASED ON STRUCTURAL CLASSIFICATION OF DIGITAL ELEVATION MODELS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 493–96. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-493-2019.

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Abstract. Multi temporal changes in built up areas are mainly caused by natural disasters (such as floods and earthquakes) or urban sprawl. Detecting these changes which consist of construction, destruction and renovation of buildings can play an important role in updating three dimensional city models and making the right decisions for urban management. Generally, change detection methods based on multi temporal remotely sensed data can be divided into 2D and 3D categories. Three dimensional change detection methods are suitable for identifying the changes of three dimensional objects such as buildings and their results are more close to reality. The objective of this study is to provide an effective method for 3D change detection of buildings in urban areas based on Digital Elevation Models (DEM). The proposed method in this paper consists of three main steps; 1) generating the normalized DSM for two epochs, 2) performing segmentation and structural classification of image segments in order to generate multi temporal classification maps, 3) producing the change maps. The ability of the proposed algorithm is evaluated in a rapid developing urban area in Tehran, Iran in a 9-years interval. The obtained results represent that the ground and bare soil decrease for about −1.37% and low-rise buildings also decrease for about −9.7%. Moreover, the class of high-rise buildings increases for about +16.4% which conforms making new constructions in addition to renovation of low-rise buildings.
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41

Brown, Keith C., Larry J. Lockwood, and Scott L. Lummer. "An Examination of Event Dependency and Structural Change in Security Pricing Models." Journal of Financial and Quantitative Analysis 20, no. 3 (1985): 315. http://dx.doi.org/10.2307/2331033.

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42

Cribbie, Robert A., and John Jamieson. "Structural Equation Models and the Regression Bias for Measuring Correlates of Change." Educational and Psychological Measurement 60, no. 6 (2000): 893–907. http://dx.doi.org/10.1177/00131640021970970.

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43

Percoco, Marco, Geoffrey J. D. Hewings, and Lanfranco Senn. "Structural change decomposition through a global sensitivity analysis of input–output models." Economic Systems Research 18, no. 2 (2006): 115–31. http://dx.doi.org/10.1080/09535310600652919.

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44

Masri, Sami F., and Miguel R. Hernandez Garcia. "Experimental study of structural change detection using data-driven reduced-order models." International Journal of Sustainable Materials and Structural Systems 4, no. 2/3/4 (2020): 171. http://dx.doi.org/10.1504/ijsmss.2020.10031278.

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45

Garcia, Miguel R. Hernandez, and Sami F. Masri. "Experimental study of structural change detection using data-driven reduced-order models." International Journal of Sustainable Materials and Structural Systems 4, no. 2/3/4 (2020): 171. http://dx.doi.org/10.1504/ijsmss.2020.109083.

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46

Hao, Kang. "Testing for structural change in cointegrated regression models: some comparisons and generalizations." Econometric Reviews 15, no. 4 (1996): 401–29. http://dx.doi.org/10.1080/07474939608800364.

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47

Chen, Gongmeng, Yoon K. Choi, and Yong Zhou. "Nonparametric estimation of structural change points in volatility models for time series." Journal of Econometrics 126, no. 1 (2005): 79–114. http://dx.doi.org/10.1016/j.jeconom.2004.02.008.

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48

Bernard, Jean-Thomas, Nadhem Idoudi, Lynda Khalaf, and Clément Yélou. "Finite sample multivariate structural change tests with application to energy demand models." Journal of Econometrics 141, no. 2 (2007): 1219–44. http://dx.doi.org/10.1016/j.jeconom.2007.02.004.

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49

Pozo, Garate, Araujo, and Ferreiro. "Energy Harvesting Technologies and Equivalent Electronic Structural Models - Review." Electronics 8, no. 5 (2019): 486. http://dx.doi.org/10.3390/electronics8050486.

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As worldwide awareness about global climate change spreads, green electronics are becoming increasingly popular as an alternative to diminish pollution. Thus, nowadays energy efficiency is a paramount characteristic in electronics systems to obtain such a goal. Harvesting wasted energy from human activities and world physical phenomena is an alternative to deal with the aforementioned problem. Energy harvesters constitute a feasible solution to harvesting part of the energy being spared. The present research work provides the tools for characterizing, designing and implementing such devices in electronic systems through their equivalent structural models.
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50

Richmond, Anthony H. "Immigration and Structural Change: The Canadian Experience, 1971–1986." International Migration Review 26, no. 4 (1992): 1200–1221. http://dx.doi.org/10.1177/019791839202600406.

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The educational, occupational, industrial and income characteristics of immigrants in Canada, 1971–1986, are considered in the context of postindustrial structural changes in the economic and social system, including declining primary and secondary sectors. Seven alternative theoretical models are reviewed. A composite model of “segmented structural change” is found to correspond more closely than alternative theoretical perspectives to the empirical evidence. Specifically, immigrants are found at all levels of the system, but there is differential incorporation by gender, ethnicity and period of immigration. Recent immigrants from Third World countries tend to be disadvantaged.
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