Relevant bibliographies by topics / Static and Dynamic Panel data Estimation

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  2. Dissertations / Theses
  3. Books
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Academic literature on the topic 'Static and Dynamic Panel data Estimation'

Author: Grafiati
Published: 14 March 2023

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Journal articles on the topic "Static and Dynamic Panel data Estimation"

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Ahmad, Nur Aminah, Georgina M. Tinungki, and Nurtiti Sunusi. "Estimation of Dynamic Panel Data Regression Parameters Using Generalized Methods of Moment." Jurnal Matematika, Statistika dan Komputasi 18, no. 3 (May 15, 2022): 484–91. http://dx.doi.org/10.20956/j.v18i3.20574.

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Panel data is a combination of cross section and time series. There are two panel data models, namely static and dynamic panel data. Because seeing the advantages of the dynamic panel data model which is able to overcome endogeneity problems related to the use of the dependent variable lag where in the static panel data model the use of the dependent variable lag causes the estimation results to be biased and inconsistent, so the author examines the dynamic panel data regression model. In the dynamic data model there is a lag of the dependent variable, this variable is correlated with error. Thus, estimation using OLS will result in a biased and inconsistent estimator. To overcome this, the dynamic panel data model can be estimated using the GMM Blundell-Bond approach. Based on the discussion, the parameter estimation formula for dynamic panel data regression using the Blundell-Bond GMM approach is as follows:
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Labra Lillo, Romilio, and Celia Torrecillas. "Estimating dynamic Panel data. A practical approach to perform long panels." Revista Colombiana de Estadística 41, no. 1 (January 1, 2018): 31–52. http://dx.doi.org/10.15446/rce.v41n1.61885.

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Panel data methodology is one of the most popular tools for quantitative analyses in the field of social sciences, particularly on topics related to economics and business. This technique allows us simultaneously addressing individual effects, numerous periods, and in turn, the endogeneity of the model or independent regressors. Despite these advantages, there are several methodological and practical limitations to perform estimations using this tool. Two types of models can be estimated with Panel data. While those of static nature have been the most developed, for performing dynamic models still remain some theoretical and practical constraints. This paper focus precisely on the latter, dynamics panel data, using an approach that combines theory and praxis, and paying special attention on estimations with macro database, that is to say, dataset with a long period of time and a small number of individuals, also called long panels.
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Lee, Lung-fei, and Jihai Yu. "Initial conditions of dynamic panel data models: on within and between equations." Econometrics Journal 23, no. 1 (August 30, 2019): 115–36. http://dx.doi.org/10.1093/ectj/utz015.

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Summary This paper investigates the quasi-maximum likelihood estimation of short dynamic panel data models. We consider their estimation on both fixed effects and random effects specifications and propose a Hausman test when exogenous variables are present. For a dynamic panel model, initial conditions play important roles in model structure and estimation, and they give rise to a between equation under the random effects framework. With the between equation properly defined, we show that the random effects model can be decomposed into a within equation and a between equation; hence, the random effects estimate is a pooling of the within and between estimates. Thus, our paper extends the pooling in the static panel data model (Maddala, 1971a) to the setting of dynamic panel data. This decomposition of a dynamic panel data model is revealing and valuable for estimation and the formulation of a Hausman test to test the possible correlation of individual effects with included regressors. Monte Carlo experiments are conducted to investigate the finite sample performance of estimators and the Hausman test. An empirical application of growth convergence in OECD countries is provided.
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Thombs, Ryan P. "A Guide to Analyzing Large N, Large T Panel Data." Socius: Sociological Research for a Dynamic World 8 (January 2022): 237802312211176. http://dx.doi.org/10.1177/23780231221117645.

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Fixed effects estimation of a static model with robust or panel corrected standard errors is commonly used to model large N, large T panel data. However, this approach is biased and inconsistent in the presence of dynamic misspecification, slope heterogeneity, and cross-sectional dependence. Common correlated effects estimation of a dynamic model has been advanced to address these issues but is rarely used in sociology. Here, I provide an overview of the large N, large T panel data literature, and I conduct an array of Monte Carlo experiments to compare the fixed effects estimator to the common correlated effects estimator regarding the aforementioned issues. I show that fixed effects estimation with robust or panel corrected standard errors do not address these problems, which is most evident with high levels of slope heterogeneity and lag misspecification, and its performance worsens as the time dimension expands. In contrast, the common correlated effects estimator produces superior estimates as T increases and is robust to slope heterogeneity and cross-sectional dependence. Following the experiments, I present an example by examining the drivers of fossil fuel consumption at the U.S. state level from 1960 to 2018, and I conclude by presenting a decision-making framework for researchers to use to make informed decisions when modeling large N, large T panel data.
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Cziráky, Dario. "Estimation of dynamic structural equation models with latent variables." Advances in Methodology and Statistics 1, no. 1 (January 1, 2004): 185–204. http://dx.doi.org/10.51936/toxt5757.

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The paper proposes a time series generalisation of the structural equation model with latent variables (SEM). An instrumental variable estimator is considered and its asymptotic properties are analysed. Special emphases are placed on the potential use of the lagged observed variables as instruments and consistency of such estimation is established under some general assumptions about the stochastic properties of the modelled variables. In addition, an identification procedure suitable both for static and dynamic structural equation models is described. The methods are illustrated in an empirical application to dynamic panel estimation of a consumption function using UK household data.
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Arshad, Muhammad Usman. "Forecasted E/P Ratio and ROE: Shanghai Stock Exchange (SSE), China." SAGE Open 11, no. 2 (April 2021): 215824402110231. http://dx.doi.org/10.1177/21582440211023189.

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This study explores the influence of forecasted earnings to price ratio (E/P) and ROE to explain the part of the variation in the Shanghai Stock Exchange (SSE) returns. The study analyzed the explanatory capacity of fundamental, risk, and combined valuation approaches variables on comparative mode between static and dynamic models with the induction of un-balanced panel data estimation. A linear dynamic panel technique is being undertaken to forecast the variables. The research findings indicate that the forecasted E/P ratio and ROE significantly explain the variation in SSE stock return and remain highly statistically significant after incorporating risk proxy variables. Moreover, the author also confirms the existence of size, momentum, liquidity, and dividend yield in the Shanghai Stock Exchange. The study introduces the fundamental valuation approach to the Chinese market based on its unique features and designs a log-linear model, which comprises forecasted E/P and ROE in addition to current E/P as an estimator for future stock returns. The incorporation of Driscoll and Kraay standard errors (DKSE) and Panel Corrected standard error (PCSE) under static while difference and system GMM under the scope of dynamic panel estimation is considered to be another contribution of the study.
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Muritala, Adewale T., Adeniyi M. Ijaiya, Ahmed O. Adekunle, Ibraheem K. Nageri, and A. Bolaji Yinus. "Impact of Oil Prices on Stock Market Development in Selected Oil Exporting Sub-Saharan African Countries." Financial Internet Quarterly 16, no. 2 (June 1, 2020): 1–13. http://dx.doi.org/10.2478/fiqf-2020-0008.

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Abstract This study examines the dynamic impacts of oil prices on stock market development in four oil exporting sub-Saharan African countries in the period of 1989-2015. The Arbitrage Pricing Theory (APT) is used as the theoretical framework where stock market prices are hypothesized to be fully reflective of all available information. Static panel data (Pooled OLS, panel Fixed Effect Model, panel Random Effect Model) and dynamic panel model of Generalized Method of Moments (GMM) were employed in the estimation. The estimation of the static panel model shows that oil prices, exchange rates, gross domestic product, inflation and the corruption index have a positive and significant impact on stock market development. However, there is a slight improvement from the estimation of the GMM dynamic panel model which confirmed that oil prices, exchange rates, gross domestic product, investment, inflation and the corruption index have a positive and significant impact on stock market development. The study therefore recommends that investors in selected the Sub-Sahara Africa (SSA) stock market need to be cognizant of the varying impacts of macroeconomic indicators, particularly those that have been found to exert strong influence on stock returns like oil prices, exchange rates, inflation and the corruption index.
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Yay, Gülsün, Hüseyin Taştan, and Asuman Oktayer. "Globalization, economic freedom, and wage inequality: A panel data analysis." Panoeconomicus 63, no. 5 (2016): 581–601. http://dx.doi.org/10.2298/pan130515024y.

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This paper examines the impact of globalization and liberalization on wage inequality using the KOF globalization index, the Economic Freedom Index (EFI) of the Fraser Institute and the Theil industrial pay inequality statistic compiled by the University of Texas Inequality Project (UTIP). Both static and dynamic fixedeffects models are estimated using a 5-year panel data set consisting of about 90 developed and developing countries for the 1970-2005 period. Estimation results from the dynamic panel data specification suggest that wage inequality has a significant and slowly changing component. The overall KOF and EFI indexes are found to be statistically insignificant in the full sample, but the results show that economic freedom is associated with more wage inequality, especially in Organisation for Economic Co-operation and Development (OECD) countries. The estimation results from country groups indicate that more deregulation is associated with more earnings inequality in OECD countries. The results from the models with subcomponents of the EFI imply that access to sound money has a negative effect on wage inequality. A more stable price system in an economy implies a more equal wage distribution in emerging markets (EM), non-OECD countries, and European Union (EU).
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Pirotte, Alain. "Convergence of the static estimation toward the long run effects of dynamic panel data models." Economics Letters 63, no. 2 (May 1999): 151–58. http://dx.doi.org/10.1016/s0165-1765(99)00023-3.

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Tinungki, Georgina Maria, Powell Gian Hartono, Robiyanto Robiyanto, Agus Budi Hartono, Jakaria Jakaria, and Lydia Rosintan Simanjuntak. "The COVID-19 Pandemic Impact on Corporate Dividend Policy of Sustainable and Responsible Investment in Indonesia: Static and Dynamic Panel Data Model Comparison." Sustainability 14, no. 10 (May 18, 2022): 6152. http://dx.doi.org/10.3390/su14106152.

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This research investigates the impact of crisis due to the COVID-19 pandemic on the dividend policy of green index companies in Indonesia, namely the Sustainable and Responsible Investment (SRI) by Biodiversity (KEHATI) Foundation, or SRI-KEHATI indexed companies. The purposive sampling technique was used to collect data from companies listed from 2014 to 2020, using static and dynamic panel data models. From the several panel data models tested, the static panel data regression with random effects model (REM) and fixed effect model (FEM) uses the least square dummy variable-robust standard error (LSDV-RSE) technique are the best econometric models feasible. The system generalized method of moments (SYS-GMM) is used as a suitable econometric model with a robustness test used to determine static panel data regression. It is reported that SRI-KEHATI indexed companies tend to distribute dividends positively during this crisis, and is also statistically proven robust. This gives a positive signal to the capital market concerning the sluggish trading activity. The market reaction test, using two-approaches, showed that this business did not provide a positive reaction to the capital market, which turned out to be pessimistic. Furthermore, profitability and financial leverage have a robust effect, while dividends from the previous year affect dividend policy on the static panel data model, and firm size affect dividend policy on SYS-GMM. Predictors that proved influential with a direction not in line with the hypothesis were investment opportunities on REM and SYS-GMM, and firm age on SYS-GMM. The parameter estimation that passes the model specification test is feasible, whiles the biased and inconsistency of parameter estimation due to the alleged correlation between ui,t and PYDi,t failed to occur in static panel data regression. The endogeneity issue was resolved by dynamic panel data regression with the strongest corrective effect. This research can be used as a reference for investors to obtain optimal returns on green index companies in the country. An optimal dividend policy can increase the value of the SRI-KEHATI indexed companies; therefore, it can contribute optimally to sustainability and responsibility for social and environmental aspects.
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Dissertations / Theses on the topic "Static and Dynamic Panel data Estimation"

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Teshome, Kagnew. "Production Transformation and Sectoral Engine of Growth Drive: a Comparative Exploration on Sub-Saharan Africa and Asian Economies." Doctoral thesis, Università di Siena, 2022. http://hdl.handle.net/11365/1212494.

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The doctoral dissertation intends to explore the level and patterns of production transformation, centering the analysis on sample economies from East Asia, Southeast Asia, South Asia and SSA. It consists of seven parts. The general introduction gives outline of the research theme, claims (problem statement), objectives and research questions. It also introduces the motivation, content and contributions of each of the six parts to the dissertation. Part two intends to give conceptual discussions on production transformation and to review relevant theoretical strands on structural transformation and structural change. The aim is to grasp useful insights on how the dynamic evolution of the production structure of an economy towards the increasing returns sector (chiefly manufacturing) pertains to employment creation, cumulative productivity increases and sustainable development. As a continuation of part two, part three seeks to discuss sectoral role of growth, framing the analytical framework in favor of the dynamic synergetic relationship between sectors (which is related to the multi-sectoral multiplier approach). With the aim at contributing to the debate on sector-led development route and wealth creation in today’s low-income economies that failed to have their own industrialization imitating the advanced economies as well as to the debate on industrialization and service transformation, part three devotes to critically and thoroughly review the tenet of the different theoretical strands (past and present) on engine of growth hypothesis. It contributes to the industrialization or the production transformation and development literature by synthesizing the role of manufacturing and other sectors (agriculture and services) to economic development and poverty reduction in the developing economies context in SSA and Asia. The lengthy discussion of part three vindicated the existence of a synergetic relation between economic sectors and production activities [and the “stimulus complement” role of services to manufacturing] through addressing hosts of questions. The remaining parts of the dissertation were intended to validate the proposed synergetic relationship between economic sectors as well as the “stimulus complement” role of services to manufacturing rather than substitute to it. The possibility for synergetic relationship between manufacturing and services activities in the transformation and development process has been missing or received very little attention in the debate hitherto – advocates of each sector completely ignore or place little focus on the existence of a dynamic synergetic relationship between them. The dissertation calls for synergy because everything is interconnected in the economic system. One cannot discuss about development without acknowledging structural heterogeneity of the economy. So, it is difficult to separately discuss or define anything clearly; difficult to draw stylized fact with respect to sectoral role in economic transformation and development process that is clear and accepted by all. This is simply because synergy by its conception involves logging a middle course between polarized ideas (extremes). In short, synergy rejects extremes and often calls for the ‘middle way,’ neither too far to the right nor to the left. Often the truth in real world production is neither one alternative nor the other but both. Choosing synergy generally requires one to accept ambiguity, uncertainty, mystery and paradox. For instance, manufacturing has indispensable role in the economy; but, agriculture should not be marginalize and belittled, nor services be ignored. The economy needs both, despite manufacturing conventionally has special place to play pivotal role than others.
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Eryuruk, Gunce. "Three Essays on Dynamic Panel Data Estimation." NCSU, 2009. http://www.lib.ncsu.edu/theses/available/etd-07312009-023153/.

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This dissertation consists of three essays, first two of which consider a new estimation method of dynamic panel data models and the last one considers an application of these models. The first essay (Chapter 1) offers empirical likelihood (EL) estimation of dynamic panel data models, which provide great flexibility to empirical researchers. EL estimation method is shown to have great advantages in usual settings, however little is known on the relative merits of these estimators in panel data models. With this essay, we try to fill that gap by establishing the asymptotic properties of the EL estimator for a dynamic panel model with individual effects when both the time and the cross-section dimensions tend to infinity. We give the conditions under which this estimator is consistent and asymptotically normal. In the second essay (Chapter 2), via a Monte Carlo study, we assess the relative finite sample performances of EL, generalized method of moments, and limited information maximum likelihood estimators for an autoregressive panel data model when there are many moment conditions. We also extend our results to the many weak moments settings. Our results suggest that when the overall performances are concerned, in terms of median, interquartile range and median absolute error of the estimators, in both strong and weak moments settings, EL is more reliable. In the final essay (Chapter 3) we consider an application of dynamic panel data models to examine the determinants of the allocation of state highway funds using panel data for North Carolina's 100 counties for the years 1990 to 2005. We make two main contributions with this essay. First, although there have been numerous studies of highway funding at the state level, to our knowledge, there is no analysis at the sub-state or county levels. Second, by using dynamic panel data models and sophisticated methods to estimate them, we account for any potential persistence in the process of adjustment toward an equilibrium, besides, unlike most of the previous studies, we control for the unobserved county heterogeneity and time effects that explain spatial differences, which may cause omitted variable problem if ignored.
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Hu, Wanhong. "Estimation of dynamic heterogeneous panel data models." Connect to resource, 1996. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1266934002.

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Loudermilk, Margaret Susan. "Estimation and testing in dynamic, nonlinear panel data models." Diss., Connect to online resource - MSU authorized users, 2006.

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Mammi, Irene. "Essays in GMM estimation of dynamic panel data models." Thesis, IMT Alti Studi Lucca, 2011. http://e-theses.imtlucca.it/56/1/Mammi_phdthesis.pdf.

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The aim of the work is twofold. First, we investigate the properties of the dynamic panel data (DPD) GMM estimator when the instrument count is high. We introduce the extraction of principal components fromthe instrument matrix as an effective strategy to reduce the number of instruments. Through Monte Carlo experiments, we want to compare the performances of the GMM estimators when the instrument set is factorized, collapsed or limited. Second, we estimate fiscal response functions on simulated panels and on real data to identify the best-performing estimator in this context, where endogeneity and instrument proliferation issues are unavoidable. The dissertation consists of three chapters. The first reviews the literature of DPD estimation and presents the issue of instrument proliferation in DPD GMM estimation. The second introduces the principal component analysis (PCA) to reduce the dimension of the instrument matrix and compares the performances of the factorized, limited and collapsed GMM estimators, finding them similar. Though the simulated models are extremely simplified, the PCA seems to be promising. The third chapter simulates fiscal response functions and investigates the properties of DPD estimators in fiscal rules estimation; the fiscal rules are then estimated on real data for EMU Countries. The system GMM estimator is the best-performing here. Instrument proliferation does not bias the estimates; collapsing and lag truncation of the instrument matrix can lead to misleading results, while the factorized estimator performs well. Discretionary policies within the EMU are systematically found a-cyclical.
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Arellano, Gomez Manuel. "Estimation and testing of dynamic econometric models from panel data." Thesis, London School of Economics and Political Science (University of London), 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.261293.

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Yu, Jihai. "Essays on spatial dynamic panel data model theories and applications /." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1179767430.

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Evaldsson, Matilda. "Has EMU Led to Higher Debt Levels? : -A Dynamic Panel Data Estimation." Thesis, KTH, Industriell ekonomi och organisation (Inst.), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-120396.

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Europe is in the midst of its deepest crisis since the 1930s where unsustainable debt-to-GDP levels are among the most alarming issues. It is so critical that it is unsure if the Euro can be saved. The risk of moral hazard increases within EMU when governments are taking too much risk in their public debt policies due to the anticipation that ECB or other Member States would eventually bail them out. Moreover, the SGP imposes restrictions on government deficits and debts but have previously failed to enforce them. The weakness seen in the past is that no sanctions have been put in place once the limits have been breached and the SGP is therefore incredible. Previous research on common pool and debt spillovers in a monetary union point to an upward drift of public debt as countries join the EMU. Does this argument hold true? In order to find out, 25 OECD countries between the years of 1995 and 2010 are analyzed using System GMM Arellano-Bover/Blundell-Bond one-step estimator. The primary balance, the interest payments, and GDP growth are regressed respectively in order to see through what channel EMU displays its effect. One regression will cover the entire time period and another will only cover the years from 1995 to 2007 in order to isolate the effects of the current crisis. The results, based on the years over the entire time period (including the crisis) suggest that the effect of an EMU Membership goes via the Interest payments which it is connected to positively. By using the equation of debt dynamics, the fact that net debt interest payments are higher for a country within EMU indicates, all else equal, that they have on average higher levels of debt. Nevertheless, this realization might be a crisis phenomenon and the implication of this is not clear. However more importantly, the regressions based on the years of 1995 and 2007 (prior to the crisis) did not display any significant results. These results indicate that there is no significant relationship between a country’s membership in EMU and its level of debt prior to the crisis.
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LeSage, James, and Manfred M. Fischer. "Cross-sectional dependence model specifications in a static trade panel data setting." WU Vienna University of Economics and Business, 2019. http://epub.wu.ac.at/6886/1/2019%2D03%2D31_WP_Cross%2Dsectional.pdf.

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The focus is on cross-sectional dependence in panel trade flow models. We propose alternative specifications for modeling time invariant factors such as socio-cultural indicator variables, e.g., common language and currency. These are typically treated as a source of heterogeneity eliminated using fixed effects transformations, but we find evidence of cross-sectional dependence after eliminating country-specific and time-specific effects. These findings suggest use of alternative simultaneous dependence model specifications that accommodate cross-sectional dependence, which we set forth along with Bayesian estimation methods. Ignoring cross-sectional dependence implies biased estimates from panel trade flow models that rely on fixed effects.
Series: Working Papers in Regional Science
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LeSage, James P., and Manfred M. Fischer. "Cross-sectional dependence model specifications in a static trade panel data setting." WU Vienna University of Economics and Business, 2017. http://epub.wu.ac.at/5904/1/intl_trade_flows_dec_07_2017v3.pdf.

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The focus is on cross-sectional dependence in panel trade flow models. We propose alternative specifications for modeling time invariant factors such as socio-cultural indicator variables, e.g., common language and currency. These are typically treated as a source of heterogeneity eliminated using fixed effects transformations, but we find evidence of cross-sectional dependence after eliminating country-specific effects. These findings suggest use of alternative simultaneous dependence model specifications that accommodate cross-sectional dependence, which we set forth along with Bayesian estimation methods. Ignoring cross-sectional dependence implies biased estimates from panel trade flow models that rely on fixed effects.
Series: Working Papers in Regional Science
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Books on the topic "Static and Dynamic Panel data Estimation"

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Blundell, Richard. Initial conditions and efficient estimation in dynamic panel data models. London: University College, 1991.

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Byun, Jae-Woong. Estimation of discrete dynamic models from endogenously-sampled company panel data: An analysis of direct investmentby Korean firms in the European Union. Leicester: University of Leicester, Department of Economics, 1994.

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Alt, James E., and Shanna S. Rose. Context‐Conditional Political Budget Cycles. Edited by Carles Boix and Susan C. Stokes. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199566020.003.0034.

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This article studies the contextual determinants of incumbents' ability and incentives to engineer political business cycles in American states. It presents a short review of related literature to clarify how and why the theory of political budget cycles evolved. It describes the data and methodology — single estimation method of dynamic panel analysis — used in the study. The results of the study are discussed in the latter part of the article.
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Book chapters on the topic "Static and Dynamic Panel data Estimation"

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Sul, Donggyu. "Static and Dynamic Relationships." In Panel Data Econometrics, 75–109. 1 Edition. | New York : Routledge, 2019.: Routledge, 2019. http://dx.doi.org/10.4324/9780429423765-6.

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Behr, Andreas. "Dynamic panel data estimation." In Investment and Liquidity Constraints, 18–45. Wiesbaden: Deutscher Universitätsverlag, 2003. http://dx.doi.org/10.1007/978-3-322-82010-5_3.

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Nwabia, Francis, Jude Osamor, Robinson Madu, Nkemakolam Izuwa, and Anthony Chikwe. "Inferences Derived from Reservoir Permeability Estimation Using Static and Dynamic Data: Core Data Analysis Versus Drawdown Tests." In Proceedings of the 2021 International Petroleum and Petrochemical Technology Conference, 184–96. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9427-1_18.

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"Estimation of a Dynamic Model." In Panel Data Econometrics with R, 161–84. Chichester, UK: John Wiley & Sons, Ltd, 2018. http://dx.doi.org/10.1002/9781119504641.ch7.

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"Estimation of dynamic effects with panel data." In Empirical Development Economics, 375–88. Routledge, 2014. http://dx.doi.org/10.4324/9780203070925-42.

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Hsiao, Cheng, M. Hashem Pesaran, and A. Kamil Tahmiscioglu. "Bayes estimation of short-run coefficients in dynamic panel data models." In Analysis of Panels and Limited Dependent Variable Models, 268–96. Cambridge University Press, 1999. http://dx.doi.org/10.1017/cbo9780511493140.013.

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Su, Liangjun, and Yonghui Zhang. "Semiparametric Estimation of Partially Linear Dynamic Panel Data Models with Fixed Effects." In Advances in Econometrics, 137–204. Emerald Group Publishing Limited, 2016. http://dx.doi.org/10.1108/s0731-905320160000036014.

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Chay, Kenneth Y., and Dean R. Hyslop. "Identification and Estimation of Dynamic Binary Response Panel Data Models: Empirical Evidence Using Alternative Approaches." In Research in Labor Economics, 1–39. Emerald Group Publishing Limited, 2014. http://dx.doi.org/10.1108/s0147-912120140000039001.

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"A Unified Estimation Approach for Spatial Dynamic Panel Data Models: Stability, Spatial Co-integration, and Explosive Roots." In Handbook of Empirical Economics and Finance, 416–53. Chapman and Hall/CRC, 2016. http://dx.doi.org/10.1201/b10440-18.

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Silva, Elvira, Spiro E. Stefanou, and Alfons Oude Lansink. "Nonparametric Approaches." In Dynamic Efficiency and Productivity Measurement, 191–208. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780190919474.003.0008.

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This chapter focuses on the nonparametric data envelopment analysis (DEA) framework of structural linear programming models underlying the estimation of efficiency. The nonparametric approach to measuring technical and cost inefficiency has been adopted in the examples in earlier chapters. Chapter 3 introduced the notions of inner- and outer-bound technologies. The inner-bound technology representation has dominated the nonparametric empirical applications in the literature on measuring efficiency and productivity. However, as this chapter shows, the outer-bound representation of the technology presents a viable alternative to measuring technical and cost efficiency as well. The chapter also develops an application to farm-level panel data.
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Conference papers on the topic "Static and Dynamic Panel data Estimation"

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Szarowská, Irena. "Impact of public R&D expenditure on economic growth in selected EU countries." In Business and Management 2016. VGTU Technika, 2016. http://dx.doi.org/10.3846/bm.2016.16.

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The aim of the paper is to investigate influence of research and development (R&D) expenditure on economic growth in 20 selected EU member states in the period 1995-2013, time span is also divided into a pre-crisis and a post-crisis period. Basic source of data is Eurostat database.The research is based on a dynamic panel regression model (GMM) and estimations are based on Arellan-Bond estimator (1991). Results confirm positive and statistically significant impact of government R&D expenditure, which is the main driver for economic growth during the analysed period. Importance and positive impact of higher education R&D expenditure increases in the post-crisis period. Contrary, business expenditure is found to be insignificant. Traditional growth variables (a higher share of qualified human resources and a higher intensity of investment) report positive effect, although investment only partly.
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Shan, Hong, and Guangming Gong. "The impact of ownership structure on firm performance: static and dynamic panel data evidence from china’s listed companies." In 2016 National Convention on Sports Science of China, edited by Z. Henan and J. Y. Beijing. Les Ulis, France: EDP Sciences, 2017. http://dx.doi.org/10.1051/ncssc/201701017.

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Kusrini, Dwi Endah, Brodjol S. S. Ulama, and Mutiah Salamah Chamid. "Generalized Method of Moment Estimation Method Lagrange Multiplier Test for Simultaneous Spatial of Dynamic Panel Data." In 1st International Conference on Mathematics and Mathematics Education (ICMMEd 2020). Paris, France: Atlantis Press, 2021. http://dx.doi.org/10.2991/assehr.k.210508.108.

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Rong, Wang, Tang Xiao-wo, and Pan Jing-ming. "Estimation of demand function of products on mobile communication market based on dynamic panel data model." In 2010 2nd IEEE International Conference on Information Management and Engineering (ICIME 2010). IEEE, 2010. http://dx.doi.org/10.1109/icime.2010.5477513.

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Atakishiyev, Fuad, Rizvan Ramazanov, Fergus Allan, and Adrian Zett. "Holistic Approach in Integrating Passive Acoustic Logs with Static and Dynamic Data Improved Wells Integrity Understanding at Field Scale." In SPE Annual Caspian Technical Conference. SPE, 2021. http://dx.doi.org/10.2118/207060-ms.

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Abstract Proactive well diagnostic surveillance helps with safe delivery of production by effective well management and risk mitigation. The objective of the paper is to demonstrate the data analytics approach utilizing passive acoustic technology in combination with conventional methods of detecting low magnitude dynamic events behind single or multiple casing strings. The results of integrated interpretation of passive acoustic wireline technology with the data from different sources helped to make optimal decision. Traditional well integrity diagnostic includes temperature and passive acoustic data analysis that are associated with high uncertainty. A newer generation of array passive acoustic technology with enhanced sensitivity capabilities was deployed offshore Azerbaijan. A combination of array passive acoustics data, single point temperature and distributed fiber optic data have been acquired during a multi-well campaign. Extensive review of well integrity history, downhole and surface gauge data incorporated with passive acoustic data from arrays of spectral sensors in time and depth domain helped to refine the process and evolve into a unique interpretation methodology. The comprehensive interpretation accounted for integration of all available static and dynamic data such as: fluids and formation pressure distribution along the borehole, cement bond logs evaluation, annuli pressure and temperature, production and downhole gauge measurements, fibre optic data, temperature and passive acoustic logs. This helped to understand the low scale dynamic events behind the casing and make an informed decision on safe and reliable well operations. The sensitivity of array passive acoustic technology proved successful in detecting subtle acoustic events where conventional methods failed or had limited success. Successful results have been achieved by customizing the logging program using a multiple well evolutionary approach that improved data quality and saved rig time. Interpretation and decisions derived from each well involved multi-disciplinary well review panel sessions with specialists from subsurface & geohazards, drilling & completions, production & operations departments. Case studies presented in this paper describe the interpretation approach of highly sensitive array passive acoustic sensors in combination with available static and dynamic point and distributed data. The logging program and interpretation approach used in this article could be considered as a basis for future applications in wells with similar design.
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Atakishiyev, Fuad, Rizvan Ramazanov, Fergus Allan, and Adrian Zett. "Holistic Approach in Integrating Passive Acoustic Logs with Static and Dynamic Data Improved Wells Integrity Understanding at Field Scale." In SPE Annual Caspian Technical Conference. SPE, 2021. http://dx.doi.org/10.2118/207060-ms.

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Abstract Proactive well diagnostic surveillance helps with safe delivery of production by effective well management and risk mitigation. The objective of the paper is to demonstrate the data analytics approach utilizing passive acoustic technology in combination with conventional methods of detecting low magnitude dynamic events behind single or multiple casing strings. The results of integrated interpretation of passive acoustic wireline technology with the data from different sources helped to make optimal decision. Traditional well integrity diagnostic includes temperature and passive acoustic data analysis that are associated with high uncertainty. A newer generation of array passive acoustic technology with enhanced sensitivity capabilities was deployed offshore Azerbaijan. A combination of array passive acoustics data, single point temperature and distributed fiber optic data have been acquired during a multi-well campaign. Extensive review of well integrity history, downhole and surface gauge data incorporated with passive acoustic data from arrays of spectral sensors in time and depth domain helped to refine the process and evolve into a unique interpretation methodology. The comprehensive interpretation accounted for integration of all available static and dynamic data such as: fluids and formation pressure distribution along the borehole, cement bond logs evaluation, annuli pressure and temperature, production and downhole gauge measurements, fibre optic data, temperature and passive acoustic logs. This helped to understand the low scale dynamic events behind the casing and make an informed decision on safe and reliable well operations. The sensitivity of array passive acoustic technology proved successful in detecting subtle acoustic events where conventional methods failed or had limited success. Successful results have been achieved by customizing the logging program using a multiple well evolutionary approach that improved data quality and saved rig time. Interpretation and decisions derived from each well involved multi-disciplinary well review panel sessions with specialists from subsurface & geohazards, drilling & completions, production & operations departments. Case studies presented in this paper describe the interpretation approach of highly sensitive array passive acoustic sensors in combination with available static and dynamic point and distributed data. The logging program and interpretation approach used in this article could be considered as a basis for future applications in wells with similar design.
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Contreras Perez, David Rafael, and Amal Almehrzi. "Data Visualization Workflow an Alternative Procedure to Quality Check Static Grids." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211607-ms.

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Abstract Static grids are commonly an ensemble of millions of reservoir property values. Analyzing this vast dataset is a challenging task and is usually performed via simple statistical parameters (e.g arithmetic mean and standard deviation). The objective of this paper is to present a quick and efficient way of analyzing static model realization results in a more visual, interactive and efficient manner allowing a multidisciplinary team to absorb information and key ideas quickly. The proposed methodology starts with a set of properties from a given static grid realization. The data are prepared with all necessary labeling and categorization to be analyzed (e.g. reservoir and segment indicators, well regions etc.). All kinds of data variations available in the static model are gathered and mobilized into dynamic data dashboards for further analysis. The interactive data visualization templates provide flexibility to filter information either by categories or data ranges. The filtering schemes are immediately propagated to all available plots in the dashboard highlighting common patterns from all the data relationships in the static model. The principle output from this methodology is an interactive data visualization panel that displays all necessary infographics related to reservoir data-trends. Starting from simple rock quality pie-charts, which can be mapped showing field-wide trends, porosity-permeability plots, to more detailed infographics of property ranges per well/reservoir indicator as well as summary tables of average properties per reservoirs/layers/segments etc. The ability to have all plots linked in a single display provides a simple but powerful platform to evaluate and interrogate data from the static grid using different views. Data can be aggregated in different ways and summarized accordingly. With this continuous process of data filtering, it is possible to quickly identify outliers and collect evidence of data inconsistency within the static grid. The additional value of the presented approach is the ability to compare multiple property realizations in a single template and summarize similarities and differences between different static realizations. The implementation of reservoir property dashboards proved to be an effective practice to increase the understanding of distributed properties inside static grids. A more comprehensive and detailed data quality check can be performed in multiple properties using a single view of the structured dataset. Additionally once data is loaded into the data visualization platform it can be easily shared with team members without the need of any specialized modeling software.
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Burger, Eric M., and Scott J. Moura. "ARX Model of a Residential Heating System With Backpropagation Parameter Estimation Algorithm." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5315.

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Model predictive control (MPC) strategies hold great potential for improving the performance and energy efficiency of building heating, ventilation, and air-conditioning (HVAC) systems. A challenge in the deployment of such predictive thermo-static control systems is the need to learn accurate models for the thermal characteristics of individual buildings. This necessitates the development of online and data-driven methods for system identification. In this paper, we propose an autoregressive with exogenous terms (ARX) model of a thermal zone within a building. To learn the model, we present a backpropagation approach for recursively estimating the parameters. Finally, we fit the linear model to data collected from a residential building with a forced-air heating and ventilation system and validate the accuracy of the trained model.
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Jeon, Soo. "State Estimation for Kinematic Model Over Lossy Network." In ASME 2010 Dynamic Systems and Control Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/dscc2010-4297.

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The major benefit of the kinematic Kalman filter (KKF), i.e., the state estimation based on kinematic model is that it is immune to parameter variations and unknown disturbances regardless of the operating conditions. In carrying out complex motion tasks such as the coordinated manipulation among multiple machines, some of the motion variables measured by sensors may only be available through the communication layer, which requires to formulate the optimal state estimator subject to lossy network. In contrast to standard dynamic systems, the kinematic model used in the KKF relies on sensory data not only for the output but also for the process input. This paper studies how the packet dropout occurring from the input sensor as well as the output sensor affects the performance of the KKF. When the output sensory data are delivered through the lossy network, it has been shown that the mean error covariance of the KKF is bounded for any non-zero packet arrival rate. On the other hand, if the input sensory data are subject to lossy network, the Bernoulli dropout model results in an unbounded mean error covariance. More practical strategy is to adopt the previous input estimate in case the current packet is dropped. For each case of packet dropout models, the stochastic characteristics of the mean error covariance are analyzed and compared. Simulation results are presented to illustrate the analytical results and to compare the performance of the time varying (optimal) filter gain with that of the static (sub-optimal) filter gain.
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Rau, Irfan Taufik, Julfree Sianturi, Azarya Hesron, and Aditya Suardiputra. "Automation of Well Correlation and Dynamic Synthesis for Efficient Reserves Estimation in Multi-Layered Oil and Gas Field." In SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205731-ms.

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Abstract The studied field was discovered in 1974 and has been in operation for nearly 50 years. Being deposited within a deltaic environment with enormous multi-layer sand-shale series, the field is vertically divided into dozens of geological layers. Previous reserves estimation method of manually performing dynamic synthesis followed by volumetric calculation per layer basis has become less preferable amid increasing drilling and well intervention activities. Meanwhile, reservoir simulation is also inapplicable for reserves estimation due to the field's subsurface complexity. This paper shares an approach to automate well correlation and dynamic synthesis process by integrating static and dynamic data into Visual Basic for Application (VBA) based tool in order to efficiently estimate reserves and accelerate candidate selection for new well drilling and well intervention. Performing dynamic synthesis on a certain reservoir within a well of interest involves estimation of latest fluid status, pressure, water risks, recovery factor, and drainage radius by analyzing recent static and dynamic data from surrounding wells. As the static data and dynamic data from hundreds of existing wells are available in separate databases, the study commences with collecting, updating, filtering, organizing and integrating data into one reliable database. Afterwards, the automation tool is designed to quantitatively mimic the logics of performing well correlation and dynamic synthesis using weighting factors that characterize the reliability of data based on 3 parameters: distance to the well of interest, recentness of data, and sand similarity. Since these parameters have distinctive influence depending on the dynamic property being estimated, influence factors are introduced for each parameter and each dynamic property through trial & error process. Combining weighting and influence factors with available data results in the estimated dynamic properties that become input to volumetric calculation of reserves. In order to validate the model and tool, blind tests are carried out using data from recently drilled wells which are not included in generating the estimation. Pressure blind test shows good correlation between predicted and realized values, meaning that the tool is able to predict pressure accurately. Reserves estimation blind test also shows satisfying results both at reservoir and well level. Following successful blind tests, the tool has been utilized to aid engineers in proposing new wells and well intervention candidates. As a result, 8 wells were able to be proposed in a timely manner for the sanction of future development. This paper presents an efficient, novel and robust approach in estimating reserves for heterogeneous fields where reservoir simulation is inapplicable. The tool also allows straightforward update when adding data from new wells. However, further study is required for estimation in less dense areas where the amount of surrounding wells and data are insufficient.
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Reports on the topic "Static and Dynamic Panel data Estimation"

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Baltagi, Badi H., Georges Bresson, Anoop Chaturvedi, and Guy Lacroix. Robust dynamic space-time panel data models using ε-contamination: An application to crop yields and climate change. CIRANO, January 2023. http://dx.doi.org/10.54932/ufyn4045.

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This paper extends the Baltagi et al. (2018, 2021) static and dynamic ε-contamination papers to dynamic space-time models. We investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, we consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner (1986)’s g-priors for the variance-covariance matrices. We propose a general “toolbox” for a wide range of specifications which includes the dynamic space-time panel model with random effects, with cross-correlated effects `a la Chamberlain, for the Hausman-Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using an extensive Monte Carlo simulation study, we compare the finite sample properties of our proposed estimator to those of standard classical estimators. We illustrate our robust Bayesian estimator using the same data as in Keane and Neal (2020). We obtain short run as well as long run effects of climate change on corn producers in the United States.
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Kimhi, Ayal, Barry Goodwin, Ashok Mishra, Avner Ahituv, and Yoav Kislev. The dynamics of off-farm employment, farm size, and farm structure. United States Department of Agriculture, September 2006. http://dx.doi.org/10.32747/2006.7695877.bard.

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Objectives: (1) Preparing panel data sets for both the United States and Israel that contain a rich set of farm attributes, such as size, specialization, and output composition, and farmers’ characteristics such as off-farm employment status, education, and family composition. (2) Developing an empirical framework for the joint analysis of all the endogenous variables of interest in a dynamic setting. (3) Estimating simultaneous equations of the endogenous variables using the panel data sets from both countries. (4) Analyzing, using the empirical results, the possible effects of economic policies and institutional changes on the dynamics of the farm sector. An added objective is analyzing structural changes in farm sectors in additional countries. Background: Farm sectors in developed countries, including the U.S. and Israel, have experienced a sharp decline in their size and importance during the second half of the 20th century. The overall trend is towards fewer and larger farms that rely less on family labor. These structural changes have been a reaction to changes in technology, in government policies, and in market conditions: decreasing terms of trade, increasing alternative opportunities, and urbanization pressures. As these factors continue to change, so does the structure of the agricultural sector. Conclusions: We have shown that all major dimensions of structural changes in agriculture are closely interlinked. These include farm efficiency, farm scale, farm scope (diversification), and off-farm labor. We have also shown that these conclusions hold and perhaps even become stronger whenever dynamic aspects of structural adjustments are explicitly modeled using longitudinal data. While the results vary somewhat in the different applications, several common features are observed for both the U.S. and Israel. First, the trend towards the concentration of farm production in a smaller number of larger farm enterprises is likely to continue. Second, at the micro level, increased farm size is negatively associated with increased off-farm labor, with the causality going both ways. Third, the increase in farm size is mostly achieved by diversifying farm production into additional activities (crops or livestock). All these imply that the farm sector converges towards a bi-modal farm distribution, with some farms becoming commercial while the remaining farm households either exit farming altogether or continue producing but rely heavily on off-farm income. Implications: The primary scientific implication of this project is that one should not analyze a specific farm attribute in isolation. We have shown that controlling for the joint determination of the various farm and household attributes is crucial for obtaining meaningful empirical results. The policy implications are to some extent general but could be different in the two countries. The general implication is that farm policy is an important determinant of structural changes in the farm sector. For the U.S., we have shown the different effects of coupled and decoupled (direct) farm payments on the various farm attributes, and also shown that it is important to take into account the joint farm-household decisions in order to conduct a meaningful policy analysis. Only this kind of analysis explains the indirect effect of direct farm payments on farm production decisions. For Israel, we concluded that farm policy (or lack of farm policy) has contributed to the fast structural changes we observed over the last 25 years. The sharp change of direction in farm policy that started in the early 1980s has accelerated structural changes that could have been smoother otherwise. These accelerated structural changes most likely lead to welfare losses in rural areas.
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