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Author: Wang, Xi
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 87
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Author: Wang, Xi
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 87
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Author: Donald W. K. Andrews
Publisher: Cambridge University Press
ISBN: 1139444603
Category : Business & Economics
Languages : en
Pages : 589
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This 2005 volume contains the papers presented in honor of the lifelong achievements of Thomas J. Rothenberg on the occasion of his retirement. The authors of the chapters include many of the leading econometricians of our day, and the chapters address topics of current research significance in econometric theory. The chapters cover four themes: identification and efficient estimation in econometrics, asymptotic approximations to the distributions of econometric estimators and tests, inference involving potentially nonstationary time series, such as processes that might have a unit autoregressive root, and nonparametric and semiparametric inference. Several of the chapters provide overviews and treatments of basic conceptual issues, while others advance our understanding of the properties of existing econometric procedures and/or propose others. Specific topics include identification in nonlinear models, inference with weak instruments, tests for nonstationary in time series and panel data, generalized empirical likelihood estimation, and the bootstrap.
Author: Takeshi Amemiya
Publisher: Cambridge University Press
ISBN: 9780521662468
Category : Business & Economics
Languages : en
Pages : 472
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This collection investigates parametric, semiparametric, nonparametric, and nonlinear estimation techniques in statistical modeling.
Author: Wenyu Zhou
Publisher:
ISBN:
Category :
Languages : en
Pages : 198
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This dissertation consists of four main chapters that study network social interaction models and panel models with grouped heterogeneity. Chapter 1 and Chapter 2 are representative work finished during my early exploration of economics. Chapter 3 and Chapter 4 are completed during the last two years of my Ph.D. studies. Chapter 1 studies a network social interaction model with heterogeneous links. I show that the endogenous and exogenous social interaction effects as well as the strength of network links are identified under some mild conditions. I adopt the nonlinear least squares method to estimate the unknown parameters using data of a single network. I also investigate the finite sample performance of the estimation method through Monte Carlo simulations and apply the model to analyze an online social network. Chapter 2 studies social interactions model with both in-group and out-group effects. The in-group effect follows the standard setup in the literature, while the out-group effect is introduced by assuming the economic outcome also depends on its out-group average value. I present a network game with limited information of outside groups that rationalizes the econometric model. I show that both effects are identified under a set of mild regularity conditions. I propose to estimate the model using the two-stage least squares (2SLS) method and establish the asymptotic normality of the estimators. The finite sample performance of the estimators are investigated through Monte Carlo simulations. Chapter 3 studies a semiparametric panel quantile regression model with grouped heterogeneity. The model can capture both time-variant and time-invariant effects of explanatory variables when group-specific heterogeneity directly affects the coefficients. A series-based estimation method is developed to estimate the parameters of interest and the group memberships. I investigate the asymptotic properties of the estimators and propose an information criterion to estimate the number of groups. The finite sample performance of the estimation method and the information criterion are investigated through Monte Carlo simulations. I apply the model to study the effect of foreign direct investment (FDI) on economic growth. My empirical findings show that FDI has large and significant heterogeneous effects on economic growth, especially for low-income countries, and such effect diminishes as the GDP per capita increases. None of these findings have been documented in previous literature. In Chapter 4 (joint with Hualei Shang), we study a nonparametric additive panel regression model with grouped heterogeneity. The model is a valuable extension to the heterogeneous panel model studied in Su et al. (2016). We propose to estimate the nonparametric components using a sieve-approximation-based C-Lasso method. We establish the asymptotic properties of the estimator and show that they enjoy the so-called oracle property. Besides, we present the decision rule for group classification and establish its consistency. A BIC-type information criterion is developed to determine the group pattern of each nonparametric component. We investigate the finite sample performance of the estimation method and the information criterion through Monte Carlo simulations. Results show that both work very well. Finally, we apply the model to study the demand for cigarettes in the United States using panel data of 46 states from 1963 to 1992.
Author:
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Category :
Languages : en
Pages :
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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 equilibri.
Author: Mei-Chiung Shih
Publisher:
ISBN:
Category :
Languages : en
Pages : 266
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Author:
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Fairouz M.Ali Elmagbri
Publisher:
ISBN:
Category :
Languages : en
Pages : 80
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Author: Takayuki Iguchi
Publisher:
ISBN:
Category : Statistics
Languages : en
Pages : 0
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Monitoring data arising from a process a practitioner desires to be in-control is a typical task in Statistical Process Control (SPC). These data are generated sequentially over time, and the goal of a SPC tool called a control chart is to detect an out-of-control process with as little delay as possible while also minimizing false alarms. The assumptions made on the data generating process guides control chart design. We consider a general task called profile monitoring wherein the data being monitored are a set of noisy responses and predictors, and we wish to detect changes in the functional relationship $f$ between these two. Ideally, the profile monitoring method should minimize the number and strength of the assumptions about the data as much as possible while being computationally fast and having both desirable sensitivity and specificity. As such we consider estimating the functional relationship $f$ in either a semiparametric or nonparametric model. The dissertation involves two completed projects and preliminary results in a promising direction. The first project proposes a semiparametric approach using a single-index model (SIM), where we monitor an $l_2$ based statistic on the parametric component called the index parameter. The SIM approach outcompetes its competitors in detection delay and is the first profile monitoring method to use SIMs to model $f$. The second project provides a nonparametric control chart that is fast, has small detection delay, and is able to avoid false alarms magnitudes better than what is found in the control chart literature. Typically, control charts are designed to achieve an in-control average run length (ARL) of 200 or 370. The proposed eigenvector perturbation control chart achieves an in-control ARL that is greater that $10^6$ while needing only a small (often mere a single) number of out-of-control observations to correctly flag an alarm. The dissertation concludes with a promising direction of research. Although the estimate of a SIM in the first project is computationally fast enough for many applications, it is insufficient for streaming data. The final project aims to fill this gap and we provide preliminary results demonstrating a faster approach that empirically gives the same predictive power as its competitor.
Author: Marie-Luce Taupin
Publisher:
ISBN:
Category :
Languages : en
Pages : 32
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