Longitudinal Data Analysis and Time Series Commons^™

Time Series Analysis For Psychological Research: Examining And Forecasting Change, Andrew T. Jebb, Louis Tay, Wei Wang, Qiming Huang

Publications and Research

Psychological research has increasingly recognized the importance of integrating temporal dynamics into its theories, and innovations in longitudinal designs and analyses have allowed such theories to be formalized and tested. However, psychological researchers may be relatively unequipped to analyze such data, given its many characteristics and the general complexities involved in longitudinal modeling. The current paper introduces time series analysis to psychological research, an analytic domain that has been essential for understanding and predicting the behavior of variables across many diverse fields. First, the characteristics of time series data are discussed. Second, different time series modeling techniques are surveyed that …

Using Spatiotemporal Methods To Fill Gaps In Energy Usage Interval Data, Kristin K. Graves

Theses and Dissertations

Researchers analyzing spatiotemporal or panel data, which varies both in location and over time, often find that their data has holes or gaps. This thesis explores alternative methods for filling those gaps and also suggests a set of techniques for evaluating those gap-filling methods to determine which works best.

The Effects Of Quantitative Easing In The United States: Implications For Future Central Bank Policy Makers, Matthew Q. Rubino

Senior Honors Projects, 2010-2019

The purpose of this thesis is to examine the effects of the Federal Reserve’s recent bond buying programs, specifically Quantitative Easing 1, Quantitative Easing 2, Operation Twist (or the Fed’s Maturity Extension Program), and Quantitative Easing 3. In this study, I provide a picture of the economic landscape leading up to the deployment of the programs, an overview of quantitative easing including each program’s respective objectives, and how and why the Fed decided to implement the programs. Using empirical analysis, I measure each program’s effectiveness by applying four models including a yield curve model, an inflation model, a money supply …

Estimation Of Heterogeneous Panels With Structural Breaks, Badi Baltagi

Center for Policy Research

This paper extends Pesaran's (2006) work on common correlated effects (CCE) estimators for large heterogeneous panels with a general multifactor error structure by allowing for unknown common structural breaks. Structural breaks due to new policy implementation or major technological shocks, are more likely to occur over a longer time span. Consequently, ignoring structural breaks may lead to inconsistent estimation and invalid inference. We propose a general framework that includes heterogeneous panel data models and structural break models as special cases. The least squares method proposed by Bai (1997a, 2010) is applied to estimate the common change points, and the consistency …

Marginal Structural Models: An Application To Incarceration And Marriage During Young Adulthood, Valerio Bacak, Edward Kennedy

Edward H. Kennedy

Advanced methods for panel data analysis are commonly used in research on family life and relationships, but the fundamental issue of simultaneous time-dependent confounding and mediation has received little attention. In this article the authors introduce inverse-probability-weighted estimation of marginal structural models, an approach to causal analysis that (unlike conventional regression modeling) appropriately adjusts for confounding variables on the causal pathway linking the treatment with the outcome. They discuss the need for marginal structural models in social science research and describe their estimation in detail. Substantively, the authors contribute to the ongoing debate on the effects of incarceration on marriage …

Estimation And Identification Of Change Points In Panel Models With Nonstationary Or Stationary Regressors And Error Term, Badi H. Baltagi, Chihwa Kao, Long Liu

Center for Policy Research

This paper studies the estimation of change point in panel models. We extend Bai (2010) and Feng, Kao and Lazarová (2009) to the case of stationary or nonstationary regressors and error term, and whether the change point is present or not. We prove consistency and derive the asymptotic distributions of the Ordinary Least Squares (OLS) and First Difference (FD) estimators. We find that the FD estimator is robust for all cases considered.

Studying The Effects Of Non Oil Exports On Targeted Economic Growth In Iranian 5th Development Plan: A Computable General Equilibrium Approach, Rasoul Bakhsi Dastjerdi Dr., Reza Moosavi Mohseni Dr., Somayye Jafari

Reza Moosavi Mohseni

we investigate the effects of non oil export on Iran’s economic growth using a computable general equilibrium (CGE) and study which tradable sectors has a larger share in reaching to targeted growth rate 8% in 5th socio economic development plan. We calibrate the model by GAMS (with emphasis on foreign trade sector). Numerical solution to the model is based on Iran’s social accounting matrix (SAM). Results show that 2.03% of targeted economic growth rate is achieved by encouraging a 6% growth in export. It also be mentioned that industry and mine sector in Iran, has more influence on growth than …

Copula Modelling Of Dependence In Multivariate Time Series, Michael S. Smith

Michael Stanley Smith