Preface.1. Introduction.1.1 Panel Data: Some Examples.1.2 Why Should We Use Panel Data? Their Benefits and Limitations.Note.2. The One-way Error Component Regression Model.2.1 Introduction.2.2 The Fixed Effects Model.2.3 The Random Effects Model.2.4 Maximum Likelihood Estimation.2.5 Prediction.2.6 Examples.2.7 Selected Applications.2.8 Computational Note.Notes.Problems.3. The Two-way Error Component Regression Model.3.1 Introduction.3.2 The Fixed Effects Model.3.3 The Random Effects Model.3.4 Maximum Likelihood Estimation.3.5 Prediction.3.6 Examples.3.7 Selected Applications.Notes.Problems.4. Test of Hypotheses with Panel Data.4.1 Tests for Poolability of the Data.4.2 Tests for Individual and Time Effects.4.3 Hausman's Specification Test.4.4 Further Reading.Notes.Problems.5. Heteroskedasticity and Serial Correlation in the Error Component Model.5.1 Heteroskedasticity.5.2 Serial Correlation.Notes.Problems.6. Seemingly Unrelated Regressions with Error Components.6.1 The One-way Model.6.2 The Two-way Model.6.3 Applications and Extensions.Problems.7. Simultaneous Equations with Error Components.7.1 Single Equation Estimation.7.2 Empirical Example: Crime in North Carolina.7.3 System Estimation.7.4 The Hausman and Taylor Estimator.7.5 Empirical Example: Earnings Equation Using PSID Data.7.6 Extensions.Notes.Problems.8. Dynamic Panel Data Models.8.1 Introduction.8.2 The Arellano and Bond Estimator.8.3 The Arellano and Bover Estimator.8.4 The Ahn and Schmidt Moment Conditions.8.5 The Blundell and Bond System GMM Estimator.8.6 The Keane and Runkle Estimator.8.7 Further Developments.8.8 Empirical Example: Dynamic Demand for Cigarettes.8.9 Further Reading.Notes.Problems.9. Unbalanced Panel Data Models.9.1 Introduction.9.2 The Unbalanced One-way Error Component Model.9.3 Empirical Example: Hedonic Housing.9.4 The Unbalanced Two-way Error Component Model.9.5 Testing for Individual and Time Effects Using Unbalanced Panel Data.9.6 The Unbalanced Nested Error Component Model.Notes.Problems.10. Special Topics.10.1 Measurement Error and Panel Data.10.2 Rotating Panels.10.3 Pseudo-panels.10.4 Alternative Methods of Pooling Time Series of Cross-section Data.10.5 Spatial Panels.10.6 Short-run vs Long-run Estimates in Pooled Models.10.7 Heterogeneous Panels.Notes.Problems.11. Limited Dependent Variables and Panel Data.11.1 Fixed and Random Logit and Probit Models.11.2 Simulation Estimation of Limited Dependent Variable Models with Panel Data.11.3 Dynamic Panel Data Limited Dependent Variable Models.11.4 Selection Bias in Panel Data.11.5 Censored and Truncated Panel Data Models.11.6 Empirical Applications.11.7 Empirical Example: Nurses' Labor Supply.11.8 Further Reading.Notes.Problems.12. Nonstationary Panels.12.1 Introduction.12.2 Panel Unit Roots Tests Assuming Cross-sectional Independence.12.3 Panel Unit Roots Tests Allowing for Cross-sectional Dependence.12.4 Spurious Regression in Panel Data.12.5 Panel Cointegration Tests.12.6 Estimation and Inference in Panel Cointegration Models.12.7 Empirical Example: Purchasing Power Parity.12.8 Further Reading.Notes.Problems.References.Index.

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Econometric Analysis of Panel Data

Preface1Difference Equations12Lag Operators253Stationary ARMA Processes434Forecasting725Maximum Likelihood Estimation1176Spectral Analysis1527Asymptotic Distribution Theory1808Linear Regression Models2009Linear Systems of Simultaneous Equations23310Covariance-Stationary Vector Processes25711Vector Autoregressions29112Bayesian Analysis35113The Kalman Filter37214Generalized Method of Moments40915Models of Nonstationary Time Series43516Processes with Deterministic Time Trends45417Univariate Processes with Unit Roots47518Unit Roots in Multivariate Time Series54419Cointegration57120Full-Information Maximum Likelihood Analysis of Cointegrated Systems63021Time Series Models of Heteroskedasticity65722Modeling Time Series with Changes in Regime677A Mathematical Review704B Statistical Tables751C Answers to Selected Exercises769D Greek Letters and Mathematical Symbols Used in the Text786Author Index789Subject Index792

https://sisis.rz.htw-berlin.de/inh2007/12357004.pdf

Time series analysis

An influential conventional wisdom holds that civil wars proliferated rapidly with the end of the Cold War and that the root cause of many or most of these has been ethnic and religious antagonisms. We show that the current prevalence of internal war is mainly the result of a steady accumulation of protracted conflicts since the 1950s and 1960s rather than a sudden change associated with a new, post-Cold War international system. We also find that after controlling for per capita income, more ethnically or religiously diverse countries have been no more likely to experience significant civil violence in this period. We argue for understanding civil war in this period in terms of insurgency or rural guerrilla warfare, a particular form of military practice that can be harnessed to diverse political agendas. The factors that explain which countries have been at risk for civil war are not their ethnic or religious characteristics but rather the conditions that favor insurgency. These include poverty—which marks financially and bureaucratically weak states and also favors rebel recruitment—political instability, rough terrain, and large populations.We wish to thank the many people who provided comments on earlier versions of this paper in a series of seminar presentations. The authors also gratefully acknowledge the support of the National Science Foundation (Grants SES-9876477 and SES-9876530); support from the Center for Advanced Study in the Behavioral Sciences with funds from the William and Flora Hewlett Foundation; valuable research assistance from Ebru Erdem, Nikolay Marinov, Quinn Mecham, David Patel, and TQ Shang; sharing of data by Paul Collier.

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Ethnicity, Insurgency, and Civil War

We examine some issues in the estimation of time-series cross-section models, calling into question the conclusions of many published studies, particularly in the field of comparative political economy. We show that the generalized least squares approach of Parks produces standard errors that lead to extreme overconfidence, often underestimating variability by 50% or more. We also provide an alternative estimator of the standard errors that is correct when the error structures show complications found in this type of model. Monte Carlo analysis shows that these “panel-corrected standard errors” perform well. The utility of our approach is demonstrated via a reanalysis of one “social democratic corporatist” model.

/pdf/what-to-do-and-not-to-do-with-time-series-cross-section-data-3xne92hvli.pdf

What to do (and not to do) with time-series cross-section data

Background The prevalence of obesity has increased substantially over the past 30 years. We performed a quantitative analysis of the nature and extent of the person-to-person spread of obesity as a possible factor contributing to the obesity epidemic. Methods We evaluated a densely interconnected social network of 12,067 people assessed repeatedly from 1971 to 2003 as part of the Framingham Heart Study. The bodymass index was available for all subjects. We used longitudinal statistical models to examine whether weight gain in one person was associated with weight gain in his or her friends, siblings, spouse, and neighbors. Results Discernible clusters of obese persons (body-mass index [the weight in kilograms divided by the square of the height in meters], ≥30) were present in the network at all time points, and the clusters extended to three degrees of separation. These clusters did not appear to be solely attributable to the selective formation of social ties among obese persons. A person’s chances of becoming obese increased by 57% (95% confidence interval [CI], 6 to 123) if he or she had a friend who became obese in a given interval. Among pairs of adult siblings, if one sibling became obese, the chance that the other would become obese increased by 40% (95% CI, 21 to 60). If one spouse became obese, the likelihood that the other spouse would become obese increased by 37% (95% CI, 7 to 73). These effects were not seen among neighbors in the immediate geographic location. Persons of the same sex had relatively greater influence on each other than those of the opposite sex. The spread of smoking cessation did not account for the spread of obesity in the network. Conclusions Network phenomena appear to be relevant to the biologic and behavioral trait of obesity, and obesity appears to spread through social ties. These findings have implications for clinical and public health interventions.

/pdf/the-spread-of-obesity-in-a-large-social-network-over-32-2y3wfpgr5w.pdf

The Spread of Obesity in a Large Social Network over 32 Years

Researchers typically analyze time-series-cross-section data with a binary dependent
variable (BTSCS) using ordinary logit or probit. However, BTSCS observations are
likely to violate the independence assumption of the ordinary logit or probit statistical
model. It is well known that if the observations are temporally related that the results of
an ordinary logit or probit analysis may be misleading. In this paper, we provide a simple
diagnostic for temporal dependence and a simple remedy. Our remedy is based on the
idea that BTSCS data are identical to grouped duration data. This remedy does not require
the BTSCS analyst to acquire any further methodological skills, and it can be easily
implemented in any standard statistical software package. While our approach is suitable
for any type of BTSCS data, we provide examples and applications from the field of
International Relations, where BTSCS data are frequently used. We use our methodology
to reassess Oneal and Russett's (1997) findings regarding the relationship between economic
interdependence, democracy, and peace. Our analyses show that (1) their finding
that economic interdependence is associated with peace is an artifact of their failure to
account for temporal dependence yet (2) their finding that democracy inhibits conflict is
upheld even taking duration dependence into account.

Taking Time Seriously: Time-Series-Cross-Section Analysis with a Binary Dependent Variable

Event History Modeling, first published in 2004, provides an accessible guide to event history analysis for researchers and advanced students in the social sciences. The substantive focus of many social science research problems leads directly to the consideration of duration models, and many problems would be better analyzed by using these longitudinal methods to take into account not only whether the event happened, but when. The foundational principles of event history analysis are discussed and ample examples are estimated and interpreted using standard statistical packages, such as STATA and S-Plus. Critical innovations in diagnostics are discussed, including testing the proportional hazards assumption, identifying outliers, and assessing model fit. The treatment of complicated events includes coverage of unobserved heterogeneity, repeated events, and competing risks models. The authors point out common problems in the analysis of time-to-event data in the social sciences and make recommendations regarding the implementation of duration modeling methods.

Event History Modeling: A Guide for Social Scientists

In a previous article we showed that ordinary least squares with panel
corrected standard errors is superior to the Parks generalized least
squares approach to the estimation of time-series-cross-section models.
In this article we compare our proposed method with another leading
technique, Kmenta's "cross-sectionally heteroskedastic and timewise autocorrelated"
model. This estimator uses generalized least squares to
correct for both panel heteroskedasticity and temporally correlated errors.
We argue that it is best to model dynamics via a lagged dependent
variable rather than via serially correlated errors. The lagged dependent
variable approach makes it easier for researchers to examine dynamics
and allows for natural generalizations in a manner that the serially correlated
errors approach does not. We also show that the generalized
least squares correction for panel heteroskedasticity is, in general, no
improvement over ordinary least squares and is, in the presence of parameter
heterogeneity, inferior to it. In the conclusion we present a
unified method for analyzing time-series-cross-section data.

Nuisance vs. Substance: Specifying and Estimating Time-Series-Cross-Section Models

▪ Abstract This article treats the analysis of “time-series–cross-section” (TSCS) data, which has become popular in the empirical analysis of comparative politics and international relations (IR). Such data consist of repeated observations on a series of fixed (nonsampled) units, where the units are of interest in themselves. An example of TSCS data is the post–World War II annual observations on the political economy of OECD nations. TSCS data are also becoming more common in IR studies that use the “dyad-year” design; such data are often complicated by a binary dependent variable (the presence or absence of dyadic conflict). Among the issues considered here are estimation and specification. I argue that treating TSCS issues as an estimation nuisance is old-fashioned; those wishing to pursue this approach should use ordinary least squares with panel correct standard errors rather than generalized least squares. A modern approach models dynamics via a lagged dependent variable or a single equation error c...

Nathaniel Beck

Papers

What to do (and not to do) with time-series cross-section data

Taking Time Seriously: Time-Series-Cross-Section Analysis with a Binary Dependent Variable

Event History Modeling: A Guide for Social Scientists

Nuisance vs. Substance: Specifying and Estimating Time-Series-Cross-Section Models

TIME-SERIES–CROSS-SECTION DATA: What Have We Learned in the Past Few Years?