Time-series Cross-sectional Models Research Articles

Globalization, Threat and Religious Freedom

While arguably central to the human experience, religion is a largely understudied component of social life and of politics. The comparative literature on religion and politics is limited in scope, and offers mostly descriptions of trends. We know, for example, that restrictions on freedom of religion are on the rise worldwide. In our theoretical framework, the recently higher universal levels of globalization combine with other sources of threat to account for the trend away from religious freedom. As threat to the majority religion increases, due to globalization and an increasing number of minority religions, freedom of religion is on the decline. Data for two decades from 147 nations are used to test hypotheses. Time-series cross-sectional and mediation models estimated at different levels of analysis with data from two independent sources confirm that threat systematically accounts for changes in religious freedom, with globalization playing a key role.

Battlegrounds and Budgets

Based on prior research on political business cycles, we investigate the manipulation of state budgets in battleground states. The state-based winner-take-all Electoral College system in the United States makes certain states extremely politicized during presidential elections. We hypothesize that budgets will target specific groups of swing voters in these battleground states. We test the impact of competitive state status in presidential elections on state budgets from 1982 to 2005 by creating a cross-sectional time-series regression model of state budget priorities from Jacoby and Schneider. While controlling for known determinants of budgets, we find strong support for our hypothesis.

What's at stake? A veto-player theory of voter turnout

Turnout is theorized to reflect elections' policy stakes. All else equal, a highly constrained policymaking context is expected to lower the potential policy stakes of a given election. This study tests if such contexts, which are characterized by multiple veto players, reduce electoral participation. According to time-series cross-sectional autoregressive dynamic lag models of turnout in 311 elections in 21 advanced industrialized democracies, additional veto players decrease turnout in both the short and long run. Moreover, the results suggest veto players conceptually fine-tune and empirically contribute to existing models of cross-national turnout. Hence this study has crucial implications for the students of electoral participation and scholars interested in the democratic outcomes of institutional design.

Market Value of the Firms and R&D Investment: Theoretical Overview and Empirical Estimation for the Panel of Countries

The aim of this paper is to investigate the issue of R&D investment and the market value of the firm. This idea dating back from Arrow paper, later developed by Paul Romer, but in the area of economic growth. Zvi Griliches (1979), first introduced the production function, which later would be used in a vast literature from this area. In the theoretical section of this paper we are describing Tobin’s original model, and Abel’s (1984) model, this models relates Tobin’s quotient with intangible assets of the company. In the empirical part we develop cross-section time series model (Feasible Generalized Least Squares Model), for a panel of countries in Europe including UK and Turkey, in total of 11 panels. Later we test that model by estimating the marginal effects of R&D investment with Tobin’s q on a small economy such as R. Macedonia. The results exert positive and statistically significant relationship between market value of the firms and R&D investment.

Cross-Sectional Time Series and Multivariate Adaptive Regression Splines Models Using Accelerometry and Heart Rate Predict Energy Expenditure of Preschoolers

Prediction equations of energy expenditure (EE) using accelerometers and miniaturized heart rate (HR) monitors have been developed in older children and adults but not in preschool-aged children. Because the relationships between accelerometer counts (ACs), HR, and EE are confounded by growth and maturation, age-specific EE prediction equations are required. We used advanced technology (fast-response room calorimetry, Actiheart and Actigraph accelerometers, and miniaturized HR monitors) and sophisticated mathematical modeling [cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS)] to develop models for the prediction of minute-by-minute EE in 69 preschool-aged children. CSTS and MARS models were developed by using participant characteristics (gender, age, weight, height), Actiheart (HR+AC_x) or ActiGraph parameters (AC_x, AC_y, AC_z, steps, posture) [x, y, and z represent the directional axes of the accelerometers], and their significant 1- and 2-min lag and lead values, and significant interactions. Relative to EE measured by calorimetry, mean percentage errors predicting awake EE (−1.1 ± 8.7%, 0.3 ± 6.9%, and −0.2 ± 6.9%) with CSTS models were slightly higher than with MARS models (−0.7 ± 6.0%, 0.3 ± 4.8%, and −0.6 ± 4.6%) for Actiheart, ActiGraph, and ActiGraph+HR devices, respectively. Predicted awake EE values were within ±10% for 81–87% of individuals for CSTS models and for 91–98% of individuals for MARS models. Concordance correlation coefficients were 0.936, 0.931, and 0.943 for CSTS EE models and 0.946, 0.948, and 0.940 for MARS EE models for Actiheart, ActiGraph, and ActiGraph+HR devices, respectively. CSTS and MARS models should prove useful in capturing the complex dynamics of EE and movement that are characteristic of preschool-aged children.

Cross-National Heterogeneity in Retail Spending

Economies worldwide vary greatly in terms of how much their consumers spend on various types of retail activities. The purpose of this article is to examine how the regulatory characteristics as well as the natures and strategies of businesses are related to retail spending. Random effect time-series cross-sectional (TSCS) models linear in parameters were employed for forty-eight economies using annual data for the 1999–2008 period. The results provided strong support that economic freedom, foreign direct investment (FDI) inflow, and access and availability as measured by the density of retail stores positively affect retail spending. It was also found that tax and social security contributions as a proportion of the gross domestic product (GDP) is positively related to per capita grocery retail spending.

Motivated Selective Attention During Political Ad Processing

This study examines the dynamic, real-time interplay between the emotional content of political television ads and individuals’ political attitudes during ad processing based upon the Dynamic Motivational Activation (DMA) theoretical framework. Time-series cross-sectional models were developed to test the effects of three motivational inputs of emotional ads (arousing content, positivity, and negativity) and viewers’ evaluation of the featured candidates on four psychophysiological responses (heart rate, skin conductance level, corrugator electromyography, and zygomatic electromyography). As predicted by the DMA, physiological responses during ad viewing were affected by their own first- and second-order dynamic system feedback effects. These results not only support the predicted dynamic nature of the physiological system but also help disentangle message effects from the moderating and accumulating effects of the physiological system itself. Also as predicted, message motivational inputs interacted with viewers’ political attitudes to determine psychophysiological responses to the ads. Supporters of opposing political candidates showed cardiac-somatic response patterns indicative of disparate attention to the advertised information. Attentional selectivity can be a critical component in determining how information processing influences campaign message reception and effects.

Fiscal Policy and the Firm

The existing literature on the political economy of taxation explores how the mobility of firms affects the ability of governments to tax capital. In this article the author tests the relationship between corporate tax rates and multinational investment decisions in advanced, industrialized economies. He utilizes a time-series cross-sectional general error correction model to explore the impact of corporate taxation rates and foreign direct investment (FDI) inflows in up to 19 Organisation for Economic Co-operation and Development economies from 1980 to 2000. To mitigate potential endogeneity problems, the author’s identification strategy takes advantage of delays between the passage of tax reductions and the implementation of these policies. The author finds no relationship between corporate tax rates and flows of foreign direct investment. This finding has implications on the link between globalization and domestic politics.

The effect of financial liberalization on stock-return volatility in GCC markets

We estimate a cross-sectional time-series model to assess the impact of equity market liberalization and capital account openness on individual-firm stock return volatility in GCC (Gulf Cooperation Council) markets. We document evidence that international participation in local trades has no impact on idiosyncratic volatility and a rising impact on total volatility. In contrast, capital account openness significantly reduces total volatility, especially for stocks with low foreign ownership limits. Moreover, we find that the effect of restrictions on capital account transactions is stronger on capital inflows than outflows and on residents than nonresidents. The findings continue to hold for portfolio return volatility. Our results are important for GCC policy makers, portfolio managers, as well as academics.

State Methamphetamine Precursor Policies and Changes in Small Toxic Lab Methamphetamine Production

Domestic production of methamphetamine in small toxic labs (STLs) results in significant community safety and health consequences. This paper examines the effects of state-level policies implemented in the middle of the last decade in reaction to a rapid increase in STL labs. These policies focused on controlling access to the methamphetamine precursor chemicals ephedrine and pseudoephedrine and the relationship of such policies with actual STL seizure rates. Data include (a) primary legal research on state laws/regulations in all 50 states in effect as of October 1, 2005; and (b) STL seizure counts for 2004–2006. Results from random effects cross-sectional time-series regression models showed that states with the greatest reduction in STL seizures had comprehensive policies involving quantity limits on methamphetamine precursor purchases, clerk intervention requirements (such as requiring buyer identification) and regulatory agency specification for monitoring compliance and tracking multiple purchases. Criminalizing purchasing violations was not related to STL reductions.

Evaluating testing effectiveness during software evolution: a time‐series cross‐section approach

SUMMARYAs software evolution organizations (SEOs) formalize their activities into standard repeatable processes, capability maturity models suggest that they gather and analyze data to quantitatively manage their activities. This study examines the use of a time‐series cross‐section (TSCS) modeling approach to inform the quantitative management of the testing activities within a mature SEO. This paper describes the TSCS modeling approach and provides an exemplar case study that details its use. Data for the case study come from readily available, commonly gathered production and testing defect reports in the quantitative measurement efforts of ait CMMI‐DEV Level 3 assessed software development and evolution organization. Covering six independent projects for 43 months, the case study defines quality in terms of reported defects in the production code base. The predictors are the six projects and defect data identified in the unit, system, and regression testing activities logs. The case study also details how results from the TSCS analysis triggered causal analysis to improve the testing activities. The study provides evidence that TSCS models can be used as a quantitative problem identification tool for the mature (SMmm Level 3 or higher) SEO's testing activities. Copyright © 2011 John Wiley & Sons, Ltd.

Determinants of Age in Europe: A Pooled Multilevel Nested Hierarchical Time-Series Cross-Sectional Model

Age is often found to be associated with a plenitude of socioeconomic, politico-administrative, biological and thanatological variables. Much less attention has been paid by scholars, however, to explaining ‘age’. In this paper we address this unfortunate scientific lacuna by developing a model of ‘age’ as a function of several factors suggested by (post)rational choice and social constructionist theories. Using state-of-the-art multilevel statistical techniques, our analysis allows the determinants of age to vary with the institutional characteristics of European countries. Our findings convincingly show that generalized trust in strangers, support for incumbent extremist political parties in provincial elections held in the month of January, and the percentage of overqualified women in the cafeterias of national parliaments are all statistically significant explanations of ‘age’. Our findings have obvious implications for conspiracy theorists, organizational advisors, spin doctors and ordinary charlatans.

What to Do (and Not Do) with Time-Series Criss-Section Data

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.

Validation of Cross-Sectional Time Series and Multivariate Adaptive Regression Splines Models for the Prediction of Energy Expenditure in Children and Adolescents Using Doubly Labeled Water ,

Accurate, nonintrusive, and inexpensive techniques are needed to measure energy expenditure (EE) in free-living populations. Our primary aim in this study was to validate cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models based on observable participant characteristics, heart rate (HR), and accelerometer counts (AC) for prediction of minute-by-minute EE, and hence 24-h total EE (TEE), against a 7-d doubly labeled water (DLW) method in children and adolescents. Our secondary aim was to demonstrate the utility of CSTS and MARS to predict awake EE, sleep EE, and activity EE (AEE) from 7-d HR and AC records, because these shorter periods are not verifiable by DLW, which provides an estimate of the individual’s mean TEE over a 7-d interval. CSTS and MARS models were validated in 60 normal-weight and overweight participants (ages 5–18 y). The Actiheart monitor was used to simultaneously measure HR and AC. For prediction of TEE, mean absolute errors were 10.7 ± 307 kcal/d and 18.7 ± 252 kcal/d for CSTS and MARS models, respectively, relative to DLW. Corresponding root mean square error values were 305 and 251 kcal/d for CSTS and MARS models, respectively. Bland-Altman plots indicated that the predicted values were in good agreement with the DLW-derived TEE values. Validation of CSTS and MARS models based on participant characteristics, HR monitoring, and accelerometry for the prediction of minute-by-minute EE, and hence 24-h TEE, against the DLW method indicated no systematic bias and acceptable limits of agreement for pediatric groups and individuals under free-living conditions.

Assessing the Effects of Municipal Term Limits on Fiscal Policy in U.S. Cities

The arguments for and against term limits have been the subject of debate since the mid-1990s. Proponents claim term limits bring new energy and ideas to government, while opponents argue they shift power to special interests and the bureaucracy. Analyses of the true effects of term limits have been focused on their implementation at the state level. This article examines the effects of term limits on taxation, city employment, and spending on other public policies at the municipal level. Using data from the U.S. Census Bureau and pooled cross-sectional time series models, the authors find that the presence of term limits has only a modest effect on economic and distributive public policies in U.S. cities as a city’s economic milieu has a greater effect on taxation and policy expenditures.

Toward an institutional ecology of establishment of foreign firms in the Chinese construction industry

In the current study, an integration of insights from institutional theory and organization ecology is used to explain the relationship between industry-level ownership structure and the establishment of foreign invested firms in the Chinese construction industry. It is argued that in a stated-owned enterprises dominated environment, where the market forces are weak, legitimation is the major driving force harming the proliferation of foreign firms, whereas in a private-enterprise dominated environment, where the market forces are strong, competition is the major driving force inhibiting the viability of foreign firms. Thus, concentration of either state ownership, implying lower legitimation of the foreign firm form, or concentration of private ownership, triggering tough competition from domestic private firms, is hypothesized to have a negative impact on the number of foreign firms. Using a regional data set from 1994 to 2007, estimation of a cross-section–time series model largely confirms our theoretical claims.

Is Immigration Responsible for the Crime Drop? An Assessment of the Influence of Immigration on Changes in Violent Crime Between 1990 and 2000

The idea that immigration increases crime rates has historically occupied an important role in criminological theory and has been central to the public and political discourses and debates on immigration policy. In contrast to the common sentiment, some scholars have recently questioned whether the increase in immigration between 1990 and 2000 may have actually been responsible for part of the national decrease in crime during the 1990s. The current work evaluates the influence of immigration on crime in urban areas across the United States between 1990 and 2000. Drawing on U.S. Census and Uniform Crime Report data, I first use ordinary least squares regression models to assess the cross-sectional relationship between immigration patterns and rates of homicide and robbery among U.S. cities with populations of at least 50,000. Second, I employ pooled cross-sectional time-series models to determine how changes in immigration influenced changes in homicide and robbery rates between 1990 and 2000. In the ordinary least squares models, immigration is associated with higher levels of homicide and robbery. However, the pooled cross-sectional time-series models suggest that cities with the largest increases in immigration between 1990 and 2000 experienced the largest decreases in homicide and robbery during the same time period. The findings offer insights into the complex relationship between immigration and crime and suggest that growth in immigration may have been responsible for part of the precipitous crime drop of the 1990s.

How streetcars shaped suburbanization: a Granger causality analysis of land use and transit in the Twin Cities

This article presents a Granger causality analysis of the coupled development of population and streetcars in the Twin Cities of Minneapolis and St Paul Historic residence and network data were assembled for 1900‐1930, and linear crosssectional time-series models were estimated at both a tract and block level using these data. It is found that, in contrast with transportation systems that were expanded in response to increased demand, the rapid expansion of the streetcar system during the electric era has been driven by other forces and to a large extent led land development in the Twin Cities. The main forces that have driven this process include technological superiority, monopoly, close connections with real estate business and people’s reliance on the streetcar for mobility. Proximity to the streetcar is found to be a crucial factor that determines the distribution and development of residences: it is observed that residential density declines with the distance from streetcar lines, and significantly drops beyond a walkable distance; it is also observed that gaining a closer access to streetcar lines within 800m (about a half mile) predicts the increase in residential density to a significant extent.

Social, Not Physical, Infrastructure: The Critical Role of Civil Society in Disaster Recovery

Despite the tremendous destruction wrought by catastrophes, social science holds few quantitative assessments of explanations for the rate of recovery. This article illuminates four factors - damage, population density, human capital, and economic capital - thought to explain the variation in the pace of population recovery following disaster along with the popular but relatively untested factor of social capital. Using time-series cross-sectional models and propensity score matching, I test these approaches using new data from the rebuilding of 39 neighborhoods in Tokyo after its 1923 earthquake. Social capital, more than earthquake damage, population density, human capital, or economic capital, best predicts population recovery in post-quake Tokyo. These findings suggest new approaches for research on social capital and disasters along with public policy avenues for handling catastrophe.

The Effect of State Regulations on Truck-Crash Fatalities

To improve traffic safety, states limit truck length and weight, and some set lower speed limits for trucks than for other vehicles. We examined the impact of truck-specific restrictions and general traffic-safety policies on fatality rates from crashes involving large trucks. We used state-level data from 1991 to 2005 with a cross-sectional time-series model that controlled for several policy measures. We found that higher speed limits for cars and trucks contributed to higher fatality rates, but differential speed limits by vehicle type had no significant impact. Truck-length limitations reduced fatalities in crashes involving large trucks. Our model estimates suggested that if all states had adopted a speed limit of 55 miles per hour for all vehicles in 2005, an additional 561 fatalities would have been averted.