Multifactor error structures utilize factor analysis to deal with complex cross-sectional dependence in Time-Series Cross-Sectional data caused by cross-level interactions. The multifactor error structure specification is a generalization of the fixed-effects model. This paper extends the existing multifactor error models from panel econometrics to multilevel modeling, from linear setups to generalized linear models with the probit and logistic links, and from assuming serial independence to modeling the error dynamics with an autoregressive process. I develop Markov Chain Monte Carlo algorithms mixed with a rejection sampling scheme to estimate the multilevel multifactor error structure model with a p-th order autoregressive process in linear, probit, and logistic specifications. I conduct several Monte Carlo studies to compare the performance of alternative specifications and approaches with varying degrees of data complication and different sample sizes. The Monte Carlo studies provide guidance on when and how to apply the proposed model. An empirical application sovereign default demonstrates how the proposed approach can accommodate a complex pattern of cross-sectional dependence and helps answer research questions related to units' sensitivity or vulnerability to systemic shocks.

Applications of modern methods for analyzing data with missing values, based primarily on multiple imputation, have in the last half-decade become common in American politics and political behavior. Scholars in these fields have thus increasingly avoided the biases and inefficiencies caused by ad hoc methods like listwise deletion and best guess imputation. However, researchers in much of comparative politics and international relations, and others with similar data, have been unable to do the same because the best available imputation methods work poorly with the time-series cross-section data structures common in these fields. We attempt to rectify this situation. First, we build a multiple i mputation model that allows smooth time trends, shifts across cross-sectional units, and correlations over time and space, resulting in far more accurate imputations. Second, we build nonignorable missingness models by enabling analysts to incorporate knowledge from area studies experts via priors on individual missing cell values, rather than on difficult-to-interpret model parameters. Third, since these tasks could not be accomplished within existing imputation algorithms, in that they cannot handle as many variables as needed even in the simpler cross-sectional data for which they were designed, we also develop a new algorithm that substantially expands the range of computationally feasible data types and sizes for which multiple imputation can be used. These developments also made it possible to implement the methods introduced here in freely available open source software that is considerably more reliable than existing strategies. These developments also made it possible to implement the methods introduced here in freely available open source software, Amelia II: A Program for Missing Data, that is considerably more reliable than existing strategies. See also: Missing Data

Matching methods improve the validity of causal inference by reducing model dependence and offering intuitive diagnostics. While they have become a part of the standard tool kit across disciplines, matching methods are rarely used when analyzing time-series cross-sectional data. We fill this methodological gap. In the proposed approach, we first match each treated observation with control observations from other units in the same time period that have an identical treatment history up to the pre-specified number of lags. We use standard matching and weighting methods to further refine this matched set so that the treated and matched control observations have similar covariate values. Assessing the quality of matches is done by examining covariate balance. Finally, we estimate both short-term and long-term average treatment effects using the difference-in-differences estimator, accounting for a time trend. We illustrate the proposed methodology through simulation and empirical studies. An open-source software package is available for implementing the proposed methods.

R Scripts contain statistical data analisys for streamflow and sediment data, including Flow Duration Curves, Double Mass Analysis, Nonlinear Regression Analysis for Suspended Sediment Rating Curves, Stationarity Tests and include several plots.

What is the relationship between environment and democracy? The framework of cultural evolution suggests that societal development is an adaptation to ecological threats. Pertinent theories assume that democracy emerges as societies adapt to ecological factors such as higher economic wealth, lower pathogen threats, less demanding climates, and fewer natural disasters. However, previous research confused within-country processes with between-country processes and erroneously interpreted between-country findings as if they generalize to within-country mechanisms. In this article, we analyze a time-series cross-sectional dataset to study the dynamic relationship between environment and democracy (1949-2016), accounting for previous misconceptions in levels of analysis. By separating within-country processes from between-country processes, we find that the relationship between environment and democracy not only differs by countries but also depends on the level of analysis. Economic wealth predicts increasing levels of democracy in between-country comparisons, but within-country comparisons show that democracy declines as countries become wealthier over time. This relationship is only prevalent among historically wealthy countries but not among historically poor countries, whose wealth also increased over time. By contrast, pathogen prevalence predicts lower levels of democracy in both between-country and within-country comparisons. Our longitudinal analyses identifying temporal precedence reveal that not only reductions in pathogen prevalence drive future democracy, but also democracy reduces future pathogen prevalence and increases future wealth. These nuanced results contrast with previous analyses using narrow, cross-sectional data. As a whole, our findings illuminate the dynamic process by which environment and democracy shape each other.

Regression results for baseline model and alternative specifications.

The QoG Institute is an independent research institute within the Department of Political Science at the University of Gothenburg. Overall 30 researchers conduct and promote research on the causes, consequences and nature of Good Governance and the Quality of Government - that is, trustworthy, reliable, impartial, uncorrupted and competent government institutions. The primary aim of QoG is to conduct and promote research on corruption. One aim of the QoG Institute is to make publicly available cross-national comparative data on QoG and its correlates.The aim of the QoG Social Policy Dataset is to promote cross-national comparative research on social policy output and its correlates, with a special focus on the connection between social policy and Quality of Government (QoG).

The main objective of our research is to address the theoretical and empirical problem of how political institutions of high quality can be created and maintained. A second objective is to study the effects of Quality of Government on a number of policy areas, such as health, the environment, social policy, and poverty.

The dataset was created as part of a research project titled “Quality of Government and the Conditions for Sustainable Social Policy”. The aim of the dataset is to promote cross-national comparative research on social policy output and its correlates, with a special focus on the connection between social policy and Quality of Government (QoG).

The data comes in three versions: one cross-sectional dataset, and two cross-sectional time-series datasets for a selection of countries. The two combined datasets are called “long” (year 1946-2009) and “wide” (year 1970-2005).

The data contains six types of variables, each provided under its own heading in the codebook: Social policy variables, Tax system variables, Social Conditions, Public opinion data, Political indicators, Quality of government variables.

QoG Social Policy Dataset can be downloaded from the Data Archive of the QoG Institute at http://qog.pol.gu.se/data/datadownloads/data-archive Its variables are now included in QoG Standard.

Samanni, Marcus. Jan Teorell, Staffan Kumlin, Stefan Dahlberg, Bo Rothstein, Sören Holmberg & Richard Svensson. 2012. The QoG Social Policy Dataset, version 4Apr12. University of Gothenburg:The Quality of Government Institute. http://www.qog.pol.gu.se

The dataset includes aggregate immigration opinions in 13 West European countries (Austria, Belgium, Denmark, France, Germany, Great Britain, Ireland, Italy, the Netherlands, Norway, Portugal, Sweden and Switzerland). The estimations are the result of a dyadic ratios algorithm.

This study is part of the American National Election Study (ANES), a time-series collection of national surveys fielded continuously since 1948. The American National Election Studies are designed to present data on Americans' social backgrounds, enduring political predispositions, social and political values, perceptions and evaluations of groups and candidates, opinions on questions of public policy, and participation in political life. As with all Time Series studies conducted during years of presidential elections, respondents were interviewed during the two months preceding the November election (Pre-election interview), and then re-interviewed during the two months following the election (Post-election interview). Like its predecessors, the 2020 ANES was divided between questions necessary for tracking long-term trends and questions necessary to understand the particular political moment of 2020. The study maintains and extends the ANES time-series 'core' by collecting data on Americans' basic political beliefs, allegiances, and behaviors, which are so critical to a general understanding of politics that they are monitored at every election, no matter the nature of the specific campaign or the broader setting. This 2020 ANES study features a fresh cross-sectional sample, with respondents randomly assigned to one of three sequential mode groups: web only, mixed web (i.e., web and phone), and mixed video (i.e., video, web, and phone). The new content for the 2020 pre-election survey includes coronavirus pandemic, election integrity, corruption, impeachment, immigration and democratic norms. The pre-election survey also includes protests and unrest over policing and racism. The new content for the 2020 post-election survey includes voting experiences, anti-elitism, faith in experts or science, climate change, gun control, opioids, rural-urban identity, international trade, transgender military service, social media usage, misinformation, perceptions of foreign countries and group empathy. Phone and video interviews were conducted by trained interviewers using computer-assisted personal interviewing (CAPI) software on computers. Unlike in earlier years, the 2020 ANES did not use computer-assisted self interviewing (CASI) during any part of the interviewer-administered modes (video and phone). Rather, in interviewer-administered modes, all questions were read out loud to respondents, and respondents also provided their answers orally. Demographic variables include respondent age, education level, political affiliation, race/ethnicity, marital status, and family composition.

Companion files for: 2014. Jessica Fortin-Rittberger. “Time-Series Cross-Section” in Henning Best and Christof Wolf (Eds.), The SAGE Handbook of Regression Analysis and Causal Inference, Sage Publishers. DOI: http://dx.doi.org/10.4135/9781446288146.n17 data file (Norris, P. (2009). Democracy timeseries data release 3.0. http://www.pippanorris.com/) and Stata do file

(C, V, S, K) index time series data generated using 0.5-Hz GEMS time series data from Taiwan. In this data set, C is a modified autocorrelation function, V is the variance, S the skewness, and K the kurtosis of the GEMS geo-electric field time series.

The data and programs replicate tables and figures from "Income convergence among U.S. states: cross-sectional and time series evidence", by Heckelman. Please see the ReadMe file for additional details.

In the ANES Time Series Cumulative Data File, the project staff have merged into a single file all cross-section cases and variables for select questions from the ANES Time Series studies conducted since 1948. Questions that have been asked in three or more Time Series studies are eligible for inclusion, with variables recoded as necessary for comparability across years.

The data track political attitudes and behaviors across the decades, including attitudes about religion. This dataset is unique given its size and comprehensive assessment of politics and religion over time. For information about the structure of the cumulative file, please see the notes listed on this page.

Enns and Koch (2015) question the validity of the Berry et al. (1998) measure of state policy mood and defend the validity of the Enns and Koch measure on two grounds. First, they claim policy mood has become more conservative in the South over time; we present empirical evidence to the contrary: policy mood became more liberal in the South between 1980 and 2010. Second, Enns and Koch (2015) argue that an indicator’s lack of face validity in cross-sectional comparisons is irrelevant when judging the measure’s suitability in the most common form of pooled cross-sectional time-series analysis. We show their argument is logically flawed, except under highly improbable circumstances. We also demonstrate, by replicating several published studies, that statistical results about the effect of state policy mood can vary dramatically depending on which of the two mood measures is used, making clear that a researcher’s measurement choice can be highly consequential.

We propose a new nonlinear time series model of expected returns based on the dynamics of the cross-sectional rank of realized returns. We model the joint dynamics of a sharp jump in the cross-sectional rank and the asset return by analyzing (1) the marginal probability distribution of a jump in the cross-sectional rank within the context of a duration model, and (2) the probability distribution of the asset return conditional on a jump, for which we specify different dynamics depending upon whether or not a jump has taken place. As a result, the expected returns are generated by a mixture of normal distributions weighted by the probability of jumping. The model is estimated for the weekly returns of the constituents of the SP500 index from 1990 to 2000, and its performance is assessed in an out-of-sample exercise from 2001 to 2005. Based on the one-step-ahead forecast of the mixture model we propose a trading rule, which is evaluated according to several forecast evaluation criteria and compared to 18 alternative trading rules. We find that the proposed trading strategy is the dominant rule by providing superior risk-adjusted mean trading returns and accurate value-at-risk forecasts.

This is a data set related to a bedload tracer field study in an alpine section of the Drava River between 11 May 2017 and 11 June 2018. A time series of bed shear stress is provided for the seeding site of the tracers in the time span of the entire tracer study. The shear stress was calculated from water depths that were modelled with a one-dimensional hydrodynamic-numerical model and based on a channel slope obtained from the analysis of cross-sections. The shear stress can also be calculated for cross-sections downstream of the seeding location by using the functions available in the corresponding publication.

The media analysis data was collected for commercial purposes. They are used in media planning as well as in the advertising planning of the different media genres (radio, press media, TV, poster and since 2010 also online). They are cross-sections that are merged together for one year. ag.ma kindly provides the data for scientific use on an annual basis – with a two-year notice period – to GESIS. In addition, agof has provided documentation regarding data collection (questionnaires, code plans, etc.) for the preparation of the MA IntermediaPlus online bundle. In order to make the data accessible for scientific use, the datasets of the individual years were harmonized and pooled into a longitudinal data set starting in 2014 as part of the dissertation project ´Audience and Market Fragmentation online´ of the Digital Society research program NRW at the Heinrich-Heine-University (HHU) and the University of Applied Sciences Düsseldorf (HSD), funded by the Ministry of Culture and Science of the German State of North Rhine-Westphalia. The prepared Longitudinal IntermediaPlus dataset 2014 to 2016 is a ´big data´, which is why the entire dataset will only be available in the form of a database (MySQL). In this database, the information of different variables of a respondent is organized in one column, one row per variable. The present data documentation shows the total database for online media use of the years 2014 to 2016. The data contains all variables of socio demography, free-time activities, additional information on a respondent and his household as well as the interview-specific variables and weights. Only the variables concerning the respondent´s media use are a selection: The online media use of all full online as well as their single entities for all genres whose business model is the provision of content is included - e-commerce, games, etc. were excluded. The media use of radio, print and TV is not included. Preparation for further years is possible, as is the preparation of cross-media media use for radio, press media and TV. Harmonization is available for radio and press media up to 2015 waiting to be applied. The digital process chain developed for data preparation and harmonization is published at GESIS and available for further projects updating the time series for further years. Recourse to these documents - Excel files, scripts, harmonization plans, etc. - is strongly recommended. The process and harmonization for the Longitudinal IntermediaPlus for 2014 to 2016 database was made available in accordance with the FAIR principles (Wilkinson et al. 2016). By harmonizing and pooling the cross-sectional datasets to one longitudinal dataset – which is being carried out by Inga Brentel and Céline Fabienne Kampes as part of the dissertation project ´Audience and Market Fragmentation online´ –, the aim is to make the data source of the media analysis, accessible for research on social and media change in Germany. Die Media-Analyse Daten wurden zu kommerziellen Zwecken erhoben. Sie werden in der Mediaplanung sowie der Werbeplanung der unterschiedlichen Mediengattungen (Radio, Pressemedien, TV, Plakat und seit 2010 auch Online) eingesetzt. Es handelt sich um Querschnitte, die für ein Jahr aneinandergereiht werden. Die ag.ma stellt freundlicherweise jährlich – mit einer Frist von zwei Jahren – die entsprechenden Daten der GESIS zur wissenschaftlichen Nutzung bereit. Zusätzlich hat die agof für die Aufbereitung der Online-Tranche der MA IntermediaPlus Unterlagen bezüglich der Datenerhebung (Fragebögen, Codepläne, usw.) bereitgestellt. Um die Daten für die wissenschaftliche Nutzung zugänglich zu machen, wurden ab 2018 im Rahmen des Dissertationsprojektes „Angebots- und Publikumsfragmentierung online“ des Graduiertenkollegs Digitale Gesellschaft NRW an der Heinrich-Heine-Universität (HHU) sowie der Hochschule Düsseldorf (HSD) gefördert durch das Ministerium für Kultur und Wissenschaft des Landes Nordrhein-Westfalen die Datensätze der einzelnen Jahre zu einem Längsschnitt-Datensatz ab 2014 harmonisiert. Bei dem aufbereiteten Längsschnitt-Datensatz 2014 bis 2016 handelt es sich um „Big-Data“, weshalb der Gesamtdatensatz nur in Form einer Datenbank (MySQL) verfügbar ist. In dieser Datenbank liegt die Information verschiedener Variablen eines Befragten untereinander. Die vorliegende Datendokumentation zeigt den Gesamtdatensatz für die Jahre 2014 bis 2016 für die Online-Mediennutzung. Folgende Variablengruppen wurden neben der Soziodemografie im Rahmen der vorliegenden Studie erhoben bzw. für den Längsschnittdatensatz harmonisiert: Freizeitverhalten, Zusatzinformation zum Befragten und dessen Haushalt wie Geräte im Haushalt, Online-Mediennutzung Content sowie interviewspezifische Variablen und Gewichte. Lediglich bei den Variablen bezüglich der Mediennutzung des Befragten, handelt es sich um eine Auswahl: es ist ausschließlich die Onlinemediennutzung aller Gesamtangebote sowie der Einzelangebote aller Genre, deren Geschäftsmodell auf der Bereitstellung von Inhalten (Content) basiert, aufgenommen – E-Commerce, Spiele, etc. wurden ausgeschlossen. Die Mediennutzung von Radio, Print und TV wurde nicht berücksichtigt. Eine Aufbereitung für weitere Jahre nach 2017 ist grundsätzlich möglich, ebenso die Aufbereitung crossmedialer Mediennutzung für Radio, Pressemedien und TV. Unterlagen zur Harmonisierung liegen für Radio und Pressemedien bis 2015 vor. Die erarbeitete digitale Prozesskette zur Datenaufbereitung und -harmonisierung ist bei GESIS publiziert und für weitere Aufbereitungsschritte verfügbar. Der Rückgriff auf diese Unterlagen – Excel-Dateien, Skripte, Harmonisierungspläne, usw. – wird ausdrücklich empfohlen. Die Aufbereitung und Harmonisierung des Längsschnitts des Gesamtdatensatzes der MA IntermediaPlus für 2014 bis 2016 erfolgte unter Berücksichtigung der FAIR-Prinzipien (Wilkinson et al. 2016). Ziel ist es durch die Harmonisierung der einzelnen Querschnitte die Datenquelle der Media-Analyse, die im Rahmen des Dissertationsprojektes „Angebots- und Publikumsfragmentierung online“ durch Inga Brentel und Céline Fabienne Kampes erfolgte, für Forschung zum sozialen und medialen Wandel in der Bundesrepublik Deutschland zugänglich zu machen.

These are the data for the replication of the statistical analysis in the article "The Enactment of Public Participation in Rulemaking: A Comparative Analysis". The dataset contains time-series (1995-2015) cross-sectional (39 OECD countries) observations, in csv format which was created for the purpose of explaining the adoption of legislations allowing public participation in and judicial review of rulemaking. The corresponding codebook lists the used variables and sources. The replication codes are for Stata.

Abstract (en): This collection contains an array of economic time series data pertaining to the United States, the United Kingdom, Germany, and France, primarily between the 1920s and the 1960s, and including some time series from the 18th and 19th centuries. These data were collected by the National Bureau of Economic Research (NBER), and they constitute a research resource of importance to economists as well as to political scientists, sociologists, and historians. Under a grant from the National Science Foundation, ICPSR and the National Bureau of Economic Research converted this collection (which existed heretofore only on handwritten sheets stored in New York) into fully accessible, readily usable, and completely documented machine-readable form. The NBER collection -- containing an estimated 1.6 million entries -- is divided into 16 major categories: (1) construction, (2) prices, (3) security markets, (4) foreign trade, (5) income and employment, (6) financial status of business, (7) volume of transactions, (8) government finance, (9) distribution of commodities, (10) savings and investments, (11) transportation and public utilities, (12) stocks of commodities, (13) interest rates, and (14) indices of leading, coincident, and lagging indicators, (15) money and banking, and (16) production of commodities. Data from all categories are available in Parts 1-22. The economic variables are usually observations on the entire nation or large subsets of the nation. Frequently, however, and especially in the United States, separate regional and metropolitan data are included in other variables. This makes cross-sectional analysis possible in many cases. The time span of variables in these files may be as short as one year or as long as 160 years. Most data pertain to the first half of the 20th century. Many series, however, extend into the 19th century, and a few reach into the 18th. The oldest series, covering brick production in England and Wales, begins in 1785, and the most recent United States data extend to 1968. The unit of analysis is an interval of time -- a year, a quarter, or a month. The bulk of observations are monthly, and most series of monthly data contain annual values or totals. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Standardized missing values.; Checked for undocumented or out-of-range codes.. Time series of economic statistics pertaining to France, Germany, the United Kingdom, and the United States between 1785 and 1968. 2007-03-26 This study, updated from OSIRIS, now includes SAS, SPSS, and Stata setup files, SAS transport (XPORT) files, SPSS portable files, a Stata system files, and an updated codebook. Funding insitution(s): National Science Foundation. The data were collected between the 1920s and the 1970s, but it is unclear from the documentation as to the exact start and end dates.