Researchers of time series cross sectional (TSCS) data regularly face the change-point problem, which re- quires them to discern between significant parametric shifts that can be deemed structural changes and minor parametric shifts that must be considered noise. In this paper, we develop a general Bayesian method for change-point detection in high dimensional data and present its application in the context of the fixed-effect model. Our proposed method, hidden Markov Bayesian bridge model (HMBB), jointly estimates high dimensional regime-specific parameters and hidden regime transitions in a unified way. We apply our method to Alvarez, Garrett, and Lange (1991)’s study of the relationship between government partisanship and economic growth and Allee and Scalera (2012)’s study of membership effects in international organizations. In both applications, we found that the proposed method successfully identify substantively meaningful temporal heterogeneity in parameters of regression models.

While fixed effects (FE) models are often employed to address potential omitted variables, we argue that these models’ real utility is in isolating a particular dimension of variance from panel data for analysis. In addition, we show through novel mathematical decomposition and simulation that only one-way FE models cleanly capture either the over-time or cross-sectional dimensions in panel data, while the two-way FE model unhelpfully combines within-unit and cross-sectional variation in a way that produces un-interpretable answers. In fact, as we show in this paper, if we begin with the interpretation that many researchers wrongly assign to the two-way FE model—that it represents a single estimate of X on Y while accounting for unit-level heterogeneity and time shocks—the two-way FE specification is statistically unidentified, a fact that statistical software packages like R and Stata obscure through internal matrix processing.

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.

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 ...

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

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.

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.

Extensive scholarly work engages with the growing number of women in legislatures around the world and highlights their role as advocates of women’s interests during parliamentary decision-making processes. This article sheds light on the reactions of men MPs (members of parliament) to this trend by uncovering how women’s numerical strength in party parliamentary groups shapes the issues that their men colleagues emphasize when speaking about women during plenary debates. I argue that, the higher the share of women in a party parliamentary group, the more will men representatives emphasize women’s interests in the context of issues they can easily relate to – either because the issues lie in men’s area of responsibility according to ideas about traditional role distributions in the society, e.g. the financing of gender equality projects, or because they are part of broader patterns of societal inequality, such as poverty or health. I provide empirical evidence for this argument based on original time-series cross-sectional data from plenary debates in six German states between 2005 and 2021 using a structural topic model. These findings shed light on men’s role as critical actors and have implications for gender equality and the functioning of representative democracy more broadly.

Abstract

Panel data possess several advantages over conventional cross-sectional and time-series data, including their power to isolate the effects of specific actions, treatments, and general policies often at the core of large-scale econometric development studies. While the concept of panel data alone provides the capacity for modeling the complexities of human behavior, the notion of universal panel data – in which time- and situation-driven variances leading to variations in tools, and thus results, are mitigated – can further enhance exploitation of the richness of panel information.

This Basic Information Document (BID) provides a brief overview of the Tanzania National Panel Survey (NPS), but focuses primarily on the theoretical development and application of panel data, as well as key elements of the universal panel survey instrument and datasets generated by the four rounds of the NPS. As this Basic Information Document (BID) for the UPD does not describe in detail the background, development, or use of the NPS itself, the round-specific NPS BIDs should supplement the information provided here.

The NPS Uniform Panel Dataset (UPD) consists of both survey instruments and datasets, meticulously aligned and engineered with the aim of facilitating the use of and improving access to the wealth of panel data offered by the NPS. The NPS-UPD provides a consistent and straightforward means of conducting not only user-driven analyses using convenient, standardized tools, but also for monitoring MKUKUTA, FYDP II, and other national level development indicators reported by the NPS.

The design of the NPS-UPD combines the four completed rounds of the NPS – NPS 2008/09 (R1), NPS 2010/11 (R2), NPS 2012/13 (R3), and NPS 2014/15 (R4) – into pooled, module-specific survey instruments and datasets. The panel survey instruments offer the ease of comparability over time, with modifications and variances easily identifiable as well as those aspects of the questionnaire which have remained identical and offer consistent information. By providing all module-specific data over time within compact, pooled datasets, panel datasets eliminate the need for user-generated merges between rounds and present data in a clear, logical format, increasing both the usability and comprehension of complex data.

Geographic coverage

Designed for analysis of key indicators at four primary domains of inference, namely: Dar es Salaam, other urban, rural, Zanzibar.

Analysis unit

• Households
• Individuals

Universe

The universe includes all households and individuals in Tanzania with the exception of those residing in military barracks or other institutions.

Kind of data

Sample survey data [ssd]

Sampling procedure

While the same sample of respondents was maintained over the first three rounds of the NPS, longitudinal surveys tend to suffer from bias introduced by households leaving the survey over time; i.e. attrition. Although the NPS maintains a highly successful recapture rate (roughly 96% retention at the household level), minimizing the escalation of this selection bias, a refresh of longitudinal cohorts was done for the NPS 2014/15 to ensure proper representativeness of estimates while maintaining a sufficient primary sample to maintain cohesion within panel analysis. A newly completed Population and Housing Census (PHC) in 2012, providing updated population figures along with changes in administrative boundaries, emboldened the opportunity to realign the NPS sample and abate collective bias potentially introduced through attrition.

To maintain the panel concept of the NPS, the sample design for NPS 2014/2015 consisted of a combination of the original NPS sample and a new NPS sample. A nationally representative sub-sample was selected to continue as part of the “Extended Panel” while an entirely new sample, “Refresh Panel”, was selected to represent national and sub-national domains. Similar to the sample in NPS 2008/2009, the sample design for the “Refresh Panel” allows analysis at four primary domains of inference, namely: Dar es Salaam, other urban areas on mainland Tanzania, rural mainland Tanzania, and Zanzibar. This new cohort in NPS 2014/2015 will be maintained and tracked in all future rounds between national censuses.

Mode of data collection

Face-to-face [f2f]

Research instrument

The format of the NPS-UPD survey instrument is similar to previously disseminated NPS survey instruments. Each module has a questionnaire and clearly identifies if the module collects information at the individual or household level. Within each module-specific questionnaire of the NPS-UPD survey instrument, there are five distinct sections, arranged vertically: (1) the UPD - “U” on the survey instrument, (2) R4, (3), R3, (4) R2, and (5) R1 – the latter 4 sections presenting each questionnaire in its original form at time of its respective dissemination.

The uppermost section of each module’s questionnaire (“U”) represents the model universal panel questionnaire, with questions generated from the comprehensive listing of questions across all four rounds of the NPS and codes generated from the comprehensive collection of codes. The following sections are arranged vertically by round, considering R4 as most recent. While not all rounds will have data reported for each question in the UPD and not each question will have reports for each of the UPD codes listed, the NPS-UPD survey instrument represents the visual, all-inclusive set of information collected by the NPS over time.

The four round-specific sections (R4, R3, R2, R1) are aligned with their UPD-equivalent question, visually presenting their contribution to compatibility with the UPD. Each round-specific section includes the original round-specific variable names, response codes and skip patterns (corresponding to their respective round-specific NPS data sets, and despite their variance from other rounds or from the comprehensive UPD code listing)4.

This replication file contains data and source code to replicate the results in "Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models" by Yiqing Xu

This paper discusses a nonstationary, heterogeneous Markov model designed to estimate entry and exit transition probabilities at the micro level from a time series of independent cross-sectional samples with a binary outcome variable. The model has its origins in the work of Moffitt and shares features with standard statistical methods for ecological inference. We outline the methodological framework proposed by Moffitt and present several extensions of the model to increase its potential application in a wider array of research contexts. We also discuss the relationship with previous lines of related research in political science. The example illustration uses survey data on American presidential vote intentions from a five-wave panel study conducted by Patterson in 1976. We treat the panel data as independent cross sections and compare the estimates of the Markov model with both dynamic panel parameter estimates and the actual observations in the panel. The results suggest that the proposed model provides a useful framework for the analysis of transitions in repeated cross sections. Open problems requiring further study are discussed.

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 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. QoG Basic Dataset, which consists of approximately the 300 most used variables from QoG Standard Dataset, is a selection of variables that cover the most important concepts related to Quality of Government. In the QoG Basic CS dataset, data from and around 2018 is included. Data from 2018 is prioritized, however, if no data is available for a country for 2018, data for 2019 is included. If no data exists for 2019, data for 2017 is included, and so on up to a maximum of +/- 3 years. In the QoG Basic TS dataset, data from 1946 to 2021 is included and the unit of analysis is country-year (e.g., Sweden-1946, Sweden-1947, etc.). Purpose: 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. In the QoG Basic TS dataset, data from 1946 to 2021 is included and the unit of analysis is country-year (e.g., Sweden-1946, Sweden-1947, etc.). Historical countries are in most cases denoted with a do-date (e.g. Ethiopia (-1992) and a from-date (Ethiopia (1993-)). QoG-institutet är ett oberoende forskningsinstitut som tillhör Statsvetenskapliga institutionen vid Göteborgs universitet. Sammanlagt är det ungefär 30 forskare som bedriver internationell forskning om orsaker till och konsekvenserna av korruption och samhällsstyrningens kvalitet. Forskningen fokuserar på det teoretiska och empiriska problemet hur politiska institutioner av hög kvalitet kan skapas och upprätthållas, samt studerar effekterna av samhällsstyrningens kvalitet på ett antal olika politikområden, som exempelvis hälsa, miljö, socialpolitik och fattigdom. QoG Basic Dataset, som består av ungefär av de 300 mest använda variablerna från QoG Standard Dataset, är ett urval antal variabler som täcker de viktigaste begreppen relaterade till Quality of Government. I QoG Basic CS datasetet ingår data från omkring 2018. Data från 2018 är prioriterat, men där inga uppgifter finns tillgängliga för 2018 för ett specifikt land så ingår data för 2019. Om inga uppgifter finns tillgängliga för 2019 så ingår data för 2017 och så vidare upp till max +/- 3 år. I QoG Basic TS datasetet ingår data från 1946 till 2021 och analysenheten är land-år (t.ex. Sverige-1946, Sverige-1947, etc.). Syfte: QoG:s huvudsakliga syfte är att bedriva och främja forskning om korruption. Ytterligare ett syfte med institutet är att offentliggöra nationellt gränsöverskridande och jämförbara data. I QoG Basic TS datasetet ingår data från 1946 till 2021 och analysenheten är land-år (t.ex. Sverige-1946, Sverige-1947, etc.). För historiska länder redovisas i de flesta fall även år för när landet upphörde att existera (exempelvis Etiopien (-1992)). Likaså redovisas årtal för om ett land återkommer i en annan form (exempelvis Etiopien (1993-)). Time-series dataset: 194 countries which are members of the United Nations well as previous members of the UN provided that their de facto sovereignty has not changed substantially since they were members. Plus an addition of 17 historical countries. A total of 211 nations. Cross-sectional dataset: 194 countries which are members of the United Nations as well as previous members of the UN provided that their de facto sovereignty has not changed substantially since they were members.Time-series dataset: 194 countries which are members of the United Nations well as previous members of the UN provided that their de facto sovereignty has not changed substantially since they were members. Plus an addition of 17 historical countries. A total of 211 nations. Cross-sectional dataset: 194 countries which are members of the United Nations as well as previous members of the UN provided that their de facto sovereignty has not changed substantially since they were members. Tidsseriedataset: 194 länder som är medlemmar i FN, eller som tidigare varit medlemmar och vars suveränitet inte förändrats sedan medlemskapet. Samt 17 nationer som upphört att existera. Totalt 211 nationer. Tvärsnittsdataset: 194 länder som är medlemmar i FN 2002, eller som tidigare varit medlemmar och vars suveränitet inte förändrats sedan medlemskapet.Tidsseriedataset: 194 länder som är medlemmar i FN, eller som tidigare varit medlemmar och vars suveränitet inte förändrats sedan medlemskapet. Samt 17 nationer som upphört att existera. Totalt 211 nationer. Tvärsnittsdataset: 194 länder som är medlemmar i FN 2002, eller som tidigare varit medlemmar och vars suveränitet inte förändrats sedan medlemskapet.

Replication material for Metzger and Jones' "Getting Time Right" (forthcoming, Political Analysis). See "readme.html" in /code folder for further documentation. The CO capsule does not rerun the main simulations, but does provide the raw simulation results from those simulations. Abstract: Logit and probit (L/P) models are a mainstay of binary time-series cross-sectional analyses (BTSCS). Researchers include cubic splines or time polynomials to acknowledge the temporal element inherent in these data. However, L/P models cannot easily accommodate three other aspects of the data’s temporality: whether covariate effects are conditional on time, whether the process of interest is causally complex, and whether our functional form assumption regarding time’s effect is correct. Failing to account for any of these issues amounts to misspecification bias, threatening our inferences’ validity. We argue scholars should consider using Cox duration models when analyzing BTSCS data, as they create fewer opportunities for such misspecification bias, while also having the ability to assess the same hypotheses as L/P. We use Monte Carlo simulations to bring new evidence to light showing Cox models perform just as well—and sometimes better—than logit models in a basic BTSCS setting, and perform considerably better in more complex BTSCS situations. In addition, we highlight a new interpretation technique for Cox models—transition probabilities—to make Cox model results more readily interpretable. We use an application from interstate conflict to demonstrate our points.

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.

Two couples of dataset and R codes used in my publication with the same title.sf.xlsx: the time series datasetSan Francisco.R: R codes used to analyze sf.xlsx27zipcodes.xls: the panel dataset27zipcodes.R: R codes used to analyze 27zipcodes.xls

Description

Dataset includes data from woody roots in Lettosuo drained peatland forest site. The study site id locates in the Tammela municipality in southern Finland (60° 38’ 31’’ N, 23° 57’ 35’’ E). Increment cores from roots were analysed for the ring δ¹³C values for dominant and suppressed Norway spruce trees and those have been analysed by Lehtonen et al. (2023), while data has been published in Lehtonen et al. (2022). Annual root cross-sectional disc areas from roots were digitized to estimate changes in the cross-sectional area. Wood density time series were defined from densitometer measurements. Site and tree selection has been described by Lehtonen et al. (2023). Data has been analysed by Lehtonen et al. (2025).

Data was collected as a part of BiBiFe (”Biogeochemical and biophysical feedbacks from forest harvesting to climate change”) consortium that is funded by the Research Council of Finland. Increment cores for stems were collected earlier and data is available in Zenodo repository (https://doi.org/10.5281/zenodo.5865404).

Sampling for root cross-sectional discs was conducted during April 2022 for sample trees (10 in total, of which 5 were suppressed trees from the selection harvest area and 5 suppressed trees from control area).

Tree ring carbon isotope data

Annual rings were analysed for δ¹³C values using laser ablation-isotope ratio mass spectrometry (LA-IRMS) at the Stable Isotope Laboratory of Luke (SILL) [https://www.luke.fi/en/research/research-infrastructures/stable-isotope-laboratory-of-luke-sill]. Ten increment cores from 2010–2021 were measured, following Saurer et al. (2023) as described in Lehtonen et al. (2023). Up to five evenly spaced “spots” for each annual tree ring were measured to obtain information on the intra-annual δ¹³C variation.

(1) File: root_d13C_spot.csv

File includes intra-annual δ¹³C estimates for proximal and distal roots. These values include atmospheric correction.

Data column description below for intra-annual root isotope data:

run_ID indicates the id of the observation

harvest indicates if the observation is from the selection harvest site

proximal indicates if the observation is the proximal root

tree indicates tree identity "C" for control and "H" for harvest

year is the year of the tree ring

spot is the number for the intra-annual measurements, 1 for first and 5 for last.

spot_size the size of the measured spot width in micrometres

δ¹³C gives the measured δ¹³C value based on the LA-IRMS measurements with atmospheric correction

corr atmospheric correction for the δ¹³C value

position indicates if the observation is the proximal- or distal root

(2) File: root_d13C.csv

File includes mean annual δ¹³C estimates for proximal and distal roots. The estimation of mean values accounts for the fact that individual measurements from outer part of the ring cover larger share from the annual growth of cross-sectional area.

Data column description below for annual root isotope data:

treatment indicates if the observation is from the selection harvest site or from the control

tree indicates tree identity "C" for control and "H" for harvest

position indicated whether root sample proximal (close to stem) or distal (further away from stem)

year is the year of the tree ring

δ¹³C gives the measured δ¹³C value based on the LA-IRMS measurements

(3) File: stem_d13C.csv

File includes mean annual δ¹³C estimates for stems. The estimation of mean values accounts for the fact that individual measurements from outer part of the ring cover larger share from the annual growth of cross-sectional area.

Data column description below for annual stem isotope data:

treatment indicates if the observation is from the selection harvest site or from the control

tree indicates tree identity "C" for control and "H" for harvest

year is the year of the tree ring

δ¹³C gives the measured δ¹³C value based on the LA-IRMS measurements

Cross-sectional measurements of root discs

In addition to the δ¹³C values, also the cross-sectional areas of root discs were measured by digitising scanned images.

(4) Files: rootg.csv

tree indicates tree identity "C" for control and "H" for harvest

position indicated whether root sample proximal (close to stem) or distal (further away from stem)

year is the year of the tree ring

g_end is the cross-sectional area of the disc sample after growing season

treatment indicates if the observation is from the selection harvest site or from the control

g_start is the cross-sectional area of the disc sample before growing season

dg is the annual cross-sectional area growth of the disc sample

Files include increment core data (in micrometers) from isotope sample trees and additional increment core trees from the control area and harvested area of the site. Dominant trees were measured only from the thinned area.

In the .csv files individual columns are for ring widths for individual trees. In the controlRW.csv and thinningRW.csv files first 5 columns include diameter increments from sample trees (those that have also d13C measurements).

(5) Files: Summary_digital_microscopy_Lettosuo.pdf

A digital microscope DSX1000 (Olympus) was used to take images of proximal and distal roots harvested from two sites: control (C, non-harvested) and harvested area (H). All samples were observed by the digital microscope to see whether or not harvesting has affected growth of roots of the remaining trees in the research sites.

Sample numbering: C, control area / H, harvested area; tree number (1–6); P, proximal root / d, distal root.

For example, sample C1P is control area, tree 1, proximal root.

1x or 10 x — the magnification of the objective used.

Several photos were taken from one sample. Stitching means a function where the digital microscopy automatically takes several photos side by side and combines them together. In this function, no scale bar can be added. A single image has also been taken from the same samples with same settings, and this image has a scale bar.

Wood density data

(6) File: LettosuoD.csv

Columns:

tree indicates tree identity "C" for control and "H" for harvest

position indicates whether root sample proximal (close to stem) or distal (further away from stem)

year is the year of the tree ring

wd is average wood density of each ring, kg/m³

Cross-sectional measurements of stems

(7) File: stemg.csv

Columns:

year is the year of the tree ring

id is a tree specific id

rw is a ring width

treatment indicates if a tree is from control or from harvest treatment

dbh is for diameter as breast height based on rw data

g_end breast height cross-sectional area of the stem at end the of the growing season

g_start breast height cross-sectional area of the stem before growing season

dg annual chnage of the breast height cross-sectional area

R code for linear mixed models

(8) File: snippet.R

R code to reproduce statistical analysis for impacts of selection harvest operation based on Before-After Control-Impact (BACI) analysis with linear mixed models.

References:

Lehtonen, A., Sahlstedt, E., Peltoniemi, M., Young, G., Salovaara, P., Leppä, K., & Rinne-Garmston, K. (2022). Tree diameter growth and increment core δ13C data from a recently thinned forestry-drained site (Lettosuo) in southern Finland. [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5865404

Lehtonen, A., Leppä, K., Rinne-Garmston, K.T., Sahlstedt, E., Schiestl-Aalto, P., Heikkinen, J., Young, G.H., Korkiakoski, M., Peltoniemi, M., Sarkkola, S., Lohila, A. and Mäkipää R., 2023. Fast recovery of suppressed Norway spruce trees after selection harvesting on a drained peatland forest site. Forest Ecology and Management, 530, p.120759. https://doi.org/10.1016/j.foreco.2022.120759

Lehtonen, A., Heikkinen, J., Boroski, C.A., Tikkasalo, O.P., Rinne-Garmston, K.T., Sahlstedt, E., Korkiakoski, M., Kärkönen, A., Lintunen, A., Mäkinen, H., Peltoniemi, M., de Quesada G., Salmon, Y., Young, G., Mäkipää, R. and Oren, R.

This repository include the processed ultrametric distance matrices data, MATLAB scripts and data holder files (in .mat format) used to generate the results and figures in the PLOS paper with the above title.

(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 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

Spurious regression analysis in panel data when the time series are cross-section dependent is analyzed in the article. The set-up includes (possibly unknown) multiple structural breaks that can affect both the deterministic and the common factor components. We show that consistent estimation of the long-run average parameter is possible once cross-section dependence is controlled using cross-section averages in the spirit of Pesaran’s common correlated effects approach. This result is used to design individual and panel cointegration test statistics that accommodate the presence of structural breaks that can induce parameter instabilities in the deterministic component, the cointegration vector and the common factor loadings.