This is replication code and data for the paper "The Long and Short (Run) Of Trade Elasticities."Abstract: We propose a novel approach to estimate the trade elasticity at various horizons. When countries change Most Favored Nation (MFN) tariffs, partners that trade on MFN terms experience plausibly exogenous tariff changes. The differential effects on imports from these countries relative to a control group – countries not subject to the MFN tariff scheme – can be used to identify the trade elasticity. We build a panel dataset combining information on product-level tariffs and trade flows covering 1995-2018, and estimate the trade elasticity at short and long horizons usinglocal projections (Jordà, 2005). Our main findings are that the elasticity of tariff-exclusive trade flows in the year following the exogenous tariff change is about −0.76, and the long-run elasticity ranges from −1.75 to −2.25. Our long-run estimates are smaller than typical in the literature, and it takes 7-10 years to converge to the long run, implying that (i) the welfare gains from trade are high and (ii) there are substantial convexities in the costs of adjusting export participation.

The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color aerial orthoimagery and 2007 topographic lidar datasets obtained from the National Oceanic and Atmospheric Administration's Ocean Service, Coastal Services Center. This 2018 data release includes rates that incorporate two new mean high water (MHW) shorelines for the Massachusetts coast extracted from lidar data collected between 2010 and 2014. The first new shoreline for the State includes data from 2010 along the North Shore and South Coast from lidar data collected by the U.S. Army Corps of Engineers (USACE) Joint Airborne Lidar Bathymetry Technical Center of Expertise. Shorelines along the South Shore and Outer Cape are from 2011 lidar data collected by the U.S. Geological Survey's (USGS) National Geospatial Program Office. Shorelines along Nantucket and Martha’s Vineyard are from a 2012 USACE Post Sandy Topographic lidar survey. The second new shoreline for the North Shore, Boston, South Shore, Cape Cod Bay, Outer Cape, South Cape, Nantucket, Martha’s Vineyard, and the South Coast (around Buzzards Bay to the Rhode Island Border) is from 2013-14 lidar data collected by the (USGS) Coastal and Marine Geology Program. This 2018 update of the rate of shoreline change in Massachusetts includes two types of rates. Some of the rates include a proxy-datum bias correction, this is indicated in the filename with “PDB”. The rates that do not account for this correction have “NB” in their file names. The proxy-datum bias is applied because in some areas a proxy shoreline (like a High Water Line shoreline) has a bias when compared to a datum shoreline (like a Mean High Water shoreline). In areas where it exists, this bias should be accounted for when calculating rates using a mix of proxy and datum shorelines. This issue is explained further in Ruggiero and List (2009) and in the process steps of the metadata associated with the rates. This release includes both long-term (~150 years) and short term (~30 years) rates. Files associated with the long-term rates have “LT” in their names, files associated with short-term rates have “ST” in their names.

Blockchain data query: Long vs Short (DEGODS)

Dataset comprises data used ti produce the article “The Short and Long-Term Impacts of Government Procurement on the Bankruptcy Rates of Brazilian Businesses”. The variables used is as follow: i) The Dependent Variables are data from Brazilian MSEs (micro and small enterprises) or MLEs (medium and large enterprises) bankruptcy rates from 2005 to 2022; ii) The Exploratory Variable of Interest are data from government public procurement with MSEs and MLEs; iii) The Explanatory Variables of Control are Exchange = Real effective exchange rate index (exports), Inflation = The Extended Consumer Price Index (IPCA), Interest = Selic-Over interest rate (rate of interbank operations with a term of 1 day backed by federal public securities), Economy = Economic Activity Index (agriculture, industry and services) of the BCB (IBCBr) – seasonally adjusted, Simples = Temporal dummy representative of the effectiveness of the National Simple Taxation System, Crisis = Temporal dummy representative of the duration of the Brazilian economic crisis, and Pandemic = Temporal dummy representative of the duration of the COVID-19 pandemic. (2023-07-17).

The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes one new mean high water (MHW) shoreline extracted from lidar data collected in 2017 for the entire coastal region of North Carolina which is divided into four subregions: northern North Carolina (NCnorth), central North Carolina (NCcentral), southern North Carolina (NCsouth), and western North Carolina (NCwest). Previously published historical shorelines for North Carolina (Kratzmann and others, 2017) were combined with the new lidar shoreline to calculate long-term (up to 169 years) and short-term (up to 20 years) rates of change. Files associated with the long-term and short-term rates are appended with "LT" and "ST", respectively. A proxy-datum bias reference line that accounts for the positional difference in a proxy shoreline (e.g. High Water Line (HWL) shoreline) and a datum shoreline (e.g. MHW shoreline) is also included in this release.

Access B2B Contact Data for North American Small Business Owners with Success.ai—your go-to provider for verified, high-quality business datasets. This dataset is tailored for businesses, agencies, and professionals seeking direct access to decision-makers within the small business ecosystem across North America. With over 170 million professional profiles, it’s an unparalleled resource for powering your marketing, sales, and lead generation efforts.

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What are the implications of hard economic times for regional economic cooperation? Existing research is sharply divided on the answer to this question. Some studies suggest that economic crises encourage governments to strengthen their regional institutions, but others indicate that they lead to decreasing investment in such initiatives. Both sides overlook the possibility that the passage of time conditions these relationships, however. We aim to bridge these opposing perspectives by distinguishing between short-term and long-term effects of economic hard times on institutionalized regional cooperation. We argue that in the short term economic crises impede regional institutionalization due to protectionist pressures, nationalistic public sentiments, and political instability. This effect is reversed in the longer term, as interest groups and the public adopt more favorable attitudes toward regional economic organizations (REOs) and governments employ these institutions to demonstrate their competence and to improve economic conditions. We evaluate this argument in relations to regional institutionalization, which refers to the functional scope and structure of REOs. Using a data set that contains information on this dimension for thirty REOs over four decades, we find strong support for the theoretical framework: regional institutionalization remains stagnant in the immediate aftermath of economic crises, but increases in subsequent years.

Dataset description This post provides the data and R scripts for analysis of data on the variation between long form nominative, short form nominative, and instrumental case in Russian predicate adjectives in sentences containing an overt copula verb. We analyze the various factors associated with the choice of form of the adjective. This is the abstract of the article: Based on data from the syntactic subcorpus of the Russian National Corpus, we undertake a quantitative analysis of the competition between Russian predicate adjectives in the instrumental (e.g., pustym ‘empty’), the long form nominative (e.g., pustoj ‘empty’), and the short form nominative (e.g., pust ‘empty’). It is argued that the choice of adjective form is partly determined by the context. Four (nearly) categorical rules are proposed based on the following contextual factors: the form of the copula verb, the presence/absence of a complement, and the nature of the subject of the sentence. At the same time, a “space of competition” is identified, where all three adjective forms are attested. It is hypothesized that within the space of competition, the three forms are recruited to convey different meanings, and it is argued that our analysis lends support to the traditional idea that the short form nominative is closely related to verbs. Our findings are furthermore compatible with the idea that the short form nominative expresses temporary states, rather than inherent permanent characteristics.

About this dataset

Context

The dataset tabulates the Shorter population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Shorter. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 143 (43.60% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Shorter population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Shorter is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Shorter is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Shorter Population by Age. You can refer the same here

Archaeorhizomycetes is a class of globally widespread soil-dwelling fungi, originally proposed to be associated with plant roots, but their ecology and nutritional mode are not clearly defined. Here, we have sequenced and aligned approximately 2500 bp of the ribosomal DNA from 18S (covering the V4 region), ITS and well into 28S. Sequences have been aligned using the 'linsi' algorithm in MAFFT v7.505 (Katoh & Standley, 2013). The alignment was trimmed for ambiguously aligned positions with trimAl using the –gappyout option (Capella_Gutiérrez et al., 2009).The phylogenetic tree was constructed with IQ-TREE v2.2.2.3 (Nguyen et al., 2015). The best fitting model was determined to be GTR+F+I+R5 by ModelFinder (Kalyaanamoorthy et al., 2017), and support values for the tree were calculated with the SH-like approximate likelihood ratio test (aLRT) and ultrafast bootstrap approximation UFBoot (Hoang et al., 2018), both with 1000 replicates.

L1 Long Short Fund reported AUD224K in Selling and Administration Expenses for its fiscal semester ending in June of 2025. Data for L1 Long Short Fund | LSF - Selling And Administration Expenses including historical, tables and charts were last updated by Trading Economics this last November in 2025.

Together, the datasets and .do files replicate Tables 2, 4, 5, 6, and 7 from "The long and short of it: The unpredictability of late deciding voters." For a description of the variables used in each model, see the paper and .do file.

Archaeorhizomycetes is a class of globally widespread soil-dwelling fungi, originally proposed to be associated with plant roots, but their ecology and nutritional mode are not clearly defined. To increase the knowledge about Archaeorhizomycetes’ ecology and biogeography, we investigate how they are distributed along major environmental gradients, as well as different soil compartments. The dataset consists of mapping files for raw sequence data deposited to ENA under accession numbers ERR15529369- ERR15529373 for PacBio long-read amplicon sequences of Archaeorhizomycetes (partial 18S, ITS, partial 28S, approx. 2500 bp) and accession numbers ERR15529374- ERR15529378 for raw sequencing files for V4 of the 18S using general eukaryotic primers (TAReuk454FWD1 and TAReukREV3). The dataset also consists of OTU tables for both sequencing runs, metadata and R scripts to analyse the data.

"Analisis nematodes sbb" is the code used for most statistical analyses of the paper https://doi.org/10.1016/j.soilbio.2023.108974 . Data used for this code is nematodes_18.txt "sem" is the code used for perfom sem analyses. Data used for this code is datossemnematodes. txt "code graph paper nematodes" is the code used for figures. Data used in this case is nematodes_graph.txt

This table covers investments of institutional investors from 1950 onwards. It enables analyzing shifts over time in the investment portfolio of institutional investors. This is possible for the total of institutional investors, and for each of the three groups: pension funds, insurance corporations and investment funds.

Data available from 1950 to 2012.

Status of the figures: The figures in this table are up to 2010 definitive, figures for 2011 are revised provisional figures and figures for 2012 are provisional. Because this table is discontinued, figures will not be updated anymore.

Changes as of 18 December 2014: None, this table is discontinued.

When will new figures be published? Not applicable anymore. This table is replaced by table Institutional investors; short-term and long-term investments. See paragraph 3.

L1 Long Short Fund reported AUD1.41B in Cash and Equivalent for its fiscal semester ending in June of 2025. Data for L1 Long Short Fund | LSF - Cash And Equivalent including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Description of Dataset This is a study of examples of Russian predicate adjectives in clauses with zero-copula present tense, where the adjective is a short form (SF) or a long form nominative (LF). The data was collected in 2022 from SynTagRus (https://universaldependencies.org/treebanks/ru_syntagrus/index.html), the syntactic subcorpus of the Russian National Corpus (https://ruscorpora.ru/new/). The data merges the results of several searches conducted to extract examples of sentences with long form and short form adjectives in predicate position, as identified by the corpus. The examples were imported to a spreadsheet and annotated manually, based on the syntactic analyses given in the corpus. For present tense sentences with no copula (Река спокойна or Река спокойная), it was necessary to search for an adjective as the top (root) node in the syntactic structure. The syntactic and morphological categories used in the corpus are explained here: https://ruscorpora.ru/page/instruction-syntax/. In order for the R code to run from these files, one needs to set up an R project with the data files in a folder named "data" and the R markdown files in a folder named "scripts". Method: Logistic regression analysis of corpus data carried out in R (R version 4.2.3 (2023-03-15)-- "Shortstop Beagle" Copyright (C) 2023 The R Foundation for Statistical Computing) and documented in an .Rmd file. Publication Abstract The present article presents an empirical investigation of the choice between so-called long (e.g., prostoj ‘simple’) and short forms (e.g., prost ‘simple’) of predicate adjectives in Russian based on data from the syntactic subcorpus of the Russian National Corpus. The data under scrutiny suggest that short forms represent the dominant option for predicate adjectives. It is proposed that long forms are descriptions of thematic participants in sentences with no complement, while short forms may take complements and describe both participants (thematic and rhematic) and situations. Within the “space of competition” where both long and short forms are well attested, it is argued that the choice of form to some extent depends on subject type, gender/number, and frequency. On the methodological level, the approach adopted in the present study may be extended to other cases of competition in morphosyntax. It is suggested that one should first “peel off” contexts where (nearly) categorical rules are at work, before one undertakes a statistical analysis of the “space of competition”.

The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using 2008-2009 color aerial orthoimagery and 2007 topographic lidar datasets obtained from NOAA's Ocean Service, Coastal Services Center. In 2018, two new mean high water (MHW) shorelines for the Massachusetts coast extracted from lidar data between 2010-2014 were added to the dataset. This 2021 data release includes rates that incorporate one new shoreline extracted from 2018 lidar data collected by the U.S. Army Corps of Engineers (USACE) Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX), added to the existing database of all historical shorelines (1844-2014), for the North Shore, South Shore, Cape Cod Bay, Outer Cape, Buzzard’s Bay, South Cape, Nantucket, and Martha’s Vineyard. 2018 lidar data did not cover the Boston or Elizabeth Islands regions. Included in this data release is a proxy-datum bias reference line that accounts for the positional difference in a proxy shoreline (like a High Water Line shoreline) and a datum shoreline (like a Mean High Water shoreline. This issue is explained further in Ruggiero and List (2009) and in the process steps of the metadata associated with the rates. This release includes both long-term (~150+ years) and short term (~30 years) rates. Files associated with the long-term rates have "LT"; in their names, files associated with short-term rates have "ST"; in their names.

In this paper we propose a dynamic multinomial probit model in order to estimate the long-run and short- run effects of marketing mix variables on brand choice. The latent variables, which contain the unobserved perceived utilities, follow a first-order vector error correction autoregressive process of order 1 with current and lagged explanatory variables. The unrestricted autoregressive parameter matrix concerns the intertemporal correlation in perceived utilities of households over purchase occasions and indicates the persistence in brand choice. As explanatory variables we consider relative prices and promotional activities like feature and display. An important and novel feature of our model is that it allows for different long-run and short-run effects of promotional activities, thereby extending the models that are currently available in the literature. Additionally, to account for different base preferences for brands across households, we allow for consumer heterogeneity. Our application concerns a panel of households choosing among several brands of a FMCG. Our estimated model turns out to be an improvement over a static model and over a model with only short-run effects, in terms of in-sample fit and out-of-sample forecasts.

BackgroundWarfarin is an effective treatment for thromboembolic disease but has a narrow therapeutic index, and dosage can differ tremendously among individuals. The study aimed to develop an individualized international normalized ratio (INR) model based on time series anticoagulant data and simulate individualized warfarin dosing.MethodsWe used a long short-term memory (LSTM) network to develop an individualized INR model based on data from 4,578 follow-up visits, including clinical and genetic factors from 624 patients whom we enrolled in our previous randomized controlled trial. The data of 158 patients who underwent valvular surgery and were included in a prospective registry study were used for external validation in the real world.ResultsThe prediction accuracy of LSTM_INR was 70.0%, which was much higher than that of MAPB_INR (maximum posterior Bayesian, 53.9%). Temporal variables were significant for LSTM_INR performance (51.7 vs. 70.0%, P < 0.05). Genetic factors played an important role in predicting INR at the onset of therapy, while after 15 days of treatment, we found that it might unnecessary to detect genotypes for warfarin dosing. Using LSTM_INR, we successfully simulated individualized warfarin dosing and developed an application (AI-WAR) for individualized warfarin therapy.ConclusionThe results indicate that temporal variables are necessary to be considered in warfarin therapy, except for clinical factors and genetic factors. LSTM network may have great potential for long-term drug individualized therapy.Trial RegistrationNCT02211326; www.chictr.org.cn:ChiCTR2100052089.