Showing 43 of total 43 results (show query)
kurthornik
tseries:Time Series Analysis and Computational Finance
Time series analysis and computational finance.
Maintained by Kurt Hornik. Last updated 6 months ago.
28.8 match 4 stars 11.22 score 10k scripts 289 dependents
mbalcilar
mFilter:Miscellaneous Time Series Filters
The mFilter package implements several time series filters useful for smoothing and extracting trend and cyclical components of a time series. The routines are commonly used in economics and finance, however they should also be interest to other areas. Currently, Christiano-Fitzgerald, Baxter-King, Hodrick-Prescott, Butterworth, and trigonometric regression filters are included in the package.
Maintained by Mehmet Balcilar. Last updated 2 years ago.
baxter-king-filterbutterworth-filterchristiano-fitzgerald-filterfiltershodrick-prescott-filtermacroeconomicstime-seriestrigonometric-regression-filter
10.0 match 5 stars 6.99 score 732 scripts 2 dependentskurthornik
clue:Cluster Ensembles
CLUster Ensembles.
Maintained by Kurt Hornik. Last updated 4 months ago.
7.0 match 2 stars 9.85 score 496 scripts 401 dependents
ropenspain
tidyBdE:Download Data from Bank of Spain
Tools to download data series from 'Banco de España' ('BdE') on 'tibble' format. 'Banco de España' is the national central bank and, within the framework of the Single Supervisory Mechanism ('SSM'), the supervisor of the Spanish banking system along with the European Central Bank. This package is in no way sponsored endorsed or administered by 'Banco de España'.
Maintained by Diego H. Herrero. Last updated 1 months ago.
apibdeggplot2macroeconomicsropenspainseries-dataspain
10.0 match 11 stars 6.46 score 14 scripts
gpetris
dlm:Bayesian and Likelihood Analysis of Dynamic Linear Models
Provides routines for Maximum likelihood, Kalman filtering and smoothing, and Bayesian analysis of Normal linear State Space models, also known as Dynamic Linear Models.
Maintained by Giovanni Petris. Last updated 6 months ago.
7.3 match 9 stars 7.65 score 470 scripts 11 dependents
sbgraves237
Ecdat:Data Sets for Econometrics
Data sets for econometrics, including political science.
Maintained by Spencer Graves. Last updated 4 months ago.
7.3 match 2 stars 7.25 score 740 scripts 3 dependents
pachadotdev
leontief:Input-Output Analysis
An implementation of the Input-Output model developed by Wassily Leontief that represents the interdependencies between different sectors of a national economy or different regional economies.
Maintained by Mauricio Vargas. Last updated 2 years ago.
10.0 match 14 stars 4.92 score 12 scripts
tylerjpike
sovereign:State-Dependent Empirical Analysis
A set of tools for state-dependent empirical analysis through both VAR- and local projection-based state-dependent forecasts, impulse response functions, historical decompositions, and forecast error variance decompositions.
Maintained by Tyler J. Pike. Last updated 2 years ago.
econometricsforecastingimpulse-responselocal-projectionmacroeconomicsstate-dependenttime-seriesvector-autoregression
10.0 match 11 stars 4.74 score 8 scripts
sebkrantz
dfms:Dynamic Factor Models
Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm or Two-Step (2S) estimation, supporting datasets with missing data. The estimation options follow advances in the econometric literature: either running the Kalman Filter and Smoother once with initial values from PCA - 2S estimation as in Doz, Giannone and Reichlin (2011) <doi:10.1016/j.jeconom.2011.02.012> - or via iterated Kalman Filtering and Smoothing until EM convergence - following Doz, Giannone and Reichlin (2012) <doi:10.1162/REST_a_00225> - or using the adapted EM algorithm of Banbura and Modugno (2014) <doi:10.1002/jae.2306>, allowing arbitrary patterns of missing data. The implementation makes heavy use of the 'Armadillo' 'C++' library and the 'collapse' package, providing for particularly speedy estimation. A comprehensive set of methods supports interpretation and visualization of the model as well as forecasting. Information criteria to choose the number of factors are also provided - following Bai and Ng (2002) <doi:10.1111/1468-0262.00273>.
Maintained by Sebastian Krantz. Last updated 1 hours ago.
dynamic-factor-modelstime-seriesopenblascpp
6.9 match 32 stars 5.76 score 12 scripts
nk027
BVAR:Hierarchical Bayesian Vector Autoregression
Estimation of hierarchical Bayesian vector autoregressive models following Kuschnig & Vashold (2021) <doi:10.18637/jss.v100.i14>. Implements hierarchical prior selection for conjugate priors in the fashion of Giannone, Lenza & Primiceri (2015) <doi:10.1162/REST_a_00483>. Functions to compute and identify impulse responses, calculate forecasts, forecast error variance decompositions and scenarios are available. Several methods to print, plot and summarise results facilitate analysis.
Maintained by Nikolas Kuschnig. Last updated 4 months ago.
bayesianbvarforecastsimpulse-responsesvector-autoregressions
5.1 match 51 stars 7.30 score 68 scripts 1 dependents
bpfaff
urca:Unit Root and Cointegration Tests for Time Series Data
Unit root and cointegration tests encountered in applied econometric analysis are implemented.
Maintained by Bernhard Pfaff. Last updated 10 months ago.
3.8 match 6 stars 8.91 score 1.4k scripts 269 dependentsbpfaff
vars:VAR Modelling
Estimation, lag selection, diagnostic testing, forecasting, causality analysis, forecast error variance decomposition and impulse response functions of VAR models and estimation of SVAR and SVEC models.
Maintained by Bernhard Pfaff. Last updated 12 months ago.
3.8 match 7 stars 8.68 score 2.8k scripts 44 dependents
fk83
bvarsv:Bayesian Analysis of a Vector Autoregressive Model with Stochastic Volatility and Time-Varying Parameters
R/C++ implementation of the model proposed by Primiceri ("Time Varying Structural Vector Autoregressions and Monetary Policy", Review of Economic Studies, 2005), with functionality for computing posterior predictive distributions and impulse responses.
Maintained by Fabian Krueger. Last updated 6 months ago.
5.6 match 30 stars 5.43 score 60 scripts 1 dependents
franzmohr
bvartools:Bayesian Inference of Vector Autoregressive and Error Correction Models
Assists in the set-up of algorithms for Bayesian inference of vector autoregressive (VAR) and error correction (VEC) models. Functions for posterior simulation, forecasting, impulse response analysis and forecast error variance decomposition are largely based on the introductory texts of Chan, Koop, Poirier and Tobias (2019, ISBN: 9781108437493), Koop and Korobilis (2010) <doi:10.1561/0800000013> and Luetkepohl (2006, ISBN: 9783540262398).
Maintained by Franz X. Mohr. Last updated 1 years ago.
bayesianbayesian-inferencebayesian-varbvarbvecmgibbs-samplingmcmcvector-autoregressionvector-error-correction-modelopenblascpp
4.0 match 31 stars 6.80 score 34 scripts 1 dependents
alexanderlange53
svars:Data-Driven Identification of SVAR Models
Implements data-driven identification methods for structural vector autoregressive (SVAR) models as described in Lange et al. (2021) <doi:10.18637/jss.v097.i05>. Based on an existing VAR model object (provided by e.g. VAR() from the 'vars' package), the structural impact matrix is obtained via data-driven identification techniques (i.e. changes in volatility (Rigobon, R. (2003) <doi:10.1162/003465303772815727>), patterns of GARCH (Normadin, M., Phaneuf, L. (2004) <doi:10.1016/j.jmoneco.2003.11.002>), independent component analysis (Matteson, D. S, Tsay, R. S., (2013) <doi:10.1080/01621459.2016.1150851>), least dependent innovations (Herwartz, H., Ploedt, M., (2016) <doi:10.1016/j.jimonfin.2015.11.001>), smooth transition in variances (Luetkepohl, H., Netsunajev, A. (2017) <doi:10.1016/j.jedc.2017.09.001>) or non-Gaussian maximum likelihood (Lanne, M., Meitz, M., Saikkonen, P. (2017) <doi:10.1016/j.jeconom.2016.06.002>)).
Maintained by Alexander Lange. Last updated 2 years ago.
3.8 match 46 stars 7.22 score 130 scripts
joachim-gassen
ExPanDaR:Explore Your Data Interactively
Provides a shiny-based front end (the 'ExPanD' app) and a set of functions for exploratory data analysis. Run as a web-based app, 'ExPanD' enables users to assess the robustness of empirical evidence without providing them access to the underlying data. You can export a notebook containing the analysis of 'ExPanD' and/or use the functions of the package to support your exploratory data analysis workflow. Refer to the vignettes of the package for more information on how to use 'ExPanD' and/or the functions of this package.
Maintained by Joachim Gassen. Last updated 4 years ago.
accountingedaexploratory-data-analysisfinanceopen-sciencereplicationshinyshiny-apps
3.3 match 156 stars 7.80 score 203 scripts
smeekes
bootUR:Bootstrap Unit Root Tests
Set of functions to perform various bootstrap unit root tests for both individual time series (including augmented Dickey-Fuller test and union tests), multiple time series and panel data; see Smeekes and Wilms (2023) <doi:10.18637/jss.v106.i12>, Palm, Smeekes and Urbain (2008) <doi:10.1111/j.1467-9892.2007.00565.x>, Palm, Smeekes and Urbain (2011) <doi:10.1016/j.jeconom.2010.11.010>, Moon and Perron (2012) <doi:10.1016/j.jeconom.2012.01.008>, Smeekes and Taylor (2012) <doi:10.1017/S0266466611000387> and Smeekes (2015) <doi:10.1111/jtsa.12110> for key references.
Maintained by Stephan Smeekes. Last updated 1 months ago.
bootstrapdickey-fullerhypothesis-testtime-seriesunit-rootopenblascpp
4.0 match 10 stars 5.91 score 27 scripts
pik-piam
mrremind:MadRat REMIND Input Data Package
The mrremind packages contains data preprocessing for the REMIND model.
Maintained by Lavinia Baumstark. Last updated 4 days ago.
3.7 match 4 stars 6.25 score 15 scripts 1 dependents
svmiller
stevedata:Steve's Toy Data for Teaching About a Variety of Methodological, Social, and Political Topics
This is a collection of various kinds of data with broad uses for teaching. My students, and academics like me who teach the same topics I teach, should find this useful if their teaching workflow is also built around the R programming language. The applications are multiple but mostly cluster on topics of statistical methodology, international relations, and political economy.
Maintained by Steve Miller. Last updated 4 days ago.
3.3 match 8 stars 5.97 score 178 scripts
ovgu-sh
desk:Didactic Econometrics Starter Kit
Written to help undergraduate as well as graduate students to get started with R for basic econometrics without the need to import specific functions and datasets from many different sources. Primarily, the package is meant to accompany the German textbook Auer, L.v., Hoffmann, S., Kranz, T. (2024, ISBN: 978-3-662-68263-0) from which the exercises cover all the topics from the textbook Auer, L.v. (2023, ISBN: 978-3-658-42699-6).
Maintained by Soenke Hoffmann. Last updated 11 months ago.
4.0 match 4.30 score 10 scripts
jvg0mes
metools:Macroeconomics Tools
Provides a number of functions to facilitate the handling and production of reports using time series data. The package was developed to be understandable for beginners, so some functions aim to transform processes that would be complex into functions with a few lines. The main advantage of using the 'metools' package is the ease of producing reports and working with time series using a few lines of code, so the code is clean and easy to understand/maintain. Learn more about the 'metools' at <https://metoolsr.wordpress.com>.
Maintained by João Victor Gomes de Araujo Santana. Last updated 5 years ago.
6.0 match 2.70 score
tkmckenzie
snfa:Smooth Non-Parametric Frontier Analysis
Fitting of non-parametric production frontiers for use in efficiency analysis. Methods are provided for both a smooth analogue of Data Envelopment Analysis (DEA) and a non-parametric analogue of Stochastic Frontier Analysis (SFA). Frontiers are constructed for multiple inputs and a single output using constrained kernel smoothing as in Racine et al. (2009), which allow for the imposition of monotonicity and concavity constraints on the estimated frontier.
Maintained by Taylor McKenzie. Last updated 5 years ago.
4.0 match 3.70 score 8 scripts
yanyachen
FinCovRegularization:Covariance Matrix Estimation and Regularization for Finance
Estimation and regularization for covariance matrix of asset returns. For covariance matrix estimation, three major types of factor models are included: macroeconomic factor model, fundamental factor model and statistical factor model. For covariance matrix regularization, four regularized estimators are included: banding, tapering, hard-thresholding and soft- thresholding. The tuning parameters of these regularized estimators are selected via cross-validation.
Maintained by YaChen Yan. Last updated 8 years ago.
2.3 match 7 stars 4.30 score 19 scripts 1 dependents
vzhomeexperiments
lazytrade:Learn Computer and Data Science using Algorithmic Trading
Provide sets of functions and methods to learn and practice data science using idea of algorithmic trading. Main goal is to process information within "Decision Support System" to come up with analysis or predictions. There are several utilities such as dynamic and adaptive risk management using reinforcement learning and even functions to generate predictions of price changes using pattern recognition deep regression learning. Summary of Methods used: Awesome H2O tutorials: <https://github.com/h2oai/awesome-h2o>, Market Type research of Van Tharp Institute: <https://vantharp.com/>, Reinforcement Learning R package: <https://CRAN.R-project.org/package=ReinforcementLearning>.
Maintained by Vladimir Zhbanko. Last updated 8 months ago.
1.6 match 23 stars 5.58 score 333 scripts
gianmarco-v
SIRE:Finding Feedback Effects in SEM and Testing for Their Significance
Provides two main functionalities. 1 - Given a system of simultaneous equation, it decomposes the matrix of coefficients weighting the endogenous variables into three submatrices: one includes the subset of coefficients that have a causal nature in the model, two include the subset of coefficients that have a interdependent nature in the model, either at systematic level or induced by the correlation between error terms. 2 - Given a decomposed model, it tests for the significance of the interdependent relationships acting in the system, via Maximum likelihood and Wald test, which can be built starting from the function output. For theoretical reference see Faliva (1992) <doi:10.1007/BF02589085> and Faliva and Zoia (1994) <doi:10.1007/BF02589041>.
Maintained by Gianmarco Vacca. Last updated 6 years ago.
4.0 match 2.00 score 5 scriptssebkrantz
samadb:South Africa Macroeconomic Database API
An R API providing access to a relational database with macroeconomic time series data for South Africa, obtained from the South African Reserve Bank (SARB) and Statistics South Africa (STATSSA), and updated on a weekly basis via the EconData <https://www.econdata.co.za/> platform and automated scraping of the SARB and STATSSA websites. The database is maintained at the Department of Economics at Stellenbosch University.
Maintained by Sebastian Krantz. Last updated 10 months ago.
5.3 match 1.00 score 2 scripts
gamrot
godley:Stock-Flow-Consistent Model Simulator
Define, simulate, and validate stock-flow consistent (SFC) macroeconomic models. The godley R package offers tools to dynamically define model structures by adding variables and specifying governing systems of equations. With it, users can analyze how different macroeconomic structures affect key variables, perform parameter sensitivity analyses, introduce policy shocks, and visualize resulting economic scenarios. The accounting structure of SFC models follows the approach outlined in the seminal study by Godley and Lavoie (2007, ISBN:978-1-137-08599-3), ensuring a comprehensive integration of all economic flows and stocks. The algorithms implemented to solve the models are based on methodologies from Kinsella and O'Shea (2010) <doi:10.2139/ssrn.1729205>, Peressini and Sullivan (1988, ISBN:0-387-96614-5), and contributions by Joao Macalos.
Maintained by Elżbieta Jowik. Last updated 4 days ago.
0.8 match 8 stars 5.28 score 16 scripts
stevecondylios
priceR:Economics and Pricing Tools
Functions to aid in micro and macro economic analysis and handling of price and currency data. Includes extraction of relevant inflation and exchange rate data from World Bank API, data cleaning/parsing, and standardisation. Inflation adjustment calculations as found in Principles of Macroeconomics by Gregory Mankiw et al (2014). Current and historical end of day exchange rates for 171 currencies from the European Central Bank Statistical Data Warehouse (2020) <https://sdw.ecb.europa.eu/curConverter.do>.
Maintained by Steve Condylios. Last updated 7 months ago.
data-scienceeconometricseconomicsfinancemodelingr-programmingstatistics
0.5 match 59 stars 7.37 score 102 scripts
pyr-opendatafr
insee:Tools to Easily Download Data from INSEE BDM Database
Using embedded sdmx queries, get the data of more than 150 000 insee series from 'bdm' macroeconomic database.
Maintained by Hadrien Leclerc. Last updated 7 months ago.
0.5 match 7 stars 6.83 score 69 scripts
mattcowgill
readrba:Download and Tidy Data from the Reserve Bank of Australia
Download up-to-date data from the Reserve Bank of Australia in a tidy data frame. Package includes functions to download current and historical statistical tables (<https://www.rba.gov.au/statistics/tables/>) and forecasts (<https://www.rba.gov.au/publications/smp/forecasts-archive.html>). Data includes a broad range of Australian macroeconomic and financial time series.
Maintained by Matt Cowgill. Last updated 4 months ago.
0.5 match 27 stars 6.62 score 26 scripts
gomesleduardo
ipeadatar:API Wrapper for 'Ipeadata'
Allows direct access to the macroeconomic, financial and regional database maintained by Brazilian Institute for Applied Economic Research ('Ipea'). This R package uses the 'Ipeadata' API. For more information, see <http://www.ipeadata.gov.br/>.
Maintained by Luiz Eduardo S. Gomes. Last updated 3 years ago.
0.5 match 26 stars 5.95 score 68 scripts
marcburri
bridgr:Bridging Data Frequencies for Timely Economic Forecasts
Provides tools for implementing bridge models, which are used to nowcast and forecast macroeconomic variables by linking high-frequency indicator variables (e.g., monthly data) to low-frequency target variables (e.g., quarterly GDP). Forecasting and aggregation of the indicator variables to match the target frequency are simplified, enabling timely predictions before official data releases are available. For more information about bridge models, see Baffigi, A., Golinelli, R., \& Parigi, G. (2004) <doi:10.1016/S0169-2070(03)00067-0>, Burri (2023) <https://www5.unine.ch/RePEc/ftp/irn/pdfs/WP23-02.pdf> or Schumacher (2016) <doi:10.1016/j.ijforecast.2015.07.004>.
Maintained by Marc Burri. Last updated 3 months ago.
0.5 match 2 stars 4.52 score 11 scripts
rubensmoura87
MultiATSM:Multicountry Term Structure of Interest Rates Models
Estimation routines for several classes of affine term structure of interest rates models. All the models are based on the single-country unspanned macroeconomic risk framework from Joslin, Priebsch, and Singleton (2014, JF) <doi:10.1111/jofi.12131>. Multicountry extensions such as the ones of Jotikasthira, Le, and Lundblad (2015, JFE) <doi:10.1016/j.jfineco.2014.09.004>, Candelon and Moura (2023, EM) <doi:10.1016/j.econmod.2023.106453>, and Candelon and Moura (Forthcoming, JFEC) <doi:10.1093/jjfinec/nbae008> are also available.
Maintained by Rubens Moura. Last updated 6 days ago.
0.5 match 3.90 score 8 scripts
cran
AFR:Toolkit for Regression Analysis of Kazakhstan Banking Sector Data
Tool is created for regression, prediction and forecast analysis of macroeconomic and credit data. The package includes functions from existing R packages adapted for banking sector of Kazakhstan. The purpose of the package is to optimize statistical functions for easier interpretation for bank analysts and non-statisticians.
Maintained by Sultan Zhaparov. Last updated 6 months ago.
0.5 match 3.18 scoresebkrantz
ugatsdb:Uganda Time Series Database API
An R API providing easy access to a relational database with macroeconomic, financial and development related time series data for Uganda. Overall more than 5000 series at varying frequency (daily, monthly, quarterly, annual in fiscal or calendar years) can be accessed through the API. The data is provided by the Bank of Uganda, the Ugandan Ministry of Finance, Planning and Economic Development, the IMF and the World Bank. The database is being updated once a month.
Maintained by Sebastian Krantz. Last updated 2 years ago.
0.5 match 1.00 score 9 scripts