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sTSD: Simulate Time Series Diagnostics

{sTSD} provides a suite of functions for the analyses of time series, with an initial focus on diagnostic tests for unit root. Its primary aim concerns the simulation of critical values that are almost always approximated or interpolated by reference to tables of critical values passed down from decades-old texts. While there is nothing necessarily wrong with the received wisdom of critical values generated decades ago, simulation provides its own perks. Not only is simulation broadly informative as to what these various test statistics do and what are their plausible values, simulation provides more flexibility for assessing unit root by way of different thresholds or different hypothesized distributions.

Installation

This package is now on CRAN. You can download it as you would any other R package.

install.packages("sTSD")

You can also install the development version of {sTSD} from Github via the {devtools} package. I suppose using the {remotes} package would work as well.

devtools::install_github("svmiller/sTSD")

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Version

Install

install.packages('sTSD')

Monthly Downloads

327

Version

0.2.0

License

GPL (>= 2)

Maintainer

Steve Miller

Last Published

January 27th, 2025

Functions in sTSD (0.2.0)

exCopdab

Dynamic Foreign Policy Behavior (COPDAB)
sim_ts

Simulate a Time Series
USDICE

Quarterly disposable income and personal consumption expenditures in the United States
USDSEK

The USD/SEK Exchange Rate
tbills

Daily maturity rates for U.S. Treasury Bills
spp_test

Simulate a Phillips-Perron Test to Assess Unit Root in a Time Series
ur_summary

Summarize Unit Root Test Simulations
skpss_test

Simulate a KPSS Test to Assess Unit Root in a Time Series
sadf_test

Simulate a (Augmented) Dickey-Fuller Test to Assess Unit Root in a Time Series
sim_df_mod

Simulate an Individual (Augmented) Dickey-Fuller Model
money_demand

Quarterly Money Demand in the United States
adf_lag_select

Identify Optimal Lag Order Selection for (Augmented) Dickey-Fuller Tests
lag_suggests

Suggested Lags for Your Time Series