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collapse (version 1.5.0)

A7-time-series-panel-series: Time Series and Panel Series

Description

collapse provides the following functions to work with time-dependent data:

  • flag, and the lag- and lead- operators L and F are S3 generics to efficiently compute sequences of lags and leads on ordered or unordered regular / balanced or irregular / unbalanced time series and panel data.

  • Similarly, fdiff, fgrowth, and the operators D, Dlog and G are S3 generics to efficiently compute sequences of suitably lagged / leaded and iterated differences, log-differences and growth rates. fdiff/D/Dlog can also compute quasi-differences of the form \(x_t - \rho x_{t-1}\) or \(log(x_t) - \rho log(x_{t-1})\) for log-differences.

  • psmat is an S3 generic to efficiently convert panel-vectors or plm::pseries and data frames or plm::pdata.frame's to panel series matrices and 3D arrays, respectively.

  • psacf, pspacf and psccf are S3 generics to compute estimates of the auto-, partial auto- and cross- correlation or covariance functions for panel-vectors or plm::pseries, and multivariate versions for data frames or plm::pdata.frame's.

Arguments

Table of Functions

S3 Generic Methods Description
flag/L/F default, matrix, data.frame, pseries, pdata.frame, grouped_df Compute (sequences of) lags and leads
fdiff/D/Dlog default, matrix, data.frame, pseries, pdata.frame, grouped_df Compute (sequences of lagged / leaded and iterated) (quasi-)differences or (quasi-)log-differences
fgrowth/G default, matrix, data.frame, pseries, pdata.frame, grouped_df Compute (sequences of lagged / leaded and iterated) growth rates (exact or via log-differencing, in percentage terms)
psmat default, pseries, data.frame, pdata.frame Convert panel data to matrix / array
psacf default, pseries, data.frame, pdata.frame Compute ACF on panel data
pspacf default, pseries, data.frame, pdata.frame Compute PACF on panel data

See Also

Collapse Overview, Data Transformations