Implements nonlinear autoregressive (AR) time series models. For univariate series, a non-parametric approach is available through additive nonlinear AR. Parametric modeling and testing for regime switching dynamics is available when the transition is either direct (TAR: threshold AR) or smooth (STAR: smooth transition AR, LSTAR). For multivariate series, one can estimate a range of TVAR or threshold cointegration TVECM models with two or three regimes. Tests can be conducted for TVAR as well as for TVECM (Hansen and Seo 2002 and Seo 2006).

Calculates total survey error (TSE) for one or more surveys, using common scale-dependent and/or scale-independent metrics.  On TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) <doi:10.1093/poq/nfq058>.

The library allows to perform a multivariate time series
    classification based on the use of Discrete Wavelet Transform for
    feature extraction, a step wise discriminant to select the most
    relevant features and finally, the use of a linear or quadratic
    discriminant for classification. Note that all these steps can be done
    separately which allows to implement new steps.  Velasco, I., Sipols,
    A., de Blas, C. S., Pastor, L., & Bayona, S. (2023)
    <doi:10.1186/S12938-023-01079-X>.  Percival, D. B., & Walden, A. T.
    (2000,ISBN:0521640687).  Maharaj, E. A., & Alonso, A. M. (2014)
    <doi:10.1016/j.csda.2013.09.006>.

Autoregressive distributed lag (A[R]DL) models (and their reparameterized equivalent, the Generalized Error-Correction Model [GECM]) are the workhorse models in uncovering dynamic inferences. ADL models are simple to estimate; this is what makes them attractive. Once these models are estimated, what is less clear is how to uncover a rich set of dynamic inferences from these models. We provide tools for recovering those inferences. These tools apply to traditional time-series quantities of interest and are built from the Impulse Response Function and Step Response Function (sometimes described as a pulse effect or a cumulative effect). They also allow for a variety of shock histories to be applied to the independent variable (beyond just a one-time, one-unit increase) as well as the recovery of inferences in levels for shocks applied to (in)dependent variables in differences (what we call the Generalized Dynamic Response Function). These effects are also available for the general conditional dynamic model advocated by Warner, Vande Kamp, and Jordan (2026 <doi:10.1017/psrm.2026.10087>). We also provide the formulae for these effects.