An API for automatic data queries to the fedstat <https://www.fedstat.ru>, using a small set of functions with a common interface.

Flow of funds are financial accounts that are provided by Federal Reserve quarterly. The package contains all
    datasets <https://www.federalreserve.gov/datadownload/Choose.aspx?rel=z1>, tables <https://www.federalreserve.gov/apps/fof/FOFTables.aspx> 
    and descriptions <https://www.federalreserve.gov/apps/fof/Guide/z1_tables_description.pdf> with functions to understand series <https://www.federalreserve.gov/apps/fof/SeriesStructure.aspx> and explore them.

Estimates the first-exposure effect (FEE) using a
    one-inflated positive Poisson model, or a one-inflated zero-truncated
    negative binomial model. In addition, estimates the marginal FEE, and
    standard errors for the FEE and marginal FEE.

Implement and fit a variety of short-memory (SM) and long-memory
  (LM) models from a very broad family of exponential generalized autoregressive
  conditional heteroskedasticity (EGARCH) models, such as a MEGARCH (modified
  EGARCH), FIEGARCH (fractionally integrated EGARCH), FIMLog-GARCH (fractionally
  integrated modulus Log-GARCH), and more. The FIMLog-GARCH as part of the
  EGARCH family is discussed in Feng et al. (2023)
  <https://econpapers.repec.org/paper/pdnciepap/156.htm>. For convenience and
  the purpose of comparison, a variety of other popular SM and LM GARCH-type
  models, like an APARCH model, a fractionally integrated
  APARCH (FIAPARCH) model, standard GARCH and fractionally integrated GARCH
  (FIGARCH) models, GJR-GARCH and FIGJR-GARCH models, TGARCH and FITGARCH
  models, are implemented as well as dual models with simultaneous modelling of
  the mean, including dual long-memory models with a fractionally integrated
  autoregressive moving average (FARIMA) model in the mean and a long-memory
  model in the variance, and semiparametric volatility model extensions.
  Parametric models and parametric model parts are fitted through
  quasi-maximum-likelihood estimation.
  Furthermore, common forecasting and backtesting functions for value-at-risk
  (VaR) and expected shortfall (ES) based on the package's models are provided.