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NNS Nowcast

Source: R/Nowcast.R
NNS.nowcast.Rd

Wrapper function for NNS nowcasting method using the nonparametric vector autoregression NNS.VAR, and Federal Reserve Nowcasting variables.

Usage

NNS.nowcast(
  h = 1,
  additional.regressors = NULL,
  additional.sources = NULL,
  naive.weights = FALSE,
  specific.regressors = NULL,
  start.date = "2000-01-03",
  keep.data = FALSE,
  status = TRUE,
  ncores = NULL
)

Arguments

h

integer; (h = 1) (default) Number of periods to forecast. (h = 0) will return just the interpolated and extrapolated values up to the current month.

additional.regressors

character; NULL (default) add more regressors to the base model. The format must utilize the getSymbols format for FRED data, else specify the source.

additional.sources

character; NULL (default) specify the source argument per getSymbols for each additional.regressors specified.

naive.weights

logical; TRUE Equal weights applied to univariate and multivariate outputs in ensemble. FALSE (default) will apply weights based on the number of relevant variables detected.

specific.regressors

integer; NULL (default) Select individual regressors from the base model per Viole (2020) listed in the Note below.

start.date

character; "2000-01-03" (default) Starting date for all data series download.

keep.data

logical; FALSE (default) Keeps downloaded variables in a new environment NNSdata.

status

logical; TRUE (default) Prints status update message in console.

ncores

integer; value specifying the number of cores to be used in the parallelized subroutine NNS.ARMA.optim. If NULL (default), the number of cores to be used is equal to the number of cores of the machine - 1.

Value

Returns the following matrices of forecasted variables:

  • "interpolated_and_extrapolated" Returns a data.frame of the linear interpolated and NNS.ARMA extrapolated values to replace NA values in the original variables argument. This is required for working with variables containing different frequencies, e.g. where NA would be reported for intra-quarterly data when indexed with monthly periods.

  • "relevant_variables" Returns the relevant variables from the dimension reduction step.

  • "univariate" Returns the univariate NNS.ARMA forecasts.

  • "multivariate" Returns the multi-variate NNS.reg forecasts.

  • "ensemble" Returns the ensemble of both "univariate" and "multivariate" forecasts.

Note

Specific regressors include:

  1. PAYEMS – Payroll Employment

  2. JTSJOL – Job Openings

  3. CPIAUCSL – Consumer Price Index

  4. DGORDER – Durable Goods Orders

  5. RSAFS – Retail Sales

  6. UNRATE – Unemployment Rate

  7. HOUST – Housing Starts

  8. INDPRO – Industrial Production

  9. DSPIC96 – Personal Income

  10. BOPTEXP – Exports

  11. BOPTIMP – Imports

  12. TTLCONS – Construction Spending

  13. IR – Import Price Index

  14. CPILFESL – Core Consumer Price Index

  15. PCEPILFE – Core PCE Price Index

  16. PCEPI – PCE Price Index

  17. PERMIT – Building Permits

  18. TCU – Capacity Utilization Rate

  19. BUSINV – Business Inventories

  20. ULCNFB – Unit Labor Cost

  21. IQ – Export Price Index

  22. GACDISA066MSFRBNY – Empire State Mfg Index

  23. GACDFSA066MSFRBPHI – Philadelphia Fed Mfg Index

  24. PCEC96 – Real Consumption Spending

  25. GDPC1 – Real Gross Domestic Product

  26. ICSA – Weekly Unemployment Claims

  27. DGS10 – 10-year Treasury rates

  28. T10Y2Y – 2-10 year Treasury rate spread

  29. WALCL – Total Assets

  30. PALLFNFINDEXM – Global Price Index of All Commodities

  31. FEDFUNDS – Federal Funds Effective Rate

  32. PPIACO – Producer Price Index All Commodities

  33. CIVPART – Labor Force Participation Rate

  34. M2NS – M2 Money Supply

  35. ADPMNUSNERNSA – ADP Payrolls

References

Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments" (ISBN: 1490523995)

Viole, F. (2019) "Multi-variate Time-Series Forecasting: Nonparametric Vector Autoregression Using NNS" doi:10.2139/ssrn.3489550

Viole, F. (2020) "NOWCASTING with NNS" doi:10.2139/ssrn.3589816

Author

Fred Viole, OVVO Financial Systems

Examples


 if (FALSE) { # \dontrun{
 ## Interpolates / Extrapolates all variables to current month
 NNS.nowcast(h = 0)
 
 ## Additional regressors and sources specified
 NNS.nowcast(h = 0, additional.regressors = c("SPY", "USO"), 
             additional.sources = c("yahoo", "yahoo"))
             
              
 ### PREDICTION INTERVALS 
 ## Store NNS.nowcast output
 nns_estimates <- NNS.nowcast(h = 12)           
 
 # Create bootstrap replicates using NNS.meboot (GDP Variable)
 gdp_replicates <- NNS.meboot(nns_estimates$ensemble$GDPC1, 
                              rho = seq(0,1,.25), 
                              reps = 100)["replicates",]
                              
 replicates <- do.call(cbind, gdp_replicates)
 
 # Apply UPM.VaR and LPM.VaR for desired prediction interval...95 percent illustrated
 # Tail percentage used in first argument per {LPM.VaR} and {UPM.VaR} functions
 lower_GDP_CIs <- apply(replicates, 1, function(z) LPM.VaR(0.025, 0, z))
 upper_GDP_CIs <- apply(replicates, 1, function(z) UPM.VaR(0.025, 0, z))
 
 # View results
 cbind(nns_estimates$ensemble$GDPC1, lower_GDP_CIs, upper_GDP_CIs)
 } # }