OPG error management

onnokleen · May 12, 2020 · 0e8f05f · 0e8f05f
1 parent 9dfde37
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Showing 10 changed files with 82 additions and 21 deletions.
diff --git a/CRAN-RELEASE b/CRAN-RELEASE
@@ -0,0 +1,2 @@
+This package was submitted to CRAN on 2019-12-04.
+Once it is accepted, delete this file and tag the release (commit 9dfde37cd9).
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,14 +1,14 @@
 Package: mfGARCH
 Title: Mixed-Frequency GARCH Models
-Version: 0.1.9
+Version: 0.2.0
 Authors@R: person("Onno", "Kleen", email = "r@onnokleen.de", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-4731-4640"))
 Description: Estimating GARCH-MIDAS (MIxed-DAta-Sampling) models (Engle, Ghysels, Sohn, 2013, <doi:10.1162/REST_a_00300>) and related statistical inference, accompanying the paper "Two are better than one: volatility forecasting using multiplicative component GARCH models" by Conrad and Kleen (2018, <doi:10.2139/ssrn.2752354>). The GARCH-MIDAS model decomposes the conditional variance of (daily) stock returns into a short- and long-term component, where the latter may depend on an exogenous covariate sampled at a lower frequency. 
 Depends:
     R (>= 3.3.0)
 License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 6.1.1
+RoxygenNote: 7.1.0
 Imports:
     Rcpp,
     graphics,

diff --git a/NEWS.md b/NEWS.md
@@ -1,3 +1,6 @@
+v0.2.0
+- Better error management for OPG standard errors
+
 v0.1.9
 - Compatibility with R 4.0.0
 

diff --git a/R/fit_mfgarch.R b/R/fit_mfgarch.R
@@ -882,12 +882,12 @@ fit_mfgarch <- function(data, y, x = NULL, K = NULL, low.freq = "date", var.rati
 
   opg.std.err <- try({sqrt(diag(solve(crossprod(jacobian(func = function(theta) -lf(theta), x = par)))))},
                      silent = TRUE)
-  if (class(inv_hessian)[1] == "try-error") {
+  if (class(opg.std.err)[1] == "try-error") {
     warning("Inverting the OPG matrix failed. No OPG standard errors calculated.")
     opg.std.err <- NA
+  } else {
+    opg.std.err <- opg.std.err * sqrt((mean(df.fitted$residuals^4, na.rm = TRUE) - 1) / 2)
   }
-  # browser()
-  opg.std.err <- opg.std.err * sqrt((mean(df.fitted$residuals^4, na.rm = TRUE) - 1) / 2)
 
   if (class(inv_hessian)[1] == "try-error") {
     warning("Inverting the Hessian matrix failed. No robust standard errors calculated. Possible workaround: Multiply returns by 100.")

diff --git a/man/df_financial.Rd b/man/df_financial.Rd
diff --git a/man/df_mfgarch.Rd b/man/df_mfgarch.Rd
diff --git a/man/fit_mfgarch.Rd b/man/fit_mfgarch.Rd
diff --git a/man/simulate_mfgarch.Rd b/man/simulate_mfgarch.Rd
diff --git a/man/simulate_mfgarch_diffusion.Rd b/man/simulate_mfgarch_diffusion.Rd
diff --git a/man/simulate_mfgarch_rv_dependent.Rd b/man/simulate_mfgarch_rv_dependent.Rd