Summary and print methods for class 'dfm'. print.dfm just prints basic model information and the factor transition matrix \(\textbf{A}\),
summary.dfm returns all system matrices and additional residual and goodness of fit statistics - with a print method allowing full or compact printout.
an object class 'dfm'.
integer. The number of digits to print out.
not used.
character. The factor estimates to use: one of "qml", "2s" or "pca".
integer. Display a more compact printout: 0 prints everything, 1 omits the observation matrix \(\textbf{C}\) and residual covariance matrix cov(resid(model)), and 2 omits all disaggregated information on the input data. Sensible default are chosen for different sizes of the input dataset so as to limit large printouts.
Summary information following a dynamic factor model estimation.
mod = DFM(diff(BM14_Q), 2, 3)
#> Converged after 26 iterations.
print(mod)
#> Dynamic Factor Model: n = 9, T = 117, r = 2, p = 3, %NA = 7.5973
#>
#> Factor Transition Matrix [A]
#> L1.f1 L1.f2 L2.f1 L2.f2 L3.f1 L3.f2
#> f1 0.6789 0.2413 -0.034 -0.4640 -0.0012 -0.1988
#> f2 0.0353 0.2270 -0.026 0.0645 -0.0744 0.1802
summary(mod)
#> Dynamic Factor Model: n = 9, T = 117, r = 2, p = 3, %NA = 7.5973
#>
#> Call: DFM(X = diff(BM14_Q), r = 2, p = 3)
#>
#> Summary Statistics of Factors [F]
#> N Mean Median SD Min Max
#> f1 117 -0.0084 0.3469 2.2931 -14.408 3.7167
#> f2 117 0.003 0.0867 0.8146 -2.4636 2.1071
#>
#> Factor Transition Matrix [A]
#> L1.f1 L1.f2 L2.f1 L2.f2 L3.f1 L3.f2
#> f1 0.67890 0.2413 -0.03401 -0.46403 -0.001235 -0.1988
#> f2 0.03533 0.2270 -0.02598 0.06451 -0.074449 0.1802
#>
#> Factor Covariance Matrix [cov(F)]
#> f1 f2
#> f1 5.2584 0.1622
#> f2 0.1622 0.6636
#>
#> Factor Transition Error Covariance Matrix [Q]
#> u1 u2
#> u1 2.7065 0.2039
#> u2 0.2039 0.6618
#>
#> Observation Matrix [C]
#> f1 f2
#> gdp 0.4094 -0.1237
#> priv_cons 0.2755 -0.4353
#> invest 0.3810 -0.3022
#> export 0.3842 0.4215
#> import 0.3911 0.2106
#> empl 0.3072 -0.3443
#> prductivity 0.2894 0.0222
#> capacity 0.2933 0.0157
#> gdp_us 0.2511 0.1259
#>
#> Observation Error Covariance Matrix [diag(R) - Restricted]
#> gdp priv_cons invest export import empl
#> 0.0953 0.4616 0.1685 0.0301 0.1232 0.4160
#> prductivity capacity gdp_us
#> 0.2317 0.4737 0.6360
#>
#> Observation Residual Covariance Matrix [cov(resid(DFM))]
#> gdp priv_cons invest export import empl
#> gdp 0.0670 -0.0046 -0.0096 -0.0038* -0.0357* -0.0679*
#> priv_cons -0.0046 0.4173 -0.0735* 0.0059 0.0060 -0.0777*
#> invest -0.0096 -0.0735* 0.1263 -0.0010 -0.0210 -0.0620*
#> export -0.0038* 0.0059 -0.0010 0.0061 -0.0119* 0.0058
#> import -0.0357* 0.0060 -0.0210 -0.0119* 0.1090 0.0367
#> empl -0.0679* -0.0777* -0.0620* 0.0058 0.0367 0.3816
#> prductivity 0.0683* 0.0273 0.0173 -0.0024 -0.0748* -0.2119*
#> capacity -0.0470* -0.0978* -0.0320 -0.0201* 0.0624* 0.0628
#> gdp_us -0.0209 -0.0023 0.0045 -0.0095 -0.0252 -0.0236
#> prductivity capacity gdp_us
#> gdp 0.0683* -0.0470* -0.0209
#> priv_cons 0.0273 -0.0978* -0.0023
#> invest 0.0173 -0.0320 0.0045
#> export -0.0024 -0.0201* -0.0095
#> import -0.0748* 0.0624* -0.0252
#> empl -0.2119* 0.0628 -0.0236
#> prductivity 0.2215 -0.1119* -0.0496
#> capacity -0.1119* 0.4666 -0.0059
#> gdp_us -0.0496 -0.0059 0.6353
#>
#> Residual AR(1) Serial Correlation
#> gdp priv_cons invest export import empl
#> -0.149924 -0.272110 -0.206251 -0.215038 -0.007949 0.434361
#> prductivity capacity gdp_us
#> 0.053696 0.091852 0.179658
#>
#> Summary of Residual AR(1) Serial Correlations
#> N Mean Median SD Min Max
#> 9 -0.0102 -0.0079 0.2282 -0.2721 0.4344
#>
#> Goodness of Fit: R-Squared
#> gdp priv_cons invest export import empl
#> 0.9330 0.5827 0.8737 0.9939 0.8910 0.6184
#> prductivity capacity gdp_us
#> 0.7785 0.5334 0.3647
#>
#> Summary of Individual R-Squared's
#> N Mean Median SD Min Max
#> 9 0.7299 0.7785 0.2139 0.3647 0.9939