Table 4 Production modelling by FVAR-FTN.

From: Enhanced forecasting of rice price and production in Malaysia using novel multivariate fuzzy time series models

Mean dependent var

4.429486

S.D. dependent var

3.285684

Sum square resid

268.3966

S.E. of regression

3.416053

R-square

0.471034

Adjusted R-square

0.680930

F(24, 23)

0.853378

P-value(F)

0.009183

MAPE

0.227710

Durbin-Watson

1.647108

  1. F-tests of zero restrictions:
  2. \({\text{All lags of TPd F}}\left( {{\text{12}},{\text{ 23}}} \right)\,=\,0.{\text{9442 }}\left[ {0.0{\text{234}}} \right].\)\({\text{All lags of TPr F}}\left( {{\text{12}},{\text{ 23}}} \right)\,=\,0.{\text{81897 }}\left[ {0.0{\text{3}}0{\text{5}}} \right].\)\({\text{All vars, lag 12 F}}\left( {{\text{2, 23}}} \right)\,{\text{ = }}\,{\text{2}}{\text{.0885 }}\left[ {{\text{0}}{\text{.0146}}} \right].\).
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