Table 1 Production modelling by VAR.

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

Mean dependent var

3.425729

S.D. dependent var

4.298571

Sum square resid

429.0540

S.E. of regression

4.319088

R-square

0.505956

Adjusted R-square

0.579569

F(24, 23)

0.981439

P-value(F)

0.519089

MAPE

0.267266

Durbin-Watson

1.820349

  1. F-tests of zero restrictions:
  2. \({\text{All }}\;{\text{lags}}\;{\text{ of }}\;{\text{Production F}}\left( {{\text{12}},{\text{ 23}}} \right)\,=\,{\text{1}}0.0{\text{667 }}\left[ {0.{\text{4286}}} \right].\)\({\text{All}}\;{\text{ lags }}\;{\text{of}}\;{\text{ Price F}}\left( {{\text{12}},{\text{ 23}}} \right)\,=\,0.{\text{62568 }}\left[ {0.{\text{7992}}} \right].\)\({\text{All}}\;{\text{ vars,}}\;{\text{ lag 12 F}}\left( {{\text{2, 23}}} \right)\,{\text{ = }}\,{\text{2}}{\text{.7515 }}\left[ {{\text{0}}{\text{.0848}}} \right].\).
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