Modeling the Price Volatility of Cassava Chips in Thailand: Evidence from Bayesian GARCH-X Estimates
Abstract
:1. Introduction
2. Research Methodology
2.1. The Unit Root Test Using Bayesian Estimation
2.2. The GARCH-X Using Bayesian Estimation
3. Empirical Results
3.1. Data Descriptive
3.2. Stationary Testing
3.3. The Estimation of GARCH-X(1,1) Using Bayesian Inference
4. Discussion
5. Conclusions and Policy Recommendation
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Items | Interpretation |
---|---|
BF < 1/10 | Strong evidence for |
1/10 < BF < 1/3 | Moderate evidence for |
1/3 < BF < 1 | Weak evidence for |
1 < BF < 3 | Weak evidence for |
3 < BF < 10 | Moderate evidence for |
10 < BF | Strong evidence for |
Statistics | Y | X1 | X2 | X3 | X4 | X5 | X6 |
---|---|---|---|---|---|---|---|
Mean | 0.0019 | 0.0042 | 0.0016 | 0.0031 | 0.0019 | 0.0028 | 0.0019 |
Median | 0.0110 | −0.0172 | 0.0000 | −0.0093 | 0.0010 | 0.0000 | −0.0317 |
Max. | 0.2943 | 3.4250 | 0.2877 | 3.3891 | 0.3236 | 0.2395 | 3.1231 |
Min. | −0.3044 | −2.5720 | −0.2881 | −2.5388 | −0.3332 | −0.2332 | −2.2706 |
Std.Dev. | 0.1931 | 0.6841 | 0.1894 | 0.6777 | 0.1956 | 0.1886 | 0.6254 |
Skewness | −0.0578 | 0.7161 | −0.0364 | 0.6982 | −0.0516 | −0.0121 | 0.7895 |
Kurtosis | 1.5083 | 9.3457 | 1.5001 | 9.3080 | 1.5821 | 1.4137 | 9.1423 |
Jarque-Bera | 9.6073 | 181.6222 | 9.6779 | 179.1358 | 8.6737 | 10.8018 | 172.6151 |
Probability | 0.0082 | 0.0000 | 0.0079 | 0.0000 | 0.0131 | 0.0045 | 0.0000 |
Variables | Bayesian Factor Ratios (M1/M2) | Implication | Result |
---|---|---|---|
Cassava chip price of Thailand (Y) | 1.65 × 10−31 | Strong evidence for Mj | I(0) |
China’s cassava import volume from Thailand (X1) | 2.2 × 10−17 | Strong evidence for Mj | I(0) |
China’s cassava chips import price from Thailand (X2) | 5.3 × 10−32 | Strong evidence for Mj | I(0) |
China’s cassava starch import price from Thailand (X3) | 1.14 × 10−17 | Strong evidence for Mj | I(0) |
Substitute crop price: maize (X4) | 3.33 × 10−29 | Strong evidence for Mj | I(0) |
Substitute crop price: wheat (X5) | 3.97 × 10−39 | Strong evidence for Mj | I(0) |
Thailand’s cassava products export volume (X6) | 2.24 × 10−16 | Strong evidence for Mj | I(0) |
Variables | X1 | X2 | X3 | |||
---|---|---|---|---|---|---|
Coefficient | 95%CI | Coefficient | 95%CI | Coefficient | 95%CI | |
−0.0002 (0.000473) | (−0.0006, 0.0012) | 0.0002 (0.0004) | (−0.0005, 0.0012) | 0.0003 (0.0040) | (−0.0006, 0.0210) | |
0.0023 (0.100100) | (0.0021, 0.1927) | 0.0025 (0.0990) | (0.0021, 0.1922) | 0.0024 (0.0091) | (0.0021, 0.1920) | |
0.0326 (0.090000) | (0.0100, 0.6900) | 0.0234 (0.0900) | (0.0960, 0.7100) | 0.3618 (0.0910) | (0.0396, 0.9071) | |
0.0006 (0.000494) | (0.0001, 0.0009) | 0.0010 (0.0045) | (0.0009, 0.0015) | 0.0001 (0.0005) | (0.00009, 0.0096) | |
Sigma2 | 0.0001 (0.000002) | (0.00009, 0.00024) | 0.0002 (0.0010) | (0.00008, 0.0010) | 0.0035 (0.0001) | (0.0006, 0.0140) |
Variables | X4 | X5 | X6 | |||
---|---|---|---|---|---|---|
Coefficient | 95%CI | Coefficient | 95%CI | Coefficient | 95%CI | |
−0.0029 (0.0005) | (−0.0065, 0.0013) | 0.0037 (0.0050) | (−0.0067, 0.0062) | −0.0062 (0.0054) | (−0.0560, 0.0122) | |
0.0032 (0.0104) | (0.0020, 0.1935) | 0.0025 (0.1004) | (0.0020, 0.1931) | 0.0029 (0.0900) | (0.0019, 0.0640) | |
0.0249 (0.0800) | (0.0003, 0.7690) | 0.0240 (0.0800) | (0.0090, 0.7000) | 0.0233 (0.9990) | (0.0037, 0.9071) | |
0.0600 (0.0019) | (0.0003, 0.0786) | −0.0210 (0.0207) | (−0.0411, 0.0030() | 0.0102 (0.0006) | (0.0090, 0.0102) | |
Sigma2 | 0.0010 (0.0002) | (0.0076, 0.0035) | 0.0100 (0.0100) | (0.00008, 0.0014) | 0.0004 (0.0080) | (0.0008, 0.0078) |
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Singvejsakul, J.; Chaovanapoonphol, Y.; Limnirankul, B. Modeling the Price Volatility of Cassava Chips in Thailand: Evidence from Bayesian GARCH-X Estimates. Economies 2021, 9, 132. https://doi.org/10.3390/economies9030132
Singvejsakul J, Chaovanapoonphol Y, Limnirankul B. Modeling the Price Volatility of Cassava Chips in Thailand: Evidence from Bayesian GARCH-X Estimates. Economies. 2021; 9(3):132. https://doi.org/10.3390/economies9030132
Chicago/Turabian StyleSingvejsakul, Jittima, Yaovarate Chaovanapoonphol, and Budsara Limnirankul. 2021. "Modeling the Price Volatility of Cassava Chips in Thailand: Evidence from Bayesian GARCH-X Estimates" Economies 9, no. 3: 132. https://doi.org/10.3390/economies9030132