| 模型 | 模型答案 | 正确答案 |
|---|---|---|
| 金声玉亮2.0 | D. (i), (ii), (iii), and (iv) Explanation: The Dickey-Fuller/Engle-Granger approach to testing for cointegration and modeling cointegrating relationships has several disadvantages: 1. Only one cointegrating relationship can be estimated (i). This is a limitation because there may be multiple cointegrating relationships among the variables. 2. The tests have low power, particularly for small samples, which means there is a high chance of the tests suggesting that variables are not cointegrated when they are (ii). 3. It is not possible to make inferences on the cointegrating regression (iii). This is because the cointegrating regression is not a standard regression model, and the usual inference procedures do not apply. 4. The procedure forces the researcher to specify which is the dependent variable and which are the independent variables (iv). This can be a problem if the researcher is not sure which variable should be the dependent variable. Therefore, all four options are correct, and the answer is D. (i), (ii), (iii), and (iv). |
D |
| deepseekr1 | The Dickey-Fuller/Engle-Granger approach has several disadvantages: 1. **i)** It can only estimate one cointegrating relationship, even if multiple exist. 2. **ii)** In small samples, the test has low power, increasing the risk of falsely concluding no cointegration (Type II error). 3. **iii)** Inferences on the cointegrating regression are unreliable due to non-standard distributions of OLS estimates. 4. **iv)** The method requires pre-specifying dependent and independent variables, unlike multivariate approaches (e.g., Johansen). All four points are valid disadvantages. **Answer: D** |
D |
| gpto1 | D | D |
| GPT-4o | D. (i), (ii), (iii), and (iv) | D |
| llama3.1-70B-Instruct | D |
D |
| llama3.1-405B-Instruct | D. (i), (ii), (iii), and (iv) The Dickey-Fuller/Engle-Granger approach to testing for cointegration and modeling cointegrating relationships has several disadvantages, including: 1. Only one cointegrating relationship can be estimated: This approach is limited to identifying a single cointegrating relationship, even if there are multiple relationships present in the data. 2. Particularly for small samples, there is a high chance of the tests suggesting that variables are not cointegrated when they are: The Dickey-Fuller test has low power in small samples, which means that it may fail to detect cointegration even when it is present. 3. It is not possible to make inferences on the cointegrating regression: The Engle-Granger approach does not provide a framework for making inferences about the cointegrating regression, such as testing hypotheses about the coefficients. 4. The procedure forces the researcher to specify which is the dependent variable and which are the independent variables: The Engle-Granger approach requires the researcher to specify the dependent variable and independent variables, which can be arbitrary and may not reflect the underlying relationships in the data. Therefore, all four statements are correct, and the correct answer is D. (i), (ii), (iii), and (iv). |
D |
| DeepSeek-Coder-V2 | D | D |
| Qwen2-72B-Instruct | D. (i), (ii), (iii), and (iv) | D |
| baichuan4 | D. (i), (ii), (iii), and (iv) | D |
| doubao | D The Dickey-Fuller/Engle-Granger approach has the limitations mentioned in all the options: it can estimate only one cointegrating relationship; for small samples, there is a high risk of incorrect non-cointegration findings; it has limitations in making inferences on the cointegrating regression; and it requires specifying dependent and independent variables. |
D |
| minimax | D. (i), (ii), (iii), and (iv) | D |
| kimi | D. (i), (ii), (iii), and (iv) | D |
| wenxinyiyan | D. (i), (ii), (iii), and (iv) Explanation: The Dickey-Fuller / Engle-Granger approach to testing for cointegration and modelling cointegrating relationships has several disadvantages: i) Only one cointegrating relationship can be estimated - This is true. The Engle-Granger approach focuses on a single cointegrating relationship at a time. ii) Particularly for small samples, there is a high chance of the tests suggesting that variables are not cointegrated when they are - This is also true. Small sample sizes can lead to inaccurate conclusions about cointegration. iii) It is not possible to make inferences on the cointegrating regression - Correct. The Engle-Granger approach does not provide a direct way to make statistical inferences on the cointegrating regression itself. iv) The procedure forces the researcher to specify which is the dependent variable and which are the independent variables - This is accurate. In the Engle-Granger method, the researcher must explicitly define the dependent and independent variables, which may not always be clear or appropriate. Therefore, all four statements are disadvantages of the Dickey-Fuller / Engle-Granger approach. |
D |
| zhipu | D. (i), (ii), (iii), and (iv) Explanation: - (i) The Dickey-Fuller / Engle-Granger approach typically allows for the estimation of only one cointegrating relationship. - (ii) Small sample sizes can lead to a high risk of Type II error, meaning the test might incorrectly suggest that variables are not cointegrated when they actually are. - (iii) Inferences on the cointegrating regression can be problematic due to issues like endogeneity and serial correlation. - (iv) The procedure requires the researcher to specify a dependent variable and independent variables, which can be limiting and potentially biases the analysis. Thus, all the listed points are disadvantages of the Dickey-Fuller / Engle-Granger approach. |
D |
| gpto1 | D | D |