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The text provided in the image is in Greek and relates to statistical and econometric methods. Here's a translation and breakdown of the tasks:

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**Given the data \( y_t, \ t = 1, \dots, T \), derived from the linear model:**

\[
y_t = a + b t + u_t, \ t = 1, \dots, T
\]

1. **Using the method of least squares, find the estimators of the parameters \( a \) and \( b \) based on the given data.**

2. **Apply the method of maximum likelihood and find the estimators of the parameters \( a \) and \( b \). What do you observe?**

3. **What is the interpretation of the parameters \( a \) and \( b \)?**

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This is a problem that involves both the **ordinary least squares (OLS)** and the **maximum likelihood estimation (MLE)** methods, commonly used to estimate parameters in regression models.

Here’s a brief explanation of what you need to do for each part:

### 1. Ordinary Least Squares (OLS)
Using OLS involves minimizing the sum of the squared residuals:

\[
\min_{a, b} \sum_{t=1}^{T} (y_t - (a + b t))^2
\]

The resulting estimators for \( a \) and \( b \) can be derived using the normal equations.

### 2. Maximum Likelihood Estimation (MLE)
MLE is another method to estimate the parameters. Assuming that the errors \( u_t \) are normally distributed, you can derive the likelihood function and maximize it with respect to \( a \) and \( b \).

### 3. Interpretation of Parameters
- \( a \) represents the intercept, which is the value of \( y \) when \( t = 0 \).
- \( b \) represents the slope or the rate of change in \( y \) with respect to \( t \).

Would you like me to walk you through the solutions for OLS and MLE in detail?

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Here are 5 related questions to consider:

1. What assumptions are made in OLS regarding the error terms \( u_t \)?
2. How would you compute the confidence intervals for \( a \) and \( b \)?
3. How does the presence of heteroskedasticity affect the OLS estimators?
4. What are the advantages of MLE over OLS in certain cases?
5. How would the estimation process change if the error terms \( u_t \) were autocorrelated?

**Tip**: When performing OLS, it's important to check whether the assumptions (such as homoscedasticity and no autocorrelation) hold to ensure valid estimates.

Given the data y_t, t = 1,...,T derived from the linear model: y_t = a + b * t + u_t, t = 1,...,T. 

1. Using the method of least squares, find the estimators of the parameters a and b based on the given data.
2. Apply the method of maximum likelihood and find the estimators of the parameters a and b. What do you observe?
3. What is the interpretation of the parameters a and b?

Learn how to estimate the parameters a and b in a linear regression model using both Ordinary Least Squares (OLS) and Maximum Likelihood Estimation (MLE). This problem explores key statistical concepts such as linear regression, normal equations, and the interpretation of parameters. Suitable for university-level students studying statistics or econometrics.

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