My thesis includes variables of inflation rate (monthly), interest rate (weekly), exchange rate (daily), and crude oil production (monthly), as well as imports and exports (annually). The period is from 2018 to 2024. I will use many pre-tests such as unit root tests, cointegration tests, etc. Before starting, I am worried about the missing data. My missing data is in the exchange rate and interest rate. I need guidance. Since I want to use panel data, is interpolation useful for time series data? Will it provide accurate results? Please help and guide me. Thank you.
To replace missing data in a time series using EViews, you can use interpolation methods. Here's a short guide:
This will replace the missing data in your time series with interpolated values based on the chosen method.
To replace missing data in a time series using EViews, you can follow these steps:
1. **Open your time series data in EViews**: Load the data into EViews and make sure the variable with the missing data is selected.
2. **Identify and mark the missing values**: In EViews, you can identify missing values by looking for the "NA" or "?" symbols in the data. You can also use the "View" menu and select "View Data" to see the data in a spreadsheet-like format, where missing values will be highlighted.
3. **Choose a method to replace the missing values**: EViews provides several options to replace missing data, including:
- **Mean/Median Substitution**: Replace the missing values with the mean or median of the non-missing values.
- **Linear Interpolation**: Replace the missing values by linearly interpolating between the nearest non-missing values.
- **Time Series Modeling**: Use time series models, such as ARIMA or Exponential Smoothing, to estimate the missing values based on the patterns in the available data.
4. **Replace the missing values**: Once you've chosen a method, you can use the "Fill Missing" command in EViews to replace the missing value.
5. **Verify the results**: After replacing the missing values, review the data to ensure that the substitutions are appropriate and do not introduce significant biases or distortions.
Souad Djegham Dear Souad Thanks a lot for your advice.I will be sure to follow these steps.
With missing observations, the most important thing to consider is why the observations are missing and only then consider how to impute or not.
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