Eviews tutorials

Here you can find a list of examples from the book and the link to the video-tutorial that replicates their results using Eviews. You can find all the available tutorials on the Youtube channel of the website. Some workfiles with additional exercises are provided here.

Chapter 1

In this set of tutorials you will learn:

1. how to run an OLS regression and save the output,
2. how to produce scatter plots to investigate the linearity assumption,
3. how to run a Ramsey RESET test.

Example 1.2 uses Fama and French dataset, while Examples 1.6 and 1.7 are based on Shiller’s data.

• Example 1.2, p. 7
• Example 1.6, p.20
• Example 1.7, p. 22

Chapter 2

In this set of tutorials you will learn:

1. how to plot the correlogram of a series,
2. how to choose the appropriate ARMA model using information criteria,
3. how to estimate an ARMA model,
4. how to test residuals.

The data used in Example 2.3 are simulated from an AR(1) process and from a white noise process.
The data used in Examples 2.8-2.10 are changes in the CPI Index contained in Shiller’s dataset.

• Example 2.3, p. 47
• Example 2.8, p.64
• Example 2.9, p. 66
• Example 2.10, p. 69

Chapter 3

In this set of tutorials you will learn:

1. how to plot two series in a chart and compute their sample statistics, including correlations and cross-correlations,
2. how to estimate a VAR model,
3. how to make forecasts and compare forecast accuracy,
4. how to plot impulse response functions,
5. how to compute variance decomposition,
6. how to test for Granger causality.

In these examples we use data on US Treasury Rates.

• Example 3.1, p. 79
• Example 3.3, p. 92
• Example 3.5, p. 98
• Example 3.7, p. 104
• Example 3.8, p. 106
• Example 3.9 p. 110

Chapter 4

In this set of tutorials you will learn:

1. how to test for the presence of unit root,
2. how to test for cointegration using Johansen’s method.

In Example 4.3 we use S&P price index, dividends and earnings coming from Shiller’s dataset while in Example 4.7 we use US Treasury yields.

• Example 4.3 (part 1), p. 127
• Example 4.3 (part 2), p. 130
• Example 4.7, p. 146

Chapter 5

In this set of tutorials you will learn how to estimate a number of different conditionally heteroskedastic models including:

1. Riskmetrics,
2. GARCH,
3. EGARCH,
4. CGARCH,
5. GARCH-in-mean.

The examples in this part use data returns of the S&P price index from Shiller’s dataset and US Treasury yields.

• Example 5.8, p. 164
• Example 5.10, p. 169
• Example 5.14, p. 180
• Example 5.16, p. 187
• Example 5.22, p. 207

Chapter 6

In this set of tutorials you will learn to estimate VECH and BEKK models.

These examples use US stock returns data.

• Example 6.2, p. 236
• Example 6.4, p. 242

Chapter 7

Concerning Chapter 7, you can find two workfiles that replicate the examples 7.1 and 7.3 here:

• Example 7.1, p. 274
• Example 7.3, p. 276

Chapter 8

In this set of tutorials you will learn:

1. how to apply a number of different tests for breaks,
2. how to estimate a regression with breaks,
3. how to estimate a threshold regression,
4. how to estimate a smooth transition regression.

The data used in the examples are from Fama and French’s dataset (see also predictability in F-F data factors).

• Example 8.1 (part 1), p. 291
• Example 8.1 (part 2), p. 296
• Example 8.4, p. 312
• Example 8.6, p. 319

Chapter 9

In this set of tutorials you are going to learn:

1. how to estimate a range of different Markov switching models,
2. how to plot transition, filtered, and smoothed probabilities.

The data used in the examples are from the International Stock Returns dataset.

• Example 9.2, p. 342
• Example 9.3, p. 347
• Example 9.5, p. 360