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Solutions Manual for Essentials of Econometrics 5th Edition by Damodar N. Gujarati , ISBN: 9781071850398 |All Chapters Covered| Guide A+

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Complete Solutions Manual for Essentials of Econometrics 5e 5th Edition by Damodar N. Gujarati. Full Chapters Solutions are included. Chapter 1 to 12 - Appendixes Solutions are included. Chapter 1. The Nature and Scope of Econometrics 1.1 What Is Econometrics? 1.2 Why Study Econometrics? 1.3 The Methodology Of Econometrics 1.4 The Road Ahead Key Terms and Concepts Questions Problems Appendix 1A: Economic Data on the World Wide Web PART I. THE LINEAR REGRESSION MODEL Chapter 2. Basic Ideas of Linear Regression: The Two-Variable Model 2.1 The Meaning of Regression 2.2 The Population Regression Function (PRF): A Hypothetical Example 2.3 Statistical or Stochastic Specification of The Population Regression Function 2.4 The Nature of the Stochastic Error Term 2.5 The Sample Regression Function (SRF) 2.6 The Special Meaning of the Term Linear Regression 2.7 Two-Variable Versus Multiple Linear Regression 2.8 Estimation of Parameters: The Method of Ordinary Least Squares 2.9 Putting It All Together 2.10 Some Illustrative Examples 2.11 Summary Key Terms and Concepts Questions Problems Optional Questions Appendix 2A: Derivation of Least Squares Estimators Chapter 3. The Two-Variable Model: Hypothesis Testing 3.1 The Classical Linear Regression Model 3.2 Variances and Standard Errors of Ordinary Least Squares Estimators 3.3 Why OLS? Properties of OLS Estimators 3.4 The Sampling, or Probability, Distributions of OLS Estimators 3.5 Hypothesis Testing 3.6 Hypothesis Testing: Some Practical Aspects 3.7 How Good Is The Fitted Regression Line: The Coefficient of Determination, r2 3.8 Reporting the Results of Regression Analysis 3.9 Illustrative Examples 3.10 Comments on the Illustrative Examples 3.11 Forecasting 3.12 Normality Tests 3.13 Summary Key Terms and Concepts Questions Problems Chapter 4. Multiple Regression: Estimation and Hypothesis Testing 4.1 The Three-Variable Linear Regression Model 4.2 Assumptions of the Multiple Linear Regression Model 4.3 Estimation of the Parameters of Multiple Regression 4.4 Goodness of Fit of Estimated Multiple Regression: Multiple Coefficient of Determination, R2 4.5 Antique Clock Auction Prices Revisited 4.6 Hypothesis Testing In A Multiple Regression: General Comments 4.7 Testing Hypotheses About Individual Partial Regression Coefficients 4.8 Testing the Joint Hypothesis That B2 = B3 = 0 Or R2 = 0 4.9 Two-Variable Regression In the Context of Multiple Regression: Introduction to Specification Bias 4.10 Comparing Two R2 Values: The Adjusted R2 4.11 When to Add an Additional Explanatory Variable to a Model 4.12 Restricted Least Squares 4.13 Illustrative Examples 4.14 Summary Key Terms and Concepts Questions Problems Appendix 4A.1: Derivations of OLS Estimators Appendix 4A.2: Derivation of Equation (4.31) Appendix 4A.3: Derivation of Equation (4.49) Chapter 5. Functional Forms of Regression Models 5.1 How to Measure Elasticity: The Log-Linear Model 5.2 Multiple Log-Linear Regression Models 5.3 How to Measure the Growth Rate: The Semilog Model 5.4 The Lin-Log Model: When the Explanatory Variable Is Logarithmic 5.5 Reciprocal Models 5.6 Polynomial Regression Models 5.7 Regression Through the Origin: The Zero Intercept Model 5.8 A Note on Scaling and Units of Measurement 5.9 Regression on Standardized Variables 5.10 Summary of Functional Forms 5.11 SUMMARY Key Terms and Concepts Questions Problems Appendix 5A: Logarithms Chapter 6. Qualitative or Dummy Variable Regression Models 6.1 The Nature of Dummy Variables 6.2 ANCOVA Models: Regression on One Quantitative Variable and One Qualitative Variable With Two Categories 6.3 Regression on One Quantitative Variable and One Qualitative Variable With More Than Two Classes or Categories 6.4 Regression on One Quantiative Explanatory Variable and More Than One Qualitative Variable 6.5 Comparing Two Regessions 6.6 The Use of Dummy Variables In Seasonal Analysis 6.7 What Happens if the Dependent Variable Is Also a Dummy Variable? The Linear Probability Model (LPM) 6.8 The Logit Model 6.9 Summary Key Terms and Concepts Questions Problems PART II. REGRESSION ANALYSIS IN PRACTICE Chapter 7. Model Selection: Criteria and Tests 7.1 The Attributes of a Good Model 7.2 Types of Specification Errors 7.3 Omisson of Relevant Variable Bias: “Underfitting” a Model 7.4 Inclusion of Irrelevant Variables: “Overfitting” a Model 7.5 Incorrect Functional Form 7.6 Errors of Measurement 7.7 Detecting Specification Errors: Tests of Specification Errors 7.8 Outliers, Leverage, and Influence Data 7.9 Probabity Distribution of the Error Term 7.10 Model Evaluation Criteria 7.11 Nonnormal Distribution of the Error Term 7.12 Fixed Versus Random (or Stochastic) Explanatory Variables 7.13 Summary Key Terms and Concepts Questions Problems Chapter 8. Multicollinearity: What Happens if Explanatory Variables Are Correlated? 8.1 The Nature of Multicollinearity: The Case of Perfect Multicollinearity 8.2 The Case of Near, or Imperfect, Multicollinearity 8.3 Theoretical Consequences of Multicollinearity 8.4 Practical Consequences of Multicollinearity 8.5 Detection of Multicollinearity 8.6 Is Multicollinearity Necessarily Bad? 8.7 An Extended Example: The Demand for Chickens In The United States, 1960 To 1982 8.8 What to Do With Multicollinearity: Remedial Measures 8.9 Summary Key Terms and Concepts Questions Problems Chapter 9. Heteroscedasticity: What Happens if the Error Variance Is Nonconstant? 9.1 The Nature of Heteroscedasticity 9.2 Consequences of Heteroscedasticity 9.3 Detection of Heteroscedasticity: How Do We Know When There Is a Heteroscedasticity Problem? 9.4 What to Do if Heteroscedasticity Is Observed: Remedial Measures 9.5 White’s Heteroscedasticity-Corrected Standard Errors and t Statistics 9.6 Some Concrete Examples of Heteroscedasticity 9.7 Summary Key Terms and Concepts Questions Problems Chapter 10. Autocorrelation: What Happens If Error Terms Are Correlated? 10.1 The Nature of Autocorrelation 10.2 Consequences of Autocorrelation 10.3 Detecting Autocorrelation 10.4 Remedial Measures 10.5 How to Estimate p 10.6 A Large Sample Method of Correcting OLS Standard Errors: The Newey–West (NW) Method 10.7 A General Test of Autocorrelation: The Breusch–Godfrey (BG) Test 10.8 Summary Key Terms and Concepts Questions Problems PART III. ADVANCED TOPICS IN ECONOMETRICS Chapter 11. Elements of Time-Series Econometrics 11.1 The Phenomenon of Spurious Regression: Nonstationary Time Series 11.2 Tests of Stationarity 11.3 Cointegrated Time Series 11.4 The Random Walk Model 11.5 Causality In Economics: The Granger Causality Test 11.6 Summary Key Terms and Concepts Problems Chapter 12. Panel Data Regression Models 12.1 The Importance of Panel Data 12.2 An Illustrative Example: Charitable Giving 12.3 Pooled OLS Regression of the Charity Function 12.4 The Fixed-Effects Least Squares Dummy Variable (LSDV) Model 12.5 Limitations of the Fixed-Effects LSDV Model 12.6 The Fixed-Effects Within-Group (WG) Estimator 12.7 The Random-Effects Model (REM) or Error Components Model (ECM) 12.8 Properties of Various Estimators 12.9 Panel Data Regressions: Some Concluding Comments 12.10 Summary and Conclusions

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Institution
Essentials Of Econometrics, 5e
Course
Essentials of Econometrics, 5e

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Solutions Manual for Essentials of Econometrics, 5e by Damodar
Gujarati
PR
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1

, CHAPTER 1
THE NATURE AND SCOPE OF ECONOMETRICS



QUESTIONS
1.1. (a) Other things remaining the same, the higher the tax rate is, the lower
the price of a house will be.
(b) Assume that the data are cross-sectional, involving several residential
communities with differing tax rates.
(c) Yi = B1 + B2 X i
where Y = price of the house and X = tax rate
(d) Yi = B1 + B2 X i + ui
PR
(e) Given the sample, one can use OLS to estimate the parameters of the
model.
(f) Aside from the tax rate, other factors that affect house prices are
mortgage interest rates, house size, buyers’ family income, the state of the
O
economy, the local crime rate, etc. Such variables may be included in a more
detailed multiple regression model.
FD
(g) A priori, B2 < 0. Therefore, one can test H0 : B2  0 against H1 : B2 < 0.

(h) The estimated regression can be used to predict the average price of a
house in a community, given the tax rate in that community. Of course, it
O
is assumed that all other factors stay the same.
1.2. Econometricians are now routinely employed in government and business
C
to estimate and / or forecast (1) price and cost elasticities, (2) production
and cost functions, and (3) demand functions for goods and services, etc.
Econometric forecasting is a growth industry.
1.3. The economy will be bolstered if the increase in the money supply leads to
a reduction in the interest rate which will lead to more investment activity
and, therefore, to more output and more employment. If the increase in the
money supply, however, leads to inflation, the preceding result may




2

, not occur. The job of the econometrician will be to develop a model to
predict the effect of the increase in the money supply on inflation, interest
rate, employment, etc.
1.4. As a matter of fact, on October 1, 1993 the Federal Government did increase
the gasoline tax by 4 cents. Since gasoline and cars are complementary
products, economic theory suggests that an increase in the price of gasoline
will not only lead to a decline in the demand for gasoline but also in the
demand for cars, ceteris paribus. The Ford Motor Companymay be advised
to produce more fuel-efficient cars to stave off a serious decline in the
demand for its cars. An automobile demand function will provide numerical
estimates of the effect of gasoline tax on the demandfor automobiles.
PR
1.5.
There are many alternative designs possible. However, to keep things
simple, and discuss just a basic idea of the design, we could think of using
an econometric model known as Autoregressive Distributed Lag (ARDL)
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of the form:
y =  + y +  g +  g +   p + +  p +   c +   c + u
FD
t t −1 0 t 1 t −1 0 t 1 t −1 0 t 1 t −1 t


where
Yt − Yt −1
yt = = real GDP growth rate in year t;
Yt −1
O
Gt − Gt −1
gt = = real government infrastructure investment growth rate in
Gt −1
C
year t;
 tp = personal income tax rate in year t;

tc = corporate income tax rate in year t;
yt = yt − yt −1 = change in real GDP growth rate in year t;
gt = gt − gt −1 = change in real government infrastructure investment growth
rate in year t;
 p =  p −  p = change in personal income tax rate in year t;
t t t −1
 c =  c −  c = change in corporate income tax rate in year t.
t t t −1



Expected signs and magnitudes of the parameters the regression model:

3

, 0    1;
0,1  0;
 0,1,0,1  0.

Note that a more realistic model would include a larger number of lags of the
regressors than included in our model, and would also include additional
regressors known as control variables which could be determined based on
economic theory.

Now, based on our specified model above, short run and long run economic
consequence of 1 unit increase in g ,  p , and  c , respectively, can be
t t t

determined by using partial derivatives as follows.

Short run economic consequences of a 1 unit increase in gt are given by:
yt
PR
= 0 ;
gt
yt +1 (yt + 1gt )

= =  0 + 1;
gt gt
yt + 2 (yt +1 )
= =  ( 0 + 1 );
O





gt gt
yt +3 (yt + 2 ) (yt + 2 )
=  
= =  2 ( 0 + 1 );
gt gt gt
FD
and so on.

Long run consequence is the sum of short run consequences
= 0 + ( 0 +  1) +  ( 0 +  1) +  2 ( 0 +  1) + ...
= 0 +  0 +  1+  2 0 +  1+  3 0 +  2 1 + ...
O

= 0(1 +  +  2 +  3 + ...) +  1(1 +  +  2 + ...)
0 
= + 1
C
1−  1− 
 +
= 0 1.
1− 

Analogously, short run economic consequences of a 1 unit increase in  tp are
given by:
yt
=  0;
 tp
y (y +   p )
t +1
= t 1 t
=  0 + 1;
 t p
 tp




4

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