San Jose State University Ergonometric Analysis Research Paper Please refer to the proposal and textbook material that I attached to this post. Try to use

San Jose State University Ergonometric Analysis Research Paper Please refer to the proposal and textbook material that I attached to this post. Try to use my idea for this research paper. If you feel like there is something need to be adjusted, please feel free to do so.

Please make sure to follow the structure!

Paper Structure:

I. Title page.

II. Abstract. This should be less than 50 words and summarize the topic, methodology, and main findings. It should appear on your title page.

III. Introduction. This section should state the nature and objectives of the project along with a brief review of any relevant literature. Make sure to provide some background or motivation for why your project is interesting.

IV. Description of the model. The model should be clearly stated and any equations carefully explained. You should write out the econometric model you plan to estimate, and discuss the expected impact of the exogenous variables in your model.

V. Data description and model estimation. You should use the techniques developed in class to analyze your data and estimate your model. Make sure to describe the dataset you are using by providing summary statistics of important variables. Your results should be reported and discussed in this section and could include: parameter estimates, standard errors, t-statistics, F-statistics, R-squared, tests for autocorrelation, heteroskedasticity, and possible multicollinearity, as appropriate.VI. Conclusion. Review the major findings as well as possible extensions for future work. Make sure to mention any limitations of your approach as well as alternative explanations of your results. Policy implications, if any, could also be included in this section.

VII. Tables and graphs. Your paper must include at least one table and one graph. The tables and graphs should be well-labeled and accessible to the reader—do not merely print out your regression output with cryptic variable names. Appendix If you have a lot of regression results or other details in your theoretical/statistical model that merits to be included yet, they may distract the reader, you may include them in an appendix.

Proposal for Term Research Paper:

The Impact of Personal Disposable Income on Personal Consumption Expenditure

In general, people pay close attention to how much money is able to spend on goods and services in order to satisfy their needs. Personal Consumption Expenditures(PCE) is a measure of the consumption of goods and services within a nation. It is an important component of the Gross Domestic Product(GDP). Personal Disposable Income(PDI) is the key determinant factor in predicting the change in PCE.

People tend to consume more while there is an increase in their income level, but this is not always the case. The effect of government raising the personal income tax rate will have some impact on consumer spending. An increased personal income tax rate causes the net personal income to decrease. The interest rate that banks offered on savings is also a factor influencing the willingness to consume as well as the inflation rate, the interest rate of the mortgage and the rate that bank charges on credit cards. I am not certain about whether to include real GDP growth rate as a factor or not. There is more research to be done in order to determine.

In my research paper, I will try to discover the relationship between personal consumption expenditure and personal disposable income. Most importantly, observe the significance of each factor on the personal consumption expenditure. These data are usually available on government websites within different departments. Proposal for Term Research Paper: The Impact of Personal Disposable Income on
Personal Consumption Expenditure
In general, people pay close attention to how much money is able to spend on goods
and services in order to satisfy their needs. Personal Consumption Expenditures(PCE) is a
measure of the consumption of goods and services within a nation. It is an important component
of the Gross Domestic Product(GDP). Personal Disposable Income(PDI) is the key determinant
factor in predicting the change in PCE.
People tend to consume more while there is an increase in their income level, but this
is not always the case. The effect of government raising the personal income tax rate will have
some impact on consumer spending. An increased personal income tax rate causes the net
personal income to decrease. The interest rate that banks offered on savings is also a factor
influencing the willingness to consume as well as the inflation rate, the interest rate of the
mortgage and the rate that bank charges on credit cards. I am not certain about whether to
include real GDP growth rate as a factor or not. There is more research to be done in order to
determine.
In my research paper, I will try to discover the relationship between personal
consumption expenditure and personal disposable income. Most importantly, observe the
significance of each factor on the personal consumption expenditure. These data are usually
available on government websites within different departments.
Introductory
Econometrics
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Introductory
Econometrics
A Modern Approach
Fifth Edition
Jeffrey M. Wooldridge
Michigan State University
Australia • Brazil • Japan • Korea • Mexico • Singapore • Spain • United Kingdom • United States
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Introductory Econometrics: A Modern
­Approach, Fifth Edition
Jeffrey M. Wooldridge
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Cover Image: © Elena R/Shutterstock.com;
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Printed in the United States of America
1 2 3 4 5 6 7 16 15 14 13 12
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Brief Contents
Chapter 1
The Nature of Econometrics and Economic Data
PART 1: Regression Analysis with Cross-Sectional Data
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
The Simple Regression Model
Multiple Regression Analysis: Estimation
Multiple Regression Analysis: Inference
Multiple Regression Analysis: OLS Asymptotics
Multiple Regression Analysis: Further Issues
Multiple Regression Analysis with Qualitative
Information: Binary (or Dummy) Variables
Heteroskedasticity
More on Specification and Data Issues
1
21
22
68
118
168
186
227
268
303
PART 2: Regression Analysis with Time Series Data
343
Chapter 10
Chapter 11
Chapter 12
344
380
412
Basic Regression Analysis with Time Series Data
Further Issues in Using OLS with Time Series Data
Serial Correlation and Heteroskedasticity in Time Series Regressions
PART 3: Advanced Topics
447
Chapter 13
Chapter 14
Chapter 15
Chapter 16
Chapter 17
Chapter 18
Chapter 19
448
484
512
554
583
632
676
Pooling Cross Sections Across Time: Simple Panel Data Methods
Advanced Panel Data Methods
Instrumental Variables Estimation and Two Stage Least Squares
Simultaneous Equations Models
Limited Dependent Variable Models and Sample Selection Corrections
Advanced Time Series Topics
Carrying Out an Empirical Project
Appendices
Appendix A
Appendix B
Appendix C
Appendix D
Appendix E
Appendix F
Appendix G
References
Glossary
Index
Basic Mathematical Tools
Fundamentals of Probability
Fundamentals of Mathematical Statistics
Summary of Matrix Algebra
The Linear Regression Model in Matrix Form
Answers to Chapter Questions
Statistical Tables
703
722
755
796
807
821
831
838
844
862
v
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deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
Contents
Preface xv
About the Author
xxv
The Nature of
Econometrics and Economic
Data 1
Chapter 1
1.1 What Is Econometrics? 1
1.2 Steps in Empirical Economic Analysis 2
1.3 The Structure of Economic Data 5
Cross-Sectional Data 5
Time Series Data 8
Pooled Cross Sections 9
Panel or Longitudinal Data 10
A Comment on Data Structures 11
1.4 Causality and the Notion of Ceteris Paribus
in Econometric Analysis 12
Summary 16
Computer Exercises 17
PART 1
Regression Analysis with
Cross-Sectional Data 21
Model
22
2.4 Units of Measurement and Functional Form 39
The Effects of Changing Units of Measurement on
OLS Statistics 40
Incorporating Nonlinearities in Simple Regression 41
The Meaning of “Linear” Regression 44
2.5 Expected Values and Variances of the OLS
Estimators 45
Unbiasedness of OLS 45
Variances of the OLS Estimators 50
Estimating the Error Variance 54
2.6 Regression through the Origin and Regression
on a Constant 57
Summary
Key Terms
58
59
Computer Exercises 63
17
Chapter 2
35
Problems 60
Key Terms 17
Problems
2.3 Properties of OLS on Any Sample of Data
Fitted Values and Residuals 35
Algebraic Properties of OLS Statistics 36
Goodness-of-Fit 38
The Simple Regression
2.1 Definition of the Simple Regression
Model 22
2.2 Deriving the Ordinary Least Squares
Estimates 27
A Note on Terminology 34
Appendix 2A 66
Chapter 3 Multiple Regression
Analysis: Estimation 68
3.1 Motivation for Multiple Regression 69
The Model with Two Independent Variables 69
The Model with k Independent Variables 71
3.2 Mechanics and Interpretation of Ordinary
Least Squares 72
Obtaining the OLS Estimates 72
Interpreting the OLS Regression Equation 74
On the Meaning of “Holding Other Factors
Fixed” in Multiple Regression 76
Changing More Than One Independent Variable
­Simultaneously 77
vi
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deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
vii
Contents
OLS Fitted Values and Residuals 77
A “Partialling Out” Interpretation of Multiple
Regression 78
Comparison of Simple and Multiple Regression
Estimates 78
Goodness-of-Fit 80
Regression through the Origin 81
3.3 The Expected Value of the OLS Estimators 83
Including Irrelevant Variables in a Regression
Model 88
Omitted Variable Bias: The Simple Case 88
Omitted Variable Bias: More General Cases 91
3.4 The Variance of the OLS Estimators 93
The Components of the OLS Variances:
Multicollinearity 94
Variances in Misspecified Models 98
Estimating s 2: Standard Errors of the OLS
Estimators 99
3.5 Efficiency of OLS: The Gauss-Markov
Theorem 101
3.6 Some Comments on the Language of Multiple
Regression Analysis 103
4.5 Testing Multiple Linear Restrictions:
The F Test 143
Testing Exclusion Restrictions 143
Relationship between F and t Statistics 149
The R-Squared Form of the F Statistic 150
Computing p-Values for F Tests 151
The F Statistic for Overall Significance of a
Regression 152
Testing General Linear Restrictions 153
4.6 Reporting Regression Results 154
Summary
Key Terms
157
159
Problems 159
Computer Exercises 164
Multiple Regression
­Analysis: OLS Asymptotics 168
chapter 5
5.1 Consistency 169
Deriving the Inconsistency in OLS
172
Key Terms 105
5.2 Asymptotic Normality and Large Sample
Inference 173
Other Large Sample Tests: The Lagrange
­Multiplier Statistic 178
Problems
5.3 Asymptotic Efficiency of OLS
Summary
104
106
Computer Exercises 110
Summary
Appendix 3A 113
Key Terms
181
182
183
Problems 183
Chapter 4 Multiple Regression
Analysis: Inference 118
Computer Exercises 183
4.1 Sampling Distributions of the OLS
Estimators 118
chapter 6
4.2 Testing Hypotheses about a Single Population
Parameter: The t Test 121
Testing against One-Sided Alternatives 123
Two-Sided Alternatives 128
Testing Other Hypotheses about bj 130
Computing p-Values for t Tests 133
A Reminder on the Language of Classical
­Hypothesis Testing 135
Economic, or Practical, versus Statistical
Significance 135
4.3 Confidence Intervals 138
4.4 Testing Hypotheses about a Single Linear
Combination of the Parameters 140
Appendix 5A 185
Multiple Regression
­Analysis: Further Issues 186
6.1 Effects of Data Scaling on OLS Statistics
Beta Coefficients 189
186
6.2 More on Functional Form 191
More on Using Logarithmic Functional
Forms 191
Models with Quadratics 194
Models with Interaction Terms 198
6.3 More on Goodness-of-Fit and Selection
of Regressors 200
Adjusted R-Squared 202
Using Adjusted R-Squared to Choose between
Nonnested Models 203
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deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
viii
Contents
Controlling for Too Many Factors in Regression
Analysis 205
Adding Regressors to Reduce the Error
Variance 206
6.4 Prediction and Residual Analysis 207
Confidence Intervals for Predictions 207
Residual Analysis 211
Predicting y When log(y) Is the Dependent
Variable 212
Summary 216
Key Terms
Problems
217
218
Computer Exercises 220
Appendix 6A
225
Multiple Regression
Analysis with Qualitative
Information: Binary (or Dummy)
Variables 227
chapter 7
chapter 8
8.1 Consequences of Heteroskedasticity for
OLS 268
8.2 Heteroskedasticity-Robust Inference after OLS
Estimation 269
Computing Heteroskedasticity-Robust LM
Tests 274
8.3 Testing for Heteroskedasticity 275
The White Test for Heteroskedasticity 279
8.4 Weighted Least Squares Estimation 280
The Heteroskedasticity Is Known up to a
­Multiplicative Constant 281
The Heteroskedasticity Function Must Be
­Estimated: Feasible GLS 286
What If the Assumed Heteroskedasticity Function
Is Wrong? 290
Prediction and Prediction Intervals with
Heteroskedasticity 292
8.5 The Linear Probability Model Revisited 294
Summary
296
7.1 Describing Qualitative Information 227
Key Terms
7.2 A Single Dummy Independent
Variable 228
Interpreting Coefficients on Dummy ­
Explanatory Variables When the Dependent
­Variable Is log(y) 233
Problems 297
7.3 Using Dummy Variables for Multiple
Categories 235
Incorporating Ordinal Information by Using
Dummy Variables 237
7.4 Interactions Involving Dummy Variables 240
Interactions among Dummy Variables 240
Allowing for Different Slopes 241
Testing for Differences in Regression Functions
across Groups 245
7.5 A Binary Dependent Variable: The Linear
Probability Model 248
7.6 More on Policy Analysis and Program
Evaluation 253
7.7 Interpreting Regression Results with Discrete
Dependent Variables 256
Summary 257
Key Terms 258
Problems
258
Computer Exercises 262
Heteroskedasticity 268
297
Computer Exercises 299
More on Specification
and Data Issues 303
chapter 9
9.1 Functional Form Misspecification 304
RESET as a General Test for Functional Form
Misspecification 306
Tests against Nonnested Alternatives 307
9.2 Using Proxy Variables for Unobserved
­Explanatory Variables 308
Using Lagged Dependent Variables as Proxy
Variables 313
A Different Slant on Multiple Regression 314
9.3 Models with Random Slopes 315
9.4 Properties of OLS under Measurement
Error 317
Measurement Error in the Dependent
Variable 318
Measurement Error in an Explanatory
Variable 320
9.5 Missing Data, Nonrandom Samples, and
­Outlying Observations 324
Copyright 2012 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has
deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it.
ix
Contents
Missing Data 324
Nonrandom Samples 324
Outliers and Influential Observations 326
9.6 Least Absolute Deviations Estimation 331
Summary
334
Key Terms 335
Problems
335
Computer Exercises 338
PART 2
Regression Analysis with Time
Series Data 343
Basic Regression Analysis
with Time Series Data 344
chapter 10
Further Issues in Using
OLS with Time Series Data 380
chapter 11
11.1 Stationary and Weakly Dependent Time
Series 381
Stationary and Nonstationary Time Series 381
Weakly Dependent Time Series 382
11.2 Asymptotic Properties of OLS
11.4 Dynamically Complete Models and the
­Absence of Serial Correlation 399
11.5 The Homoskedasticity Assumption for
Time Series Models 402
10.1 The Nature of Time Series Data 344
Summary
10.2 Examples of Time Series Regression
Models 345
Static Models 346
Finite Distributed Lag Models 346
A Convention about the Time Index 349
Key Terms
10.3 Finite Sample Properties of OLS under
­Classical Assumptions 349
Unbiasedness of OLS 349
The Variances of the OLS Estimators and the
Gauss-Markov Theorem 352
Inference under the Classical Linear Model
Assumptions 355
10.4 Functional Form, Dummy Variables, and Index
Numbers 356
10.5 Trends and Seasonality 363
Characterizing Trending Time Series 363
Using Trending Variables in Regression
Analysis 366
A Detrending Interpretation of Regressions with
a Time Trend 368
Computing R-Squared when the Dependent
­Variable Is Trending 370
Seasonality 371
Summary
373
Key Terms 374
Problems
375
Computer Exercises 377
384
11.3 Using Highly Persistent Time Series in
­Regression Analysis 391
Highly Persistent Time Series 391
Transformations on Highly Persistent Time
Series 395
Deciding Whether a Time Series Is I(1) 396
402
404
Problems 404
Computer Exercises 407
chapter 12 Serial Correlation and
Heteroskedasticity in Time Series
Regressions 412
12.1 Properties of OLS with Serially Correlated
Errors 412
Unbiasedness and Consistency 412
Efficiency and Inference 413
Goodness-of-Fit 414
Serial Correlation in the Presence of Lagged
­Dependent Variables 415
12.2 Testing for Serial Correlation 416
A t Test for AR(1) Serial Correlation with Strictly
Exogenous Regressors 416
The Durbin-Watson Test under Classical
Assumptions 418
Testing for AR(1) Serial Correlation without
Strictly Exogenous Regressors 420
Testing for Higher Order Serial Correlation 421
12.3 Correcting for Serial Correlation with Strictly
Exogenous Regressors 423
Obtaining the Best Linear Unbiased Estimator…
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