Using Excel For Principles of Econometrics, Fourth Edition

Transcription

1 Using Excel For Principles of Econometrics, Fourth Edition GENEVIEVE BRIAND Washington State University R. CARTER HILL Louisiana State University JOHN WILEY & SONS, INC New York / Chichester / Weinheim I Brisbane / Singapore / Toronto

2 CONTENTS CHAPTER 1 Introduction to Excel Starting Excel Entering Data Using Excel for Calculations Arithmetic Operations Mathematical Functions Editing your Data Saving and Printing your Data Importing Data into Excel Resources for Economists on the Internet Data Files for Principles of Econometrics a John Wiley & Sons Website b Principles of Econometrics Website Importing ASCII Files 14 CHAPTER 2 The Simple Linear Regression Model Plotting the Food Expenditure Data Using Chart Tools Editing the Graph a Editing the Vertical Axis b Axis Titles c Gridlines and Markers d Moving the Chart Estimating a Simple Regression Using Least Squares Estimators' Formulas Using Excel Regression Analysis Routine Plotting a Simple Regression Using Two Points Using Excel Built-in Feature Using a Regression Option Editing the Chart Expected Values of b\ and b Model Assumptions Random Number Generation The LINEST Function Repeated Sampling Variance and Covariance of b x and b Nonlinear Relationships A Quadratic Model a Estimating the Model b Scatter Plot of Data with Fitted Quadratic Relationship A Log-Linear Model a Histograms of PRICE and \n(price) b Estimating the Model c Scatter Plot of Data with Fitted Log- Linear Relationship Regression with Indicator Variables Histograms of House Prices Estimating the Model 65 CHAPTER 3 Interval Estimation and Hypothesis Testing Interval Estimation The/-Distribution a The/-Distribution versus Normal Distribution b /-Critical Values and Interval Estimates c Percentile Values Id TINV Function 69 3.Lie Appendix E: Table 2 in POE Obtaining Interval Estimates An Illustration 71 V11I

3 3.1.3a Using the Interval Estimator Formula b Excel Regression Default Output c Excel Regression Confidence Level Option The Repeated Sampling Context (Advanced Material) a Model Assumptions b Repeated Random Sampling c The LINEST Function Revisited d The Simulation Template e The IF Function f The OR Function g TheCOUNTIF Function 80 Hypothesis Tests One-Tail Tests with Alternative "Greater Than" (>) One-Tail Tests with Alternative "Less Than" (<) Two-Tail Tests with Alternative "Not Equal To" &) 82 Examples of Hypothesis Tests Right-Tail Tests a One-Tail Test of Significance b One-Tail Test of an Economic Hypothesis Left-Tail Tests Two-Tail Tests a Two-Tail Test of an Economic Hypothesis b Two-Tail Test of Significance 87 The p- Value Thep-Value Rule a Definition of p-value b Justification for the p- Value Rule The TDIST Function Examples of Hypothesis Tests Revisited a Right-Tail Test from Section 3.3.1b b Left-Tail Test from Section c Two-Tail Test from Section 3.3.3a d Two-Tail Test from Section 3.3.3b 93 CHAPTER 4 Prediction, Goodness-of-Fit and Modeling Issues Least Squares Prediction Measuring Goodness-of-Fit Coefficient of Determination or/? Correlation Analysis and Ä The Food Expenditure Example and the CORREL Function The Effects of Scaling the Data Changing the Scale of x Changing the Scale of y Changing the Scale of x andy A Linear-Log Food Expenditure Model Estimating the Model Scatter Plot of Data with Fitted Linear-Log Relationship Using Diagnostic Residual Plots Random Residual Pattern Heteroskedastic Residual Pattern Detecting Model Specification Errors Are the Regression Errors Normally Distributed? 115

4 4.6.1 Histogram of the Residuals The Jarque-Bera Test for Normality using the CHINV and CHIDIST Functions The Jarque-Bera Test for Normality for the Linear-Log Food Expenditure Model Polynomial Models: An Empirical Example Scatter Plot of Wheat Yield over Time The Linear Equation Model a Estimating the Model b Residuals Plot The Cubic Equation Model a Estimating the Model b Residuals Plot Log-Linear Models A Growth Model A Wage Equation Prediction A Generalized R 2 Measure Prediction Intervals A Log-Log Model: Poultry Demand Equation Estimating the Model A Generalized R 2 Measure Scatter Plot of Data with Fitted Log-Log Relationship 140 CHAPTER 5 The Multiple Linear Regression Least Squares Estimates Using the Hamburger Chain Data Interval Estimation Hypothesis Tests for a Single Coefficient Tests of Significance One-Tail Tests a Left-Tail Test of Elastic Demand b Right-Tail Test of Advertising Effectiveness 147 Polynomial Equations: Extending the Model for Burger Barn Sales 148 Interaction Variables Linear Models Log-Linear Models 151 Measuring Goodness-of-Fit 153 CHAPTER 6 Further Inference in the Multiple Regression Model Testing the Effect of Advertising: the F- test The Logic of the Test The Unrestricted and Restricted Models Test Template Testing the Significance of the Model Null and Alternative Hypotheses Test Template Excel Regression Output The Relationship between t- and F-Tests Testing Some Economic Hypotheses The Optimal Level of Advertising The Optimal Level of Advertising and Price The Use of Nonsample Information Model Specification Omitted Variables Irrelevant Variables The RESET Test Poor Data, Collinearity and Insignificance Correlation Matrix The Car Mileage Model Example 177 x

5 CHAPTER 7 Using Indicator Variables Indicator Variables: The University Effect on House Prices Example Applying Indicator Variables Interactions Between Qualitative Factors Qualitative Factors with Several Categories Testing the Equivalence of Two Regressions Log-Linear Models: a Wage Equation Example The Linear Probability Model: A Marketing Example The Difference Estimator: The Project STAR Example The Differences-in-Differences Estimator: The Effect of Minimum Wage Change Example 198 CHAPTER 8 Heteroskedasticity The Nature of Heteroskedasticity Detecting Heteroskedasticity Residual Plots Lagrange Multiplier Tests a Using the Lagrange Multiplier or Breusch- Pagan Test b Using the White Test The Goldfeld-Quandt Test a The Logic of the Test b Test Template c Wage Equation Example d Food Expenditure Example Heteroskedasticity-Consistent Standard Errors or the White Standard Errors Generalized Least Squares: Known Form of Variance Variance Proportional to x: Food Expenditure Example Grouped Data: Wage Equation Example a Separate Wage Equations for Metropolitan and Rural Areas b GLS Wage Equation 223 Generalized Least Squares: Unknown Form of Variance 224 CHAPTER 9 Regressions with Time Series Data: Stationary Variables Finite Distributed Lags US Economic Time Series An Example: The Okun's Law Serial Correlation Serial Correlation in Ouput Growth a Scatter Diagram for G, and C, b Correlogram for G Serially Correlated Errors a Australian Economic Time Series b A Phillips Curve c Correlogram for Residuals Lagrange Multiplier Tests for Serially Correlated Errrors /-Test Version Tx R 2 Version Estimation with Serially Correlated Errors Generalized Least Squares Estimation of an AR(1) Error Model a The Prais-Winsten Estimator b The Cochrane-Orcutt Estimator Autoregressive Distributed Lag (ARDL) Model 252 xi

6 9.5 Forecasting Using an Autoregressive (AR) Model Using an Exponential Smoothing Model Multiplier Analysis 258 CHAPTER 10 Random Regressors and Moment-Based Estimation OLS Estimation of a Wage Equation Instrumental Variables Estimation of the Wage Equation With a Single Instrument a First Stage Equation for EDUC b Stage 2 Least Squares Estimates With a Surplus Instrument a First Stage Equation for EDUC b Stage 2 Least Squares Estimates Specification Tests for the Wage Equation The Hausman Test Testing Surplus Moment Conditions 274 CHAPTER 11 Simultaneous Equations Models Supply and Demand Model for Truffles The Reduced Form Equations a Reduced Form Equation for Q b Reduced Form Equation for P The Structural Equations or Stage 2 Least Squares Estimates a 2SLS Estimates for Truffle Demand b 2SLS Estimates for Truffle Supply Supply and Demand Model for the Fulton Fish Market The Reduced Form Equations a Reduced Form Equation for InQ b Reduced Form Equation for InP The Structural Equations or Stage 2 Least Squares Estimates a 2SLS Estimates for Fulton Fish Demand 290 CHAPTER 12 Nonstationary Time-Series Data and Cointegration Stationary and Nonstationary Variables US Economic Time Series Simulated Data Spurious Regressions Unit Root Tests for Stationarity Cointegration 306 CHAPTER 13 Vector Error Correction and Vector Autoregressive Models Estimating a VEC Model Test for Cointegration The VEC Model Estimating a VAR Model Test for Cointegration The VAR Model Impulse Responses Functions The Univariate Case The Bivariate Case 325 xii

7 CHAPTER 14 Time-Varying Volatility and ARCH Models Time-Varying Volatility Returns Data Simulated Data Testing and Forecasting Testing for ARCH Effects a Time Series and Histogram b Lagrange Multiplier Test Forecasting Volatility Extensions The GARCH Model The T-GARCH Model The GARCH-In-Mean Model 352 CHAPTER 15 Panel Data Models Pooled Least Squares Estimates of Wage Equation The Fixed Effects Model Estimates of Wage Equation for Small N a The Least Squares Dummy Variable Estimator for Small N b The Fixed Effects Estimator: Estimates of Wage Equation forw = Fixed Effects Estimates of Wage Equation from Complete Panel The Random Effects Model Testing for Random Effects Random Effects Estimation of the Wage Equation Sets of Regression Equations Estimation: Equal Coefficients, Equal Error Variances Estimation: Different Coefficients, Equal Error Variances Estimation: Different Coefficients, Different Error Variances Seemingly Unrelated Regressions: Testing for Contemporaneous Correlation 388 CHAPTER 16 Qualitative and Limited Dependent Variable Models Least Squares Fitted Linear Probability Model Limited Dependent Variables Censored Data Simulated Data 395 APPENDIX A Mathematical Tools 402 A.l Mathematical Operations 402 A. 1.1 Exponents 408 A. 1.2 Scientific Notation 409 A. 1.3 Logarithm and the Number e 410 A.2 Percentages 413 APPENDIX B Review of Probability Concepts 416 B.l Binomial Probabilities 416 B. 1.1 Computing Binomial Probabilities Directly 417 B. 1.2 Computing Binomial Probabilities Using BINOMDIST 419 B.2 The Normal Distributions 422 B.2.1 The STANDARDIZE Function 422 B.2.2 TheNORMSDIST Function 423 B.2.3 TheNORMSINV Function 423 B.2.4 TheNORMDIST Function 424 B.2.5 TheNORMINV Function 424 B.2.6 A Template for Normal Distribution Probability Calculations 424 xiii

8 B.3 Distributions Related to the Normal 426 B.3.1 The Chi-Square Distribution 426 B.3.2 The /-Distribution 428 B.3.3 The F-Distribution 429 APPENDIX C Review of Statistical Inference 431 C. 1 Examining a Sample of Data 431 C.2 Estimating Population Parameters 436 C.2.1 Creating Random Samples 436 C.2.2 Estimating a Population Mean 438 C.2.3 Estimating a Population C.2.4 Variance 438 Standard Error of the Sample Mean 439 C.3 The Central Limit Theorem 439 C.4 Interval Estimation 444 C.4.1 Interval Estimation with a 2 unkown 446 C.4.2 Interval Estimation with the Hip Data 447 C.5 Hypothesis Tests About a Population Mean 449 C.5.1 An Example 450 C.5.2 The/?-value 450 C.5.3 A Template for Hypothesis Tests 451 C.6 Other Useful Tests 454 C.6.1 Simulating Data 454 C.6.2 Testing a Population Variance 456 C.6.3 Testing Two Population Means 459 C.6.4 Testing Two Population Variances 461 C.7 Testing Population Normality 463 C.7.1 A Histogram 463 C.7.2 The Jacque-Bera Test 465 Index 467 xiv