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Cross section and experimental data analysis using EViews / / I Gusti Ngurah Agung
| Cross section and experimental data analysis using EViews / / I Gusti Ngurah Agung |
| Autore | Agung I Gusti Ngurah |
| Pubbl/distr/stampa | Singapore, : John Wiley & Sons, 2011 |
| Descrizione fisica | 1 online resource (586 p.) |
| Disciplina | 005.5/5 |
| Soggetto topico | Statistics |
| ISBN |
0-470-82845-5
1-283-37249-5 9786613372499 0-470-82843-9 0-470-82844-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
CROSS SECTION AND EXPERIMENTAL DATA ANALYSIS USING EVIEWS; Contents; Preface; 1 Misinterpretation of Selected Theoretical Concepts of Statistics; 1.1 Introduction; 1.2 What is a Population?; 1.3 A Sample and Sample Space; 1.3.1 What is a Sample?; 1.3.2 What is the Sample Space?; 1.3.3 What is a Representative Sample?; 1.3.4 Relationship between the Sample Space, Population, and a Sample; 1.4 Distribution of a Random Sample Space; 1.5 What is a Random Variable?; 1.6 Theoretical Concept of a Random Sample; 1.6.1 What is a Random Sample in Statistics?; 1.6.2 Central Limit Theorem
1.6.3 Unbiased Statistics based on Random Samples 1.6.4 Special Notes on Nonrandom Sample; 1.7 Does a Representative Sample Really Exist?; 1.8 Remarks on Statistical Powers and Sample Sizes; 1.9 Hypothesis and Hypothesis Testing; 1.10 Groups of Research Variables; 1.10.1 Problem Indicators; 1.10.2 Controllable Cause Factors; 1.10.3 Uncontrollable Cause Factors; 1.10.4 Background or Classification Factors; 1.10.5 Environmental Factors; 1.11 Causal Relationship between Variables; 1.11.1 Bivariate Correlation; 1.11.2 Special Remarks; 1.12 Misinterpretation of Selected Statistics 1.12.1 Standard Error 1.12.2 Significance Level and Power of a Test; 1.12.3 Reliability of a Test or Instrument; 1.12.4 Validity of a Test or Instrument; 1.12.5 Reliability and Validity of Forecasting; 1.12.6 Reliability and Validity of a Predicted Risk; 2 Simple Statistical Analysis but Good for Strategic Decision Making; 2.1 Introduction; 2.2 A Single Input for Decision Making; 2.2.1 A Single Sampled Unit; 2.2.2 Descriptive Statistics Based on a Single Measurable Variable; 2.2.3 Agung Six-Point Scale (ASPS) Problem Indicator; 2.2.4 Latent Variables and Composite Indexes 2.2.5 Demographic and Social-Economic Factors 2.2.6 Garbage as a Data Source; 2.2.7 Boxplot as an Input for Decision Making; 2.2.8 A Series of Inputs for Strategic Decision Making; 2.3 Data Transformation; 2.3.1 To Generate Categorical Variables; 2.3.2 To Generate Dummy Variables; 2.4 Biserial Correlation Analysis; 2.5 One-Way Tabulation of a Variable; 2.6 Two-Way Tabulations; 2.6.1 Measure of Associations for Bivariate Categorical Variables; 2.6.2 Other Measures of Association Based on a 2 X 2 Table; 2.6.3 Measures of Association Based on a I X 2 Table; 2.7 Three-Way Tabulation 2.7.1 Conditional Measures of Association for a 2 X 2 X 2 Table 2.7.2 Conditional Odds Ratio for an I X J X 2 Table; 2.8 Special Notes and Comments; 2.9 Special Cases of the N-Way Incomplete Tables; 2.10 Partial Associations; 2.11 Multiple Causal Associations Based on Categorical Variables; 2.11.1 Theoretical and Empirical Concepts of Causal Associations; 2.11.2 Multidimensional Frequency Table; 2.12 Seemingly Causal Model Based on Categorical Variables; 2.12.1 Causal Association Based on (X1, X2, Y1) or (X1, Y1, Y2); 2.12.2 Causal Association Based on (X1, X2, Y1, Y2) 2.12.3 Causal Association Based on Multidimensional Variables |
| Record Nr. | UNINA-9910133595903321 |
Agung I Gusti Ngurah
|
||
| Singapore, : John Wiley & Sons, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Cross section and experimental data analysis using EViews / / I Gusti Ngurah Agung
| Cross section and experimental data analysis using EViews / / I Gusti Ngurah Agung |
| Autore | Agung I Gusti Ngurah |
| Pubbl/distr/stampa | Singapore, : John Wiley & Sons, 2011 |
| Descrizione fisica | 1 online resource (586 p.) |
| Disciplina | 005.5/5 |
| Soggetto topico | Statistics |
| ISBN |
0-470-82845-5
1-283-37249-5 9786613372499 0-470-82843-9 0-470-82844-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
CROSS SECTION AND EXPERIMENTAL DATA ANALYSIS USING EVIEWS; Contents; Preface; 1 Misinterpretation of Selected Theoretical Concepts of Statistics; 1.1 Introduction; 1.2 What is a Population?; 1.3 A Sample and Sample Space; 1.3.1 What is a Sample?; 1.3.2 What is the Sample Space?; 1.3.3 What is a Representative Sample?; 1.3.4 Relationship between the Sample Space, Population, and a Sample; 1.4 Distribution of a Random Sample Space; 1.5 What is a Random Variable?; 1.6 Theoretical Concept of a Random Sample; 1.6.1 What is a Random Sample in Statistics?; 1.6.2 Central Limit Theorem
1.6.3 Unbiased Statistics based on Random Samples 1.6.4 Special Notes on Nonrandom Sample; 1.7 Does a Representative Sample Really Exist?; 1.8 Remarks on Statistical Powers and Sample Sizes; 1.9 Hypothesis and Hypothesis Testing; 1.10 Groups of Research Variables; 1.10.1 Problem Indicators; 1.10.2 Controllable Cause Factors; 1.10.3 Uncontrollable Cause Factors; 1.10.4 Background or Classification Factors; 1.10.5 Environmental Factors; 1.11 Causal Relationship between Variables; 1.11.1 Bivariate Correlation; 1.11.2 Special Remarks; 1.12 Misinterpretation of Selected Statistics 1.12.1 Standard Error 1.12.2 Significance Level and Power of a Test; 1.12.3 Reliability of a Test or Instrument; 1.12.4 Validity of a Test or Instrument; 1.12.5 Reliability and Validity of Forecasting; 1.12.6 Reliability and Validity of a Predicted Risk; 2 Simple Statistical Analysis but Good for Strategic Decision Making; 2.1 Introduction; 2.2 A Single Input for Decision Making; 2.2.1 A Single Sampled Unit; 2.2.2 Descriptive Statistics Based on a Single Measurable Variable; 2.2.3 Agung Six-Point Scale (ASPS) Problem Indicator; 2.2.4 Latent Variables and Composite Indexes 2.2.5 Demographic and Social-Economic Factors 2.2.6 Garbage as a Data Source; 2.2.7 Boxplot as an Input for Decision Making; 2.2.8 A Series of Inputs for Strategic Decision Making; 2.3 Data Transformation; 2.3.1 To Generate Categorical Variables; 2.3.2 To Generate Dummy Variables; 2.4 Biserial Correlation Analysis; 2.5 One-Way Tabulation of a Variable; 2.6 Two-Way Tabulations; 2.6.1 Measure of Associations for Bivariate Categorical Variables; 2.6.2 Other Measures of Association Based on a 2 X 2 Table; 2.6.3 Measures of Association Based on a I X 2 Table; 2.7 Three-Way Tabulation 2.7.1 Conditional Measures of Association for a 2 X 2 X 2 Table 2.7.2 Conditional Odds Ratio for an I X J X 2 Table; 2.8 Special Notes and Comments; 2.9 Special Cases of the N-Way Incomplete Tables; 2.10 Partial Associations; 2.11 Multiple Causal Associations Based on Categorical Variables; 2.11.1 Theoretical and Empirical Concepts of Causal Associations; 2.11.2 Multidimensional Frequency Table; 2.12 Seemingly Causal Model Based on Categorical Variables; 2.12.1 Causal Association Based on (X1, X2, Y1) or (X1, Y1, Y2); 2.12.2 Causal Association Based on (X1, X2, Y1, Y2) 2.12.3 Causal Association Based on Multidimensional Variables |
| Record Nr. | UNINA-9910812400003321 |
|
Agung I Gusti Ngurah
|
||
| Singapore, : John Wiley & Sons, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Panel data analysis using eviews / / I. Gusti Ngurah Agung
| Panel data analysis using eviews / / I. Gusti Ngurah Agung |
| Autore | Agung I Gusti Ngurah |
| Pubbl/distr/stampa | Hoboken : , : Wiley, , 2014 |
| Descrizione fisica | 1 online resource (541 p.) |
| Disciplina | 005.5/5 |
| Soggetto topico | Statistics |
| ISBN |
1-118-71556-X
1-118-71554-3 1-118-71557-8 |
| Classificazione | MAT029000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Panel Data Analysis Using EViews; Contents; Preface; About the Author; Part One: Panel Data as a Multivariate Time Series by States; 1 Data Analysis Based on a Single Time Series by States; 1.1 Introduction; 1.2 Multivariate Growth Models; 1.2.1 Continuous Growth Models; 1.2.2 Discontinuous Growth Models; 1.3 Alternative Multivariate Growth Models; 1.3.1 A Generalization of MAR(p)_GM; 1.3.2 Multivariate Lagged Variables Growth Models; 1.3.3 Multivariate Lagged-Variable Autoregressive Growth Models; 1.3.4 Bounded MLVAR(p; q)_GM; 1.3.5 Special Notes
1.4 Various Models Based on Correlated States1.4.1 Seemingly Causal Models with Trend; 1.4.2 The Application of the Object "VAR"; 1.4.3 The Application of the Instrumental Variables Models; 1.5 Seemingly Causal Models with Time-Related Effects; 1.5.1 SCM Based on the Path Diagram in Figure 1.10(a); 1.5.2 SCM Based on the Path Diagram in Figure 1.10(b); 1.6 The Application of the Object POOL; 1.6.1 What is a Fixed-Effect Model?; 1.6.2 What is a Random Effect Model?; 1.6.3 Special Notes; 1.7 Growth Models of Sample Statistics; 1.8 Special Notes on Time-State Observations 1.9 Growth Models with an Environmental Variable1.9.1 The Simplest Possible Model; 1.9.2 The Application of VAR and VEC Models; 1.9.3 Application of ARCH Model; 1.9.4 The Application of Instrumental Variables Models; 1.10 Models with an Environmental Multivariate; 1.10.1 Bivariate Correlation and Simple Linear Regressions; 1.10.2 Simple Models with an Environmental Multivariate; 1.10.3 The VAR Models; 1.11 Special Piece-Wise Models; 1.11.1 The Application of Growth Models; 1.11.2 Equality Tests by Classifications; 2 Data Analysis Based on Bivariate Time Series by States; 2.1 Introduction 2.2 Models Based on Independent States2.2.1 MAR(p) Growth Model with an Exogenous Variable; 2.2.2 A General MAR(p) Model with an Exogenous Variable; 2.3 Time-Series Models Based on Two Correlated States; 2.3.1 Analysis using the Object System; 2.3.2 Two-SLS Instrumental Variables Models; 2.3.3 Three-SLS Instrumental Variables Models; 2.3.4 Analysis using the Object "VAR"; 2.4 Time-Series Models Based on Multiple Correlated States; 2.4.1 Extension of the Path Diagram in Figure 2.6; 2.4.2 SCMs as VAR Models; 2.5 Time-Series Models with an Environmental Variable Zt, Based on Independent States 2.5.1 The Simplest Possible Model2.5.2 Interaction Models Based on Two Independent States; 2.6 Models Based on Correlated States; 2.6.1 MLV(1) Interaction Model with Trend; 2.6.2 Simultaneous SCMs with Trend; 2.7 Piece-Wise Time-Series Models; 3 Data Analysis Based on Multivariate Time Series by States; 3.1 Introduction; 3.2 Models Based on (X_i,Y_i,Z_i) for Independent States; 3.2.1 MLVAR(p; q) Model with Trend Based on (X_i,Y_i,Z_i); 3.3 Models Based on (X_i, Y_i,Z_i) for Correlated States; 3.3.1 MLV(1) Interaction Model with Trend; 3.3.2 MLV(1) Interaction Model with Time-Related Effects 3.4 Simultaneous SCMs with Trend |
| Record Nr. | UNINA-9910138972803321 |
|
Agung I Gusti Ngurah
|
||
| Hoboken : , : Wiley, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Panel data analysis using eviews / / I. Gusti Ngurah Agung
| Panel data analysis using eviews / / I. Gusti Ngurah Agung |
| Autore | Agung I Gusti Ngurah |
| Pubbl/distr/stampa | Hoboken : , : Wiley, , 2014 |
| Descrizione fisica | 1 online resource (541 p.) |
| Disciplina | 005.5/5 |
| Soggetto topico | Statistics |
| ISBN |
1-118-71556-X
1-118-71554-3 1-118-71557-8 |
| Classificazione | MAT029000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Panel Data Analysis Using EViews; Contents; Preface; About the Author; Part One: Panel Data as a Multivariate Time Series by States; 1 Data Analysis Based on a Single Time Series by States; 1.1 Introduction; 1.2 Multivariate Growth Models; 1.2.1 Continuous Growth Models; 1.2.2 Discontinuous Growth Models; 1.3 Alternative Multivariate Growth Models; 1.3.1 A Generalization of MAR(p)_GM; 1.3.2 Multivariate Lagged Variables Growth Models; 1.3.3 Multivariate Lagged-Variable Autoregressive Growth Models; 1.3.4 Bounded MLVAR(p; q)_GM; 1.3.5 Special Notes
1.4 Various Models Based on Correlated States1.4.1 Seemingly Causal Models with Trend; 1.4.2 The Application of the Object "VAR"; 1.4.3 The Application of the Instrumental Variables Models; 1.5 Seemingly Causal Models with Time-Related Effects; 1.5.1 SCM Based on the Path Diagram in Figure 1.10(a); 1.5.2 SCM Based on the Path Diagram in Figure 1.10(b); 1.6 The Application of the Object POOL; 1.6.1 What is a Fixed-Effect Model?; 1.6.2 What is a Random Effect Model?; 1.6.3 Special Notes; 1.7 Growth Models of Sample Statistics; 1.8 Special Notes on Time-State Observations 1.9 Growth Models with an Environmental Variable1.9.1 The Simplest Possible Model; 1.9.2 The Application of VAR and VEC Models; 1.9.3 Application of ARCH Model; 1.9.4 The Application of Instrumental Variables Models; 1.10 Models with an Environmental Multivariate; 1.10.1 Bivariate Correlation and Simple Linear Regressions; 1.10.2 Simple Models with an Environmental Multivariate; 1.10.3 The VAR Models; 1.11 Special Piece-Wise Models; 1.11.1 The Application of Growth Models; 1.11.2 Equality Tests by Classifications; 2 Data Analysis Based on Bivariate Time Series by States; 2.1 Introduction 2.2 Models Based on Independent States2.2.1 MAR(p) Growth Model with an Exogenous Variable; 2.2.2 A General MAR(p) Model with an Exogenous Variable; 2.3 Time-Series Models Based on Two Correlated States; 2.3.1 Analysis using the Object System; 2.3.2 Two-SLS Instrumental Variables Models; 2.3.3 Three-SLS Instrumental Variables Models; 2.3.4 Analysis using the Object "VAR"; 2.4 Time-Series Models Based on Multiple Correlated States; 2.4.1 Extension of the Path Diagram in Figure 2.6; 2.4.2 SCMs as VAR Models; 2.5 Time-Series Models with an Environmental Variable Zt, Based on Independent States 2.5.1 The Simplest Possible Model2.5.2 Interaction Models Based on Two Independent States; 2.6 Models Based on Correlated States; 2.6.1 MLV(1) Interaction Model with Trend; 2.6.2 Simultaneous SCMs with Trend; 2.7 Piece-Wise Time-Series Models; 3 Data Analysis Based on Multivariate Time Series by States; 3.1 Introduction; 3.2 Models Based on (X_i,Y_i,Z_i) for Independent States; 3.2.1 MLVAR(p; q) Model with Trend Based on (X_i,Y_i,Z_i); 3.3 Models Based on (X_i, Y_i,Z_i) for Correlated States; 3.3.1 MLV(1) Interaction Model with Trend; 3.3.2 MLV(1) Interaction Model with Time-Related Effects 3.4 Simultaneous SCMs with Trend |
| Record Nr. | UNINA-9910828295303321 |
|
Agung I Gusti Ngurah
|
||
| Hoboken : , : Wiley, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Quantile regression : applications on experimental and cross section data using EViews / / I. Gusti Ngurah Agung
| Quantile regression : applications on experimental and cross section data using EViews / / I. Gusti Ngurah Agung |
| Autore | Agung I Gusti Ngurah |
| Pubbl/distr/stampa | Hoboken, NJ : , : John Wiley & Sons, Inc., , 2021 |
| Descrizione fisica | 1 online resource (499 pages) |
| Disciplina | 519.536 |
| Soggetto topico |
Quantile regression
Mathematical statistics |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-71518-0
1-119-71516-4 1-119-71495-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- Preface -- About the Author -- Chapter 1 Test for the Equality of Medians by Series/Group of Variables -- 1.1 Introduction -- 1.2 Test for Equality of Medians of Y1 by Categorical Variables -- 1.3 Test for Equality of Medians of Y1 by Categorical Variables -- 1.4 Testing the Medians of Y1 Categorized by X1 -- 1.5 Testing the Medians of Y1 Categorized by RX1 & -- equals -- @Ranks(X1,a) -- 1.6 Unexpected Statistical Results -- 1.7 Testing the Medians of Y1 by X1 and Categorical Factors -- 1.8 Testing the Medians of Y by Numerical Variables -- 1.8.1 Findings Based on Data& -- uscore -- Faad.wf1 -- 1.8.2 Findings Based on Mlogit.wf1 -- 1.9 Application of the Function @Mediansby(Y,IV) -- Chapter 2 One‐ and Two‐way ANOVA Quantile Regressions -- 2.1 Introduction -- 2.2 One‐way ANOVA Quantile Regression -- 2.3 Alternative Two‐way ANOVA Quantile Regressions -- 2.3.1 Applications of the Simplest Equation Specification -- 2.3.2 Application of the Quantile Process -- 2.3.3 Applications of the Models with Intercepts -- 2.4 Forecasting -- 2.5 Additive Two‐way ANOVA Quantile Regressions -- 2.6 Testing the Quantiles of Y1 Categorized by X1 -- 2.7 Applications of QR on Population Data -- 2.7.1 One‐way‐ANOVA‐QRs -- 2.7.2 Application of the Forecasting -- 2.7.3 Two‐way ANOVA‐QRs -- 2.8 Special Notes and Comments on Alternative Options -- Chapter 3 N‐Way ANOVA Quantile Regressions -- 3.1 Introduction -- 3.2 The Models Without an Intercept -- 3.3 Models with Intercepts -- 3.4 I × J × K Factorial QRs Based on susenas.wf1 -- 3.4.1 Alternative ESs of CWWH on F1, F2, and F3 -- 3.4.1.1 Applications of the Simplest ES in (3.5a) -- 3.4.1.2 Applications of the ES in (3.5b) -- 3.4.1.3 Applications of the ES in (3.5c) -- 3.5 Applications of the N‐Way ANOVA‐QRs -- 3.5.1 Four‐Way ANOVA‐QRs.
Chapter 4 Quantile Regressions Based on (X1,Y1) -- 4.1 Introduction -- 4.2 The Simplest Quantile Regression -- 4.3 Polynomial Quantile Regressions -- 4.3.1 Quadratic Quantile Regression -- 4.3.2 Third Degree Polynomial Quantile Regression -- 4.3.3 Forth Degree Polynomial Quantile Regression -- 4.3.4 Fifth Degree Polynomial Quantile Regression -- 4.4 Logarithmic Quantile Regressions -- 4.4.1 The Simplest Semi‐Logarithmic QR -- 4.4.2 The Semi‐Logarithmic Polynomial QR -- 4.4.2.1 The Basic Semi‐Logarithmic Third Degree Polynomial QR -- 4.4.2.2 The Bounded Semi‐Logarithmic Third Degree Polynomial QR -- 4.5 QRs Based on MCYCLE.WF1 -- 4.5.1 Scatter Graphs of (MILL,ACCEL) with Fitted Curves -- 4.5.2 Applications of Piecewise Linear QRs -- 4.5.3 Applications of the Quantile Process -- 4.5.4 Alterative Piecewise Linear QRs -- 4.5.5 Applications of Piecewise Quadratic QRs -- 4.5.6 Alternative Piecewise Polynomial QRs -- 4.5.7 Applications of Continuous Polynomial QRs -- 4.5.8 Special Notes and Comments -- 4.6 Quantile Regressions Based on SUSENAS‐2013.wf1 -- 4.6.1 Application of CWWH on AGE -- 4.6.1.1 Quantile Regressions of CWWH on AGE -- 4.6.1.2 Application of Logarithmic QRs -- 4.6.2 An Application of Life‐Birth on AGE for Ever Married Women -- 4.6.2.1 QR(Median) of LBIRTH on AGE as a Numerical Predictor -- Chapter 5 Quantile Regressions with Two Numerical Predictors -- 5.1 Introduction -- 5.2 Alternative QRs Based on Data& -- uscore -- Faad.wf1 -- 5.2.1 Alternative QRs Based on (X1,X2,Y1) -- 5.2.1.1 Additive QR -- 5.2.1.2 Semi‐Logarithmic QR of log(Y1) on X1 and X2 -- 5.2.1.3 Translog QR of log(Y1) on log(X1) and log(X2) -- 5.2.2 Two‐Way Interaction QRs -- 5.2.2.1 Interaction QR of Y1 on X1 and X2 -- 5.2.2.2 Semi‐Logarithmic Interaction QR Based on (X1,X2,Y1) -- 5.2.2.3 Translogarithmic Interaction QR Based on (X1,X2,Y1). 5.3 An Analysis Based on Mlogit.wf1 -- 5.3.1 Alternative QRs of LW -- 5.3.2 Alternative QRs of INC -- 5.3.2.1 Using Z‐Scores Variables as Predictors -- 5.3.2.2 Alternative QRs of INC on Other Sets of Numerical Predictors -- 5.3.2.3 Alternative QRs Based on Other Sets of Numerical Variables -- 5.4 Polynomial Two‐Way Interaction QRs -- 5.5 Double Polynomial QRs -- 5.5.1 Additive Double Polynomial QRs -- 5.5.2 Interaction Double Polynomial QRs -- Chapter 6 Quantile Regressions with Multiple Numerical Predictors -- 6.1 Introduction -- 6.2 Alternative Path Diagrams Based on (X1,X2,X3,Y1) -- 6.2.1 A QR Based on the Path Diagram in Figure a -- 6.2.2 A QR Based on the Path Diagram in Figure b -- 6.2.3 QR Based on the Path Diagram in Figure c -- 6.2.3.1 A Full Two‐Way Interaction QR -- 6.2.3.2 A Full Three‐Way Interaction QR -- 6.2.4 QR Based on the Path Diagram in Figure d -- 6.3 Applications of QRs Based on Data& -- uscore -- Faad.wf1 -- 6.4 Applications of QRs Based on Data in Mlogit.wf1 -- 6.5 QRs of PR1 on (DIST1,X1,X2) -- 6.6 Advanced Statistical Analysis -- 6.6.1 Applications of the Quantiles Process -- 6.6.1.1 An Application of the Process Coefficients -- 6.6.1.2 An Application of the Quantile Slope Equality Test -- 6.6.1.3 An Application of the Symmetric Quantiles Test -- 6.6.2 An Application of the Ramsey RESET Test -- 6.6.3 Residual Diagnostics -- 6.7 Forecasting -- 6.7.1 Basic Forecasting -- 6.7.2 Advanced Forecasting -- 6.8 Developing a Complete Data& -- uscore -- LW.wf1 -- 6.9 QRs with Four Numerical Predictors -- 6.9.1 An Additive QR -- 6.9.2 Alternative Two‐Way Interaction QRs -- 6.9.2.1 A Two‐Way Interaction QR Based on Figure a -- 6.9.2.2 A Two‐Way Interaction QR Based on Figure b -- 6.9.2.3 A Two‐Way Interaction QR Based on Figure c -- 6.9.2.4 A Two‐Way Interaction QR Based on Figure d -- 6.9.3 Alternative Three‐Way Interaction QRs. 6.9.3.1 Alternative Models Based on Figure a -- 6.9.3.2 Alternative Models Based on Figure b -- 6.9.3.3 Alternative Models Based on Figure c -- 6.9.3.4 Alternative Models Based on Figure d -- 6.10 QRs with Multiple Numerical Predictors -- 6.10.1 Developing an Additive QR -- 6.10.2 Developing a Simple Two‐Way Interaction QR -- 6.10.3 Developing a Simple Three‐Way Interaction QR -- Chapter 7 Quantile Regressions with the Ranks of Numerical Predictors -- 7.1 Introduction -- 7.2 NPQRs Based on a Single Rank Predictor -- 7.2.1 Alternative Piecewise NPQRs of ACCEL on R& -- uscore -- Milli -- 7.2.2 Polynomial NPQRs of ACCEL on R& -- uscore -- Milli -- 7.2.3 Special Notes and Comments -- 7.3 NPQRs on Group of R& -- uscore -- Milli -- 7.3.1 An Application of the G& -- uscore -- Milli as a Categorical Variable -- 7.3.2 The kth‐Degree Polynomial NPQRs of ACCEL on G& -- uscore -- Milli -- 7.4 Multiple NPQRs Based on Data‐Faad.wf1 -- 7.4.1 An NPQR Based on a Triple Numerical Variable (X1,X2,Y) -- 7.4.2 NPQRs with Multi‐Rank Predictors -- 7.5 Multiple NPQRs Based on MLogit.wf1 -- Chapter 8 Heterogeneous Quantile Regressions Based on Experimental Data -- 8.1 Introduction -- 8.2 HQRs of Y1 on X1 by a Cell‐Factor -- 8.2.1 The Simplest HQR -- 8.2.2 A Piecewise Quadratic QR -- 8.2.3 A Piecewise Polynomial Quantile Regression -- 8.3 HLQR of Y1 on (X1,X2) by the Cell‐Factor -- 8.3.1 Additive HLQR of Y1 on (X1,X2) by CF -- 8.3.2 A Two‐Way Interaction Heterogeneous‐QR of Y1 on (X1,X2) by CF -- 8.3.3 An Application of Translog‐Linear QR of Y1 on (X1,X2) by CF -- 8.4 The HLQR of Y1 on (X1,X2,X3) by a Cell‐Factor -- 8.4.1 An Additive HLQR of Y1 on (X1,X2,X3) by CF -- 8.4.2 A Full Two‐Way Interaction HQR of Y1 on (X1,X2,X3) by CF -- 8.4.3 A Full Three‐Way Interaction HQR of Y1 on (X1,X2,X3) by CF -- Chapter 9 Quantile Regressions Based on CPS88.wf1. 9.1 Introduction -- 9.2 Applications of an ANOVA Quantile Regression -- 9.2.1 One‐Way ANOVA‐QR -- 9.2.2 Two‐Way ANOVA Quantile Regression -- 9.2.2.1 The Simplest Equation of Two‐Way ANOVA‐QR -- 9.2.2.2 A Special Equation of the Two‐Way ANOVA‐QR -- 9.2.2.3 An Additive Two‐Way ANOVA‐QR -- 9.2.3 Three‐Way ANOVA‐QRs -- 9.3 Quantile Regressions with Numerical Predictors -- 9.3.1 QR of LWAGE on GRADE -- 9.3.1.1 A Polynomial QR of LWAGE on GRADE -- 9.3.1.2 The Simplest Linear QR of Y1 on a Numerical X1 -- 9.3.2 Quantile Regressions of Y1 on (X1,X2) -- 9.3.2.1 Hierarchical and Nonhierarchical Two‐Way Interaction QRs -- 9.3.2.2 A Special Polynomial Interaction QR -- 9.3.2.3 A Double Polynomial Interaction QR of Y1 on (X1,X2) -- 9.3.3 QRs of Y1 on Numerical Variables (X1,X2,X3) -- 9.3.3.1 A Full Two‐Way Interaction QR -- 9.3.3.2 A Full‐Three‐Way‐Interaction QR -- 9.4 Heterogeneous Quantile‐Regressions -- 9.4.1 Heterogeneous Quantile Regressions by a Factor -- 9.4.1.1 A Heterogeneous Linear QR of LWAGE on POTEXP by IND1 -- 9.4.1.2 A Heterogeneous Third‐Degree Polynomial QR of LWAGE on GRADE -- 9.4.1.3 An Application of QR for a Large Number of Groups -- 9.4.1.4 Comparison Between Selected Heterogeneous QR(Median) -- Chapter 10 Quantile Regressions of a Latent Variable -- 10.1 Introduction -- 10.2 Spearman‐rank Correlation -- 10.3 Applications of ANOVA‐QR(τ) -- 10.3.1 One‐way ANOVA‐QR of BLV -- 10.3.2 A Two‐Way ANOVA‐QR of BLV -- 10.3.2.1 The Simplest Equation of a Two‐Way ANOVA‐QR of BLV -- 10.3.2.2 A Two‐way ANOVA‐QR of BLV with an Intercept -- 10.3.2.3 A Special Equation of Two‐Way ANOVA‐QR of BLV -- 10.4 Three‐way ANOVA‐QR of BLV -- 10.5 QRs of BLV on Numerical Predictors -- 10.5.1 QRs of BLV on MW -- 10.5.1.1 The Simplest Linear Regression of BLV on MW -- 10.5.1.2 Polynomial Regression of BLV on MW -- 10.5.2 QRs of BLV on Two Numerical Predictors. 10.5.2.1 An Additive QR of BLV. |
| Record Nr. | UNINA-9910555112403321 |
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Agung I Gusti Ngurah
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| Hoboken, NJ : , : John Wiley & Sons, Inc., , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Quantile regression : applications on experimental and cross section data using EViews / / I. Gusti Ngurah Agung
| Quantile regression : applications on experimental and cross section data using EViews / / I. Gusti Ngurah Agung |
| Autore | Agung I Gusti Ngurah |
| Pubbl/distr/stampa | Hoboken, NJ : , : John Wiley & Sons, Inc., , 2021 |
| Descrizione fisica | 1 online resource (499 pages) |
| Disciplina | 519.536 |
| Soggetto topico |
Quantile regression
Mathematical statistics |
| ISBN |
1-119-71518-0
1-119-71516-4 1-119-71495-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- Preface -- About the Author -- Chapter 1 Test for the Equality of Medians by Series/Group of Variables -- 1.1 Introduction -- 1.2 Test for Equality of Medians of Y1 by Categorical Variables -- 1.3 Test for Equality of Medians of Y1 by Categorical Variables -- 1.4 Testing the Medians of Y1 Categorized by X1 -- 1.5 Testing the Medians of Y1 Categorized by RX1 & -- equals -- @Ranks(X1,a) -- 1.6 Unexpected Statistical Results -- 1.7 Testing the Medians of Y1 by X1 and Categorical Factors -- 1.8 Testing the Medians of Y by Numerical Variables -- 1.8.1 Findings Based on Data& -- uscore -- Faad.wf1 -- 1.8.2 Findings Based on Mlogit.wf1 -- 1.9 Application of the Function @Mediansby(Y,IV) -- Chapter 2 One‐ and Two‐way ANOVA Quantile Regressions -- 2.1 Introduction -- 2.2 One‐way ANOVA Quantile Regression -- 2.3 Alternative Two‐way ANOVA Quantile Regressions -- 2.3.1 Applications of the Simplest Equation Specification -- 2.3.2 Application of the Quantile Process -- 2.3.3 Applications of the Models with Intercepts -- 2.4 Forecasting -- 2.5 Additive Two‐way ANOVA Quantile Regressions -- 2.6 Testing the Quantiles of Y1 Categorized by X1 -- 2.7 Applications of QR on Population Data -- 2.7.1 One‐way‐ANOVA‐QRs -- 2.7.2 Application of the Forecasting -- 2.7.3 Two‐way ANOVA‐QRs -- 2.8 Special Notes and Comments on Alternative Options -- Chapter 3 N‐Way ANOVA Quantile Regressions -- 3.1 Introduction -- 3.2 The Models Without an Intercept -- 3.3 Models with Intercepts -- 3.4 I × J × K Factorial QRs Based on susenas.wf1 -- 3.4.1 Alternative ESs of CWWH on F1, F2, and F3 -- 3.4.1.1 Applications of the Simplest ES in (3.5a) -- 3.4.1.2 Applications of the ES in (3.5b) -- 3.4.1.3 Applications of the ES in (3.5c) -- 3.5 Applications of the N‐Way ANOVA‐QRs -- 3.5.1 Four‐Way ANOVA‐QRs.
Chapter 4 Quantile Regressions Based on (X1,Y1) -- 4.1 Introduction -- 4.2 The Simplest Quantile Regression -- 4.3 Polynomial Quantile Regressions -- 4.3.1 Quadratic Quantile Regression -- 4.3.2 Third Degree Polynomial Quantile Regression -- 4.3.3 Forth Degree Polynomial Quantile Regression -- 4.3.4 Fifth Degree Polynomial Quantile Regression -- 4.4 Logarithmic Quantile Regressions -- 4.4.1 The Simplest Semi‐Logarithmic QR -- 4.4.2 The Semi‐Logarithmic Polynomial QR -- 4.4.2.1 The Basic Semi‐Logarithmic Third Degree Polynomial QR -- 4.4.2.2 The Bounded Semi‐Logarithmic Third Degree Polynomial QR -- 4.5 QRs Based on MCYCLE.WF1 -- 4.5.1 Scatter Graphs of (MILL,ACCEL) with Fitted Curves -- 4.5.2 Applications of Piecewise Linear QRs -- 4.5.3 Applications of the Quantile Process -- 4.5.4 Alterative Piecewise Linear QRs -- 4.5.5 Applications of Piecewise Quadratic QRs -- 4.5.6 Alternative Piecewise Polynomial QRs -- 4.5.7 Applications of Continuous Polynomial QRs -- 4.5.8 Special Notes and Comments -- 4.6 Quantile Regressions Based on SUSENAS‐2013.wf1 -- 4.6.1 Application of CWWH on AGE -- 4.6.1.1 Quantile Regressions of CWWH on AGE -- 4.6.1.2 Application of Logarithmic QRs -- 4.6.2 An Application of Life‐Birth on AGE for Ever Married Women -- 4.6.2.1 QR(Median) of LBIRTH on AGE as a Numerical Predictor -- Chapter 5 Quantile Regressions with Two Numerical Predictors -- 5.1 Introduction -- 5.2 Alternative QRs Based on Data& -- uscore -- Faad.wf1 -- 5.2.1 Alternative QRs Based on (X1,X2,Y1) -- 5.2.1.1 Additive QR -- 5.2.1.2 Semi‐Logarithmic QR of log(Y1) on X1 and X2 -- 5.2.1.3 Translog QR of log(Y1) on log(X1) and log(X2) -- 5.2.2 Two‐Way Interaction QRs -- 5.2.2.1 Interaction QR of Y1 on X1 and X2 -- 5.2.2.2 Semi‐Logarithmic Interaction QR Based on (X1,X2,Y1) -- 5.2.2.3 Translogarithmic Interaction QR Based on (X1,X2,Y1). 5.3 An Analysis Based on Mlogit.wf1 -- 5.3.1 Alternative QRs of LW -- 5.3.2 Alternative QRs of INC -- 5.3.2.1 Using Z‐Scores Variables as Predictors -- 5.3.2.2 Alternative QRs of INC on Other Sets of Numerical Predictors -- 5.3.2.3 Alternative QRs Based on Other Sets of Numerical Variables -- 5.4 Polynomial Two‐Way Interaction QRs -- 5.5 Double Polynomial QRs -- 5.5.1 Additive Double Polynomial QRs -- 5.5.2 Interaction Double Polynomial QRs -- Chapter 6 Quantile Regressions with Multiple Numerical Predictors -- 6.1 Introduction -- 6.2 Alternative Path Diagrams Based on (X1,X2,X3,Y1) -- 6.2.1 A QR Based on the Path Diagram in Figure a -- 6.2.2 A QR Based on the Path Diagram in Figure b -- 6.2.3 QR Based on the Path Diagram in Figure c -- 6.2.3.1 A Full Two‐Way Interaction QR -- 6.2.3.2 A Full Three‐Way Interaction QR -- 6.2.4 QR Based on the Path Diagram in Figure d -- 6.3 Applications of QRs Based on Data& -- uscore -- Faad.wf1 -- 6.4 Applications of QRs Based on Data in Mlogit.wf1 -- 6.5 QRs of PR1 on (DIST1,X1,X2) -- 6.6 Advanced Statistical Analysis -- 6.6.1 Applications of the Quantiles Process -- 6.6.1.1 An Application of the Process Coefficients -- 6.6.1.2 An Application of the Quantile Slope Equality Test -- 6.6.1.3 An Application of the Symmetric Quantiles Test -- 6.6.2 An Application of the Ramsey RESET Test -- 6.6.3 Residual Diagnostics -- 6.7 Forecasting -- 6.7.1 Basic Forecasting -- 6.7.2 Advanced Forecasting -- 6.8 Developing a Complete Data& -- uscore -- LW.wf1 -- 6.9 QRs with Four Numerical Predictors -- 6.9.1 An Additive QR -- 6.9.2 Alternative Two‐Way Interaction QRs -- 6.9.2.1 A Two‐Way Interaction QR Based on Figure a -- 6.9.2.2 A Two‐Way Interaction QR Based on Figure b -- 6.9.2.3 A Two‐Way Interaction QR Based on Figure c -- 6.9.2.4 A Two‐Way Interaction QR Based on Figure d -- 6.9.3 Alternative Three‐Way Interaction QRs. 6.9.3.1 Alternative Models Based on Figure a -- 6.9.3.2 Alternative Models Based on Figure b -- 6.9.3.3 Alternative Models Based on Figure c -- 6.9.3.4 Alternative Models Based on Figure d -- 6.10 QRs with Multiple Numerical Predictors -- 6.10.1 Developing an Additive QR -- 6.10.2 Developing a Simple Two‐Way Interaction QR -- 6.10.3 Developing a Simple Three‐Way Interaction QR -- Chapter 7 Quantile Regressions with the Ranks of Numerical Predictors -- 7.1 Introduction -- 7.2 NPQRs Based on a Single Rank Predictor -- 7.2.1 Alternative Piecewise NPQRs of ACCEL on R& -- uscore -- Milli -- 7.2.2 Polynomial NPQRs of ACCEL on R& -- uscore -- Milli -- 7.2.3 Special Notes and Comments -- 7.3 NPQRs on Group of R& -- uscore -- Milli -- 7.3.1 An Application of the G& -- uscore -- Milli as a Categorical Variable -- 7.3.2 The kth‐Degree Polynomial NPQRs of ACCEL on G& -- uscore -- Milli -- 7.4 Multiple NPQRs Based on Data‐Faad.wf1 -- 7.4.1 An NPQR Based on a Triple Numerical Variable (X1,X2,Y) -- 7.4.2 NPQRs with Multi‐Rank Predictors -- 7.5 Multiple NPQRs Based on MLogit.wf1 -- Chapter 8 Heterogeneous Quantile Regressions Based on Experimental Data -- 8.1 Introduction -- 8.2 HQRs of Y1 on X1 by a Cell‐Factor -- 8.2.1 The Simplest HQR -- 8.2.2 A Piecewise Quadratic QR -- 8.2.3 A Piecewise Polynomial Quantile Regression -- 8.3 HLQR of Y1 on (X1,X2) by the Cell‐Factor -- 8.3.1 Additive HLQR of Y1 on (X1,X2) by CF -- 8.3.2 A Two‐Way Interaction Heterogeneous‐QR of Y1 on (X1,X2) by CF -- 8.3.3 An Application of Translog‐Linear QR of Y1 on (X1,X2) by CF -- 8.4 The HLQR of Y1 on (X1,X2,X3) by a Cell‐Factor -- 8.4.1 An Additive HLQR of Y1 on (X1,X2,X3) by CF -- 8.4.2 A Full Two‐Way Interaction HQR of Y1 on (X1,X2,X3) by CF -- 8.4.3 A Full Three‐Way Interaction HQR of Y1 on (X1,X2,X3) by CF -- Chapter 9 Quantile Regressions Based on CPS88.wf1. 9.1 Introduction -- 9.2 Applications of an ANOVA Quantile Regression -- 9.2.1 One‐Way ANOVA‐QR -- 9.2.2 Two‐Way ANOVA Quantile Regression -- 9.2.2.1 The Simplest Equation of Two‐Way ANOVA‐QR -- 9.2.2.2 A Special Equation of the Two‐Way ANOVA‐QR -- 9.2.2.3 An Additive Two‐Way ANOVA‐QR -- 9.2.3 Three‐Way ANOVA‐QRs -- 9.3 Quantile Regressions with Numerical Predictors -- 9.3.1 QR of LWAGE on GRADE -- 9.3.1.1 A Polynomial QR of LWAGE on GRADE -- 9.3.1.2 The Simplest Linear QR of Y1 on a Numerical X1 -- 9.3.2 Quantile Regressions of Y1 on (X1,X2) -- 9.3.2.1 Hierarchical and Nonhierarchical Two‐Way Interaction QRs -- 9.3.2.2 A Special Polynomial Interaction QR -- 9.3.2.3 A Double Polynomial Interaction QR of Y1 on (X1,X2) -- 9.3.3 QRs of Y1 on Numerical Variables (X1,X2,X3) -- 9.3.3.1 A Full Two‐Way Interaction QR -- 9.3.3.2 A Full‐Three‐Way‐Interaction QR -- 9.4 Heterogeneous Quantile‐Regressions -- 9.4.1 Heterogeneous Quantile Regressions by a Factor -- 9.4.1.1 A Heterogeneous Linear QR of LWAGE on POTEXP by IND1 -- 9.4.1.2 A Heterogeneous Third‐Degree Polynomial QR of LWAGE on GRADE -- 9.4.1.3 An Application of QR for a Large Number of Groups -- 9.4.1.4 Comparison Between Selected Heterogeneous QR(Median) -- Chapter 10 Quantile Regressions of a Latent Variable -- 10.1 Introduction -- 10.2 Spearman‐rank Correlation -- 10.3 Applications of ANOVA‐QR(τ) -- 10.3.1 One‐way ANOVA‐QR of BLV -- 10.3.2 A Two‐Way ANOVA‐QR of BLV -- 10.3.2.1 The Simplest Equation of a Two‐Way ANOVA‐QR of BLV -- 10.3.2.2 A Two‐way ANOVA‐QR of BLV with an Intercept -- 10.3.2.3 A Special Equation of Two‐Way ANOVA‐QR of BLV -- 10.4 Three‐way ANOVA‐QR of BLV -- 10.5 QRs of BLV on Numerical Predictors -- 10.5.1 QRs of BLV on MW -- 10.5.1.1 The Simplest Linear Regression of BLV on MW -- 10.5.1.2 Polynomial Regression of BLV on MW -- 10.5.2 QRs of BLV on Two Numerical Predictors. 10.5.2.1 An Additive QR of BLV. |
| Record Nr. | UNINA-9910829888103321 |
|
Agung I Gusti Ngurah
|
||
| Hoboken, NJ : , : John Wiley & Sons, Inc., , 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Time series data analysis using EViews [[electronic resource] /] / I Gusti Ngurah Agung
| Time series data analysis using EViews [[electronic resource] /] / I Gusti Ngurah Agung |
| Autore | Agung I Gusti Ngurah |
| Pubbl/distr/stampa | Hoboken, NJ, : Wiley, c2009 |
| Descrizione fisica | 1 online resource (634 p.) |
| Disciplina |
519.22
519.5/5 |
| Collana | Statistics in practice Time series data analysis using EViews |
| Soggetto topico |
Time-series analysis
Econometric models |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-118-17630-8
1-282-38217-9 9786612382178 0-470-82369-0 0-470-82368-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
TIME SERIES DATA ANALYSIS USING EVIEWS; Contents; Preface; 1 EViews workfile and descriptive data analysis; 2 Continuous growth models; 3 Discontinuous growth models; 4 Seemingly causal models; 5 Special cases of regression models; 6 VAR and system estimation methods; 7 Instrumental variables models; 8 ARCH models; 9 Additional testing hypotheses; 10 Nonlinear least squares models; 11 Nonparametric estimation methods; Appendix A: Models for a single time series; Appendix B: Simple linear models; Appendix C: General linear models; Appendix D: Multivariate general linear models; References
Index |
| Record Nr. | UNINA-9910139772703321 |
|
Agung I Gusti Ngurah
|
||
| Hoboken, NJ, : Wiley, c2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Time series data analysis using EViews [[electronic resource] /] / I Gusti Ngurah Agung
| Time series data analysis using EViews [[electronic resource] /] / I Gusti Ngurah Agung |
| Autore | Agung I Gusti Ngurah |
| Pubbl/distr/stampa | Hoboken, NJ, : Wiley, c2009 |
| Descrizione fisica | 1 online resource (634 p.) |
| Disciplina |
519.22
519.5/5 |
| Collana | Statistics in practice Time series data analysis using EViews |
| Soggetto topico |
Time-series analysis
Econometric models |
| ISBN |
1-118-17630-8
1-282-38217-9 9786612382178 0-470-82369-0 0-470-82368-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
TIME SERIES DATA ANALYSIS USING EVIEWS; Contents; Preface; 1 EViews workfile and descriptive data analysis; 2 Continuous growth models; 3 Discontinuous growth models; 4 Seemingly causal models; 5 Special cases of regression models; 6 VAR and system estimation methods; 7 Instrumental variables models; 8 ARCH models; 9 Additional testing hypotheses; 10 Nonlinear least squares models; 11 Nonparametric estimation methods; Appendix A: Models for a single time series; Appendix B: Simple linear models; Appendix C: General linear models; Appendix D: Multivariate general linear models; References
Index |
| Record Nr. | UNINA-9910830681103321 |
|
Agung I Gusti Ngurah
|
||
| Hoboken, NJ, : Wiley, c2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Time series data analysis using EViews [[electronic resource] /] / I Gusti Ngurah Agung
| Time series data analysis using EViews [[electronic resource] /] / I Gusti Ngurah Agung |
| Autore | Agung I Gusti Ngurah |
| Pubbl/distr/stampa | Hoboken, NJ, : Wiley, c2009 |
| Descrizione fisica | 1 online resource (634 p.) |
| Disciplina |
519.22
519.5/5 |
| Collana | Statistics in practice Time series data analysis using EViews |
| Soggetto topico |
Time-series analysis
Econometric models |
| ISBN |
1-118-17630-8
1-282-38217-9 9786612382178 0-470-82369-0 0-470-82368-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
TIME SERIES DATA ANALYSIS USING EVIEWS; Contents; Preface; 1 EViews workfile and descriptive data analysis; 2 Continuous growth models; 3 Discontinuous growth models; 4 Seemingly causal models; 5 Special cases of regression models; 6 VAR and system estimation methods; 7 Instrumental variables models; 8 ARCH models; 9 Additional testing hypotheses; 10 Nonlinear least squares models; 11 Nonparametric estimation methods; Appendix A: Models for a single time series; Appendix B: Simple linear models; Appendix C: General linear models; Appendix D: Multivariate general linear models; References
Index |
| Record Nr. | UNINA-9910841051903321 |
|
Agung I Gusti Ngurah
|
||
| Hoboken, NJ, : Wiley, c2009 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||