Essays in honour of Fabio Canova / edited by Juan José Dolado, Luca Gambetti, Christian Matthes.

Format:

Book

Contributor:

Dolado, Juan J., editor.

Gambetti, Luca, editor.

Matthes, Christian, 1979- editor.

Series:

Advances in econometrics ; Volume 44, Part A.

Advances in econometrics ; Volume 44, Part A

Language:

English

Subjects (All):

Econometrics.

Physical Description:

1 online resource (224 pages)

Place of Publication:

Bingley, England : Emerald Publishing, [2022]

Summary:

Both parts of Volume 44 of Advances in Econometricspay tribute to Fabio Canova for his major contributions to economics over the last four decades.

Contents:

Intro

Half Title Page

Series Editors

Title Page

Copyright Page

Contents

List of Contributors

Introduction

References

Chapter 1: Real-Time Real Economic Activity: Entering and Exiting the Pandemic Recession of 2020

1. Introduction

2. Nowcast Construction, Characteristics, and Assessment

2.1. Construction and Updating

2.2. Ex Post Characteristics

2.3. Performance Assessment

3. The Pandemic Recession Entry and Exit

3.1. A Detailed Look at the Later-Vintage Path

3.2. Real-time Vintages

3.2.1. Five Snapshots

3.2.2. The Full Path Plot and Dot Plot

3.3. Real Economic Activity and COVID-19

4. Comparison to the Great Recession Exit

5. Concluding Remarks and Directions for Future Research

Chapter 2: State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models

2. The Models

2.1. Modelling State Correlation

UC1 and UC2 Models

UC3 and UC4 Models

3. State Correlation and the Forecasting Function

4. Bayesian Inference

4.1. Posterior Analysis

4.1.1. Disturbance Smoothing

5. Evaluation

5.1. Forecast Metrics

5.2. Out-of-Sample Results

5.3. Correlation, Trend Inflation and Inflation Expectations

5.4. Robustness

6. Conclusion

Chapter 3: On Identification Issues in Business Cycle Accounting Models

1 Introduction

2 The Prototype (M)BCA Economy

2.1 Description of the Economy

2.2 Equilibrium Conditions

2.3 Operational Model

3 Methodology

3.1 State Space Form

3.2 Estimated Parameters

3.3 Komunjer and Ng (2011) Test for Strict Identification

3.4 Iskrev (2010) Test for Strict and Weak Identification

3.4.1 Preliminaries

3.4.2 General Principles of Identification Analysis

3.4.3 Identification Strength

4 Results

4.1 Komunjer and Ng (2011).

4.1.1 Chari et al. (2007) BCA Model

4.1.2 Šustek (2011) MBCA Model

4.2 Iskrev (2010)

4.2.1 Chari et al. (2007) BCA Model

4.2.2 Šustek (2011) MBCA Model

5 Economic Relevance

5.1 Chari et al. (2007) BCA Model

5.2 Brinca et al. (2016) Multi-country BCA Analysis

6 Statistics for Practitioners

6.1 Empirical Distance Measures

7 Conclusion

A. Appendix - BCA and MBCA Model with Investment Adjustment Costs

A.1. Komunjer and Ng (2011)

A.2 Iskrev (2010)

A.2.1 Chari et al. (2007) BCA Model

A.1.1 Chari et al. (2007) BCA Model

A.1.2 Šustek (2011) MBCA Model

B. Appendix - Model Derivations

B.1. Representative Consumer

B.1.1. Optimization Problem of the Household

B.1.2. Lagrangian Function

B.1.3. First-order Necessary Conditions

B.2. Representative Producer

B.2.1. Optimization Problem of the Firm

B.3. Additional Model Equations

B.4. Functional Forms and Auxiliary Assumptions

B.5. Operational Model

B.6. Steady State

B.7. Definitions

B.7.1. Variables

B.7.2. Parameters

C. Appendix - Gensys State Space

C.1. Log-linearized Equilibrium Conditions

C.2. Allowing for Adjustment Costs

C.3. Extension to MBCA - Šustek (2011)

C.4.1. BCA - Chari et al. (2007)

C.4.2. MBCA - Šustek (2011)

C.4.3. BCA - Chari et al. (2007) With Adjustment Costs

C.4.4. MBCA - Šustek (2011) with Adjustment Costs

D. Appendix - Derivatives with Alternativestepsize

Chapter 4: The Effect of News Shocks and Monetary Policy

2. Data and the VAR Model

3. Results

4. Conclusion

Chapter 5: Statistical Identification of Economic Shocks by Signs in Structural Vector Autoregression

2. Model

3. Application to the US Labour Market

3.1. GMM Estimation

3.2. Empirical Results

References.

Chapter 6: kewed SVARs: Tracking the Structural Sources of Macroeconomic Tail Risks

2 A SVAR Model With Skewed Shocks

2.1 Bayesian Estimation of the Skewed SVAR Model

3. Tracking Macroeconomic Tail Risks in the Euro Area

3.1. Data and Model Specification

3.2 The Evolution of Skewness in the Euro Area

3.3 The Macroeconomic Impact of Time Varying Skewness

3.3.1 Historical Shock Decomposition

3.3.2 Impulse Response Function Analysis

3.3.3 The Impact of Asymmetric Risks on the Forecasting Distributions

4 Conclusion

Appendix

A. Estimated Monthly Real GDP Growth

Index.

Notes:

Description based on print version record.

Includes bibliographical references and index.

Other Format:

Print version: Dolado, Juan J. Essays in Honour of Fabio Canova

ISBN:

1-80382-635-5