Motivated by the desire to inform macroeconomic policy-makers in order to make decisions, this dissertation consists of a compilation of three essays devoted to three different economic questions using three different econometric methods. In particular, Chapter 1 proposes a Markov-switching model to estimate the probability of being in a state characterized by a housing boom fueled by credit (HBFC). This state can be understood as the one potentially previous to a housing bubble, so that it informs policymakers about a dangerous macrofinancial risk at a time in which it might still be possible to avoid the housing bubble and its likely consequential turmoil. Estimated with US data, such model proved consistency in identifying HBFC preceding housing bubbles as estimated in the literature. Chapter 2 is devoted to a thick modeling tool to forecast residential investment. Housing investment is known to be an important leading indicator of economic activity, so its forecast seems key for policymaking. Estimated with euro area (EA) and EA largest five countries data, this tool proved successful in beating benchmark models, while also highlighting the importance of including building permits in housing investment models. Finally, Chapter 3 estimates the effects of monetary policy shocks in the EA and EA largest four economies by means of a factor-augmented Bayesian VAR model identified by sign and narrative sign restrictions. Results show that such shock has significantly negative effects on euro area economic activity, while also show a high degree of heterogeneity in the euro area countries. Looking forward, the methodologies used in this PhD dissertation have proved to be very flexible, so that they could be applied also to other interesting applications.

Motivated by the desire to inform macroeconomic policy-makers in order to make decisions, this dissertation consists of a compilation of three essays devoted to three different economic questions using three different econometric methods. In particular, Chapter 1 proposes a Markov-switching model to estimate the probability of being in a state characterized by a housing boom fueled by credit (HBFC). This state can be understood as the one potentially previous to a housing bubble, so that it informs policymakers about a dangerous macrofinancial risk at a time in which it might still be possible to avoid the housing bubble and its likely consequential turmoil. Estimated with US data, such model proved consistency in identifying HBFC preceding housing bubbles as estimated in the literature. Chapter 2 is devoted to a thick modeling tool to forecast residential investment. Housing investment is known to be an important leading indicator of economic activity, so its forecast seems key for policymaking. Estimated with euro area (EA) and EA largest five countries data, this tool proved successful in beating benchmark models, while also highlighting the importance of including building permits in housing investment models. Finally, Chapter 3 estimates the effects of monetary policy shocks in the EA and EA largest four economies by means of a factor-augmented Bayesian VAR model identified by sign and narrative sign restrictions. Results show that such shock has significantly negative effects on euro area economic activity, while also show a high degree of heterogeneity in the euro area countries. Looking forward, the methodologies used in this PhD dissertation have proved to be very flexible, so that they could be applied also to other interesting applications.

(2022). Essays on empirical macroeconomics. (Tesi di dottorato, Università degli Studi di Milano-Bicocca, 2022).

Essays on empirical macroeconomics

CANIZARES MARTINEZ, CARLOS
2022

Abstract

Motivated by the desire to inform macroeconomic policy-makers in order to make decisions, this dissertation consists of a compilation of three essays devoted to three different economic questions using three different econometric methods. In particular, Chapter 1 proposes a Markov-switching model to estimate the probability of being in a state characterized by a housing boom fueled by credit (HBFC). This state can be understood as the one potentially previous to a housing bubble, so that it informs policymakers about a dangerous macrofinancial risk at a time in which it might still be possible to avoid the housing bubble and its likely consequential turmoil. Estimated with US data, such model proved consistency in identifying HBFC preceding housing bubbles as estimated in the literature. Chapter 2 is devoted to a thick modeling tool to forecast residential investment. Housing investment is known to be an important leading indicator of economic activity, so its forecast seems key for policymaking. Estimated with euro area (EA) and EA largest five countries data, this tool proved successful in beating benchmark models, while also highlighting the importance of including building permits in housing investment models. Finally, Chapter 3 estimates the effects of monetary policy shocks in the EA and EA largest four economies by means of a factor-augmented Bayesian VAR model identified by sign and narrative sign restrictions. Results show that such shock has significantly negative effects on euro area economic activity, while also show a high degree of heterogeneity in the euro area countries. Looking forward, the methodologies used in this PhD dissertation have proved to be very flexible, so that they could be applied also to other interesting applications.
PELAGATTI, MATTEO MARIA
CERASI, VITTORIA
Motivated by the desire to inform macroeconomic policy-makers in order to make decisions, this dissertation consists of a compilation of three essays devoted to three different economic questions using three different econometric methods. In particular, Chapter 1 proposes a Markov-switching model to estimate the probability of being in a state characterized by a housing boom fueled by credit (HBFC). This state can be understood as the one potentially previous to a housing bubble, so that it informs policymakers about a dangerous macrofinancial risk at a time in which it might still be possible to avoid the housing bubble and its likely consequential turmoil. Estimated with US data, such model proved consistency in identifying HBFC preceding housing bubbles as estimated in the literature. Chapter 2 is devoted to a thick modeling tool to forecast residential investment. Housing investment is known to be an important leading indicator of economic activity, so its forecast seems key for policymaking. Estimated with euro area (EA) and EA largest five countries data, this tool proved successful in beating benchmark models, while also highlighting the importance of including building permits in housing investment models. Finally, Chapter 3 estimates the effects of monetary policy shocks in the EA and EA largest four economies by means of a factor-augmented Bayesian VAR model identified by sign and narrative sign restrictions. Results show that such shock has significantly negative effects on euro area economic activity, while also show a high degree of heterogeneity in the euro area countries. Looking forward, the methodologies used in this PhD dissertation have proved to be very flexible, so that they could be applied also to other interesting applications.
Housing booms; Credit; Housing investment; Monetary policy; EA heterogeneity
Housing booms; Credit; Housing investment; Monetary policy; EA heterogeneity
SECS-P/06 - ECONOMIA APPLICATA
English
ECONOMIA - DEFAP
33
2019/2020
(2022). Essays on empirical macroeconomics. (Tesi di dottorato, Università degli Studi di Milano-Bicocca, 2022).
File in questo prodotto:
File Dimensione Formato  
phd_unimib_833525.pdf

accesso aperto

Descrizione: Tesi
Tipologia di allegato: Doctoral thesis
Dimensione 3.67 MB
Formato Adobe PDF
3.67 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/359807
Citazioni
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
Social impact