29 results on '"Danilo Leiva-Leon"'
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2. Do Inflation Expectations Improve Model-Based Inflation Forecasts?
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Jan-Oliver Menz, Danilo Leiva-Leon, and Marta Banbura
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Inflation ,History ,Polymers and Plastics ,Headline inflation ,media_common.quotation_subject ,Financial market ,Univariate ,Discount points ,Industrial and Manufacturing Engineering ,Bayesian vector autoregression ,Econometrics ,Economics ,Range (statistics) ,Business and International Management ,Phillips curve ,media_common - Abstract
Those of professional forecasters do. For a wide range of time series models for the euro area and its member states we find a higher average forecast accuracy of models that incorporate information on inflation expectations from the ECB’s SPF and Consensus Economics compared to their counterparts that do not. The gains in forecast accuracy from incorporating inflation expectations are typically not large but significant in some periods. Both short- and long-term expectations provide useful information. By contrast, incorporating expectations derived from financial market prices or those of firms and households does not lead to systematic improvements in forecast performance. Individual models we consider are typically better than univariate benchmarks but for the euro area the professional forecasters are more accurate, especially in recent years (not always for the countries). The analysis is undertaken for headline inflation and inflation excluding energy and food and both point and density forecast are evaluated using real-time data vintages over 2001-2019.
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- 2021
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3. Endogenous time variation in vector autoregressions
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Danilo Leiva-Leon and Luis Uzeda
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Estimation ,Economics and Econometrics ,Bayesian probability ,Monetary policy ,Econometrics ,Economics ,State space ,Endogeneity ,Variation (game tree) ,Scenario analysis ,Parametrization ,Social Sciences (miscellaneous) - Abstract
We introduce a new class of time-varying parameter vector autoregressions (TVP-VARs) where the identified structural innovations are allowed to influence the dynamics of the coefficients in these models. An estimation algorithm and a parameterization conducive to model comparison are also provided. We apply our framework to the U.S. economy. Scenario analysis suggests that once accounting for the influence of structural shocks on the autoregressive coefficients, the effects of monetary policy on economic activity are larger and more persistent than in an otherwise standard TVP-VAR. Our results also indicate that cost-push shocks play a prominent role in understanding historical changes in inflation-gap persistence.
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- 2021
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4. Tracking Weekly State-Level Economic Conditions
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Christiane Baumeister, Danilo Leiva-Leon, and Eric R. Sims
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History ,Economics and Econometrics ,Polymers and Plastics ,media_common.quotation_subject ,Aggregate (data warehouse) ,Economic collapse ,Recession ,Industrial and Manufacturing Engineering ,Paycheck ,Multiple time dimensions ,Dynamic factor ,Business cycle ,Economics ,Econometrics ,Preprint ,Business and International Management ,Social Sciences (miscellaneous) ,media_common - Abstract
In this paper, we develop a novel dataset of weekly economic conditions indices for the 50 U.S. states going back to 1987 based on mixed-frequency dynamic factor models with weekly, monthly, and quarterly variables that cover multiple dimensions of state economies. We show that there is considerable heterogeneity in the length, depth, and timing of business cycles across individual states. We assess the role of states in national recessions and propose an aggregate indicator that allows us to gauge the overall weakness of the U.S. economy. We also illustrate the usefulness of these state-level indices for quantifying the main forces contributing to the economic collapse caused by the COVID-19 pandemic and for evaluating the effectiveness of federal economic policies like the Paycheck Protection Program.
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- 2021
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5. Tracking weekly state-level economic conditions
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Danilo Leiva-Leon, Christiane Baumeister, and Eric R. Sims
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Paycheck ,State (polity) ,Multiple time dimensions ,Dynamic factor ,media_common.quotation_subject ,Econometrics ,Economics ,Business cycle ,Public policy ,Economic collapse ,Recession ,media_common - Abstract
In this paper, we develop a novel dataset of weekly economic conditions indices for the 50 U.S. states going back to 1987 based on mixed-frequency dynamic factor models with weekly, monthly, and quarterly variables that cover multiple dimensions of state economies. We show that there is considerable heterogeneity in the length, depth, and timing of business cycles across individual states. We assess the role of states in national recessions and propose an aggregate indicator that allows us to gauge the overall weakness of the U.S. economy. We also illustrate the usefulness of these state-level indices for quantifying the main forces contributing to the economic collapse caused by the COVID-19 pandemic and for evaluating the effectiveness of federal economic policies like the Paycheck Protection Program.
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- 2021
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6. Fluctuations in global output volatility
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Danilo Leiva-Leon and Lorenzo Ductor
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Economics and Econometrics ,Model uncertainty ,Output volatility ,Economics ,Trade ,Developing country ,Monetary economics ,Volatility (finance) ,Finance ,Factor model - Abstract
We thank the editor Mark M. Spiegel and two anonymous referees for excellent useful comments and suggestions that helped to improve this article. We would like to thank Maximo Camacho, Luciano Campos, Alessandro Galessi, Domenico Giannone, Daryna Grechyna, Carlos Thomas, Gabriel Perez-Quiros, Iryna Sikora, Francesco Zanetti and the participants at the 2018 ASSA meetings, the Econometric Society meeting of Latin American, the International Association for Applied Econometrics Conference, the VIII Zaragoza Workshop on Time Series Econometrics, and at the internal seminar series of the Banco de Espana for helpful comments and suggestions. The views expressed in this paper are those of the authors and are in no way the responsibility of the Banco de Espana or Eurosystem. Project PID2019-111708GA-I00 financed by MCIN/AEI/10.13039/501100011033., In this paper, we dissect the time-varying output volatility of the main world economies to study its dynamics, spillovers, and determinants, from a global perspective. Our analysis relies on a hierarchical volatility factor model and Bayesian model averaging. We show that the increasing comovement observed in international macroeconomic volatility is substantially larger in developing than in developed countries. Instead, developed countries have exhibited more asymmetric volatility shocks than developing countries in recent times. We also show that, although the downward trend in global volatility is related with increasing trade, idiosyncratic changes in volatility are highly influenced by domestic monetary policies. However, due to the declining role played by these idiosyncratic components over time, policymakers currently face greater constraints when it comes to stabilizing output fluctuations., Banco de Espana or Eurosystem PID2019-111708GA-I00 MCIN/AEI/10.13039/501100011033
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- 2022
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7. THE PROPAGATION OF INDUSTRIAL BUSINESS CYCLES
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Danilo Leiva-Leon and Maximo Camacho
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Economics and Econometrics ,symbols.namesake ,Multivariate statistics ,Transmission (telecommunications) ,0502 economics and business ,05 social sciences ,Economics ,Business cycle ,symbols ,Econometrics ,050207 economics ,050205 econometrics ,Gibbs sampling - Abstract
This paper examines the evolution of the distribution of industry-specific business cycle linkages, which are modeled through a multivariate Markov-switching model and estimated by Gibbs sampling. Using nonparametric density estimation approaches, we find that the number and location of modes in the distribution of industrial dissimilarities change over the business cycle. There is a relatively stable trimodal pattern during expansionary and recessionary phases characterized by highly, moderately, and lowly synchronized industries. However, during phase changes, the density mass spreads from moderately synchronized industries to lowly synchronized industries. This agrees with a sequential transmission of the industrial business cycle dynamics.
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- 2017
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8. Measuring Business Cycles Intra-Synchronization in US: A Regime-switching Interdependence Framework
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Danilo Leiva-Leon
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Statistics and Probability ,Economics and Econometrics ,Independent business ,Reliability (computer networking) ,Monte Carlo method ,Aggregate (data warehouse) ,05 social sciences ,Regime switching ,Economy ,Synchronization (computer science) ,0502 economics and business ,Business cycle ,Econometrics ,Economics ,050207 economics ,Statistics, Probability and Uncertainty ,Humanities ,Social Sciences (miscellaneous) ,Network analysis ,050205 econometrics - Abstract
Este articulo propone un modelo econometrico de regimenes markovianos para identificar endogenamente periodos en los que las economias tienden a experimentar fases del ciclo economico de manera sincronizada e independiente. La fiabilidad del modelo econometrico se ha validado con datos simulados utilizando experimentos de Monte-carlo. El modelo se aplica para identificar cambios en la sincronizacion regional de Estados Unidos (EEUU). Los resultados indican la presencia de cambios significativos en los patrones ciclicos de afiliacion de los estados de EEUU, y muestran que, cuanto mas similares son las estructuras economicas de los estados, mayor es la correlacion entre sus ciclos economicos. Adicionalmente, un analisis de redes, basado en las medidas de sincronizacion estimadas, revela un cambio en la propagacion de choques contractivos a traves de estados, lo que sugiere que EEUU se ha sincronizado internamente con mayor intensidad desde principios de los anos noventa.
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- 2017
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9. Macro-financial Interactions in a Changing World
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Danilo Leiva-Leon and Eddie Gerba
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Finance ,Spillover effect ,business.industry ,Economics ,Macro ,business ,Measure (mathematics) ,Sign (mathematics) ,Factor analysis ,Financial sector - Abstract
We measure the time-varying strength of macro-financial linkages within and across the US and euro area economies by employing a large set of information for each region. In doing so, we rely on factor models with drifting parameters where real and financial cycles are extracted, and shocks are identified via sign and exclusion restrictions. The main results show that the euro area is disproportionately more sensitive to shocks in the US macroeconomy and financial sector, resulting in an asymmetric cross-border spillover pattern between the two economies. Moreover, while macro-financial interactions have steadily increased in the euro area since the late 1980s, they have oscillated in the US, exhibiting very long cycles of macro-financial interdependence.
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- 2020
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10. Markov-switching three-pass regression filter
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Danilo Leiva-Leon, Pierre Guérin, and Massimiliano Marcellino
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Statistics and Probability ,Economics and Econometrics ,Markov chain ,Computer science ,05 social sciences ,computer.software_genre ,01 natural sciences ,Regression ,MARKOV-SWITCHING ,010104 statistics & probability ,FACTOR MODEL ,FORECASTING ,Filter (video) ,0502 economics and business ,Data mining ,0101 mathematics ,Statistics, Probability and Uncertainty ,computer ,Algorithm ,Social Sciences (miscellaneous) ,050205 econometrics ,Factor analysis - Abstract
We introduce a new approach for the estimation of high-dimensional factor models with regime-switching factor loadings by extending the linear three-pass regression filter to settings where paramet...
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- 2020
11. Real-time Weakness of the Global Economy: A First Assessment of the Coronavirus Crisis
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Danilo Leiva-Leon, Gabriel Perez-Quiros, and Eyno Rots
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Weakness ,Index (economics) ,World economy ,Economy ,Economics ,Business cycle ,medicine ,medicine.symptom ,Construct (philosophy) ,Emerging markets ,China ,Factor analysis - Abstract
We propose an empirical framework to measure the degree of weakness of the global economy in real-time. It relies on nonlinear factor models designed to infer recessionary episodes of heterogeneous deepness, and fitted to the largest advanced economies (U.S., Euro Area, Japan, U.K., Canada and Australia) and emerging markets (China, India, Russia, Brazil, Mexico and South Africa). Based on such inferences, we construct a Global Weakness Index that has three main features. First, it can be updated as soon as new regional data is released, as we show by measuring the economic effects of coronavirus. Second, it provides a consistent narrative of the main regional contributors of world economy's weakness. Third, it allows to perform robust risk assessments based on the probability that the level of global weakness would exceed a certain threshold of interest in every period of time.
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- 2020
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12. Increasing linkages among European regions. The role of sectoral composition
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María Dolores Gadea-Rivas, Danilo Leiva-Leon, and Ana Gómez-Loscos
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Economics and Econometrics ,050208 finance ,Ile de france ,0502 economics and business ,05 social sciences ,Business cycle ,Economics ,Economic geography ,Regime switching ,050207 economics ,Composition (language) ,Great recession - Abstract
This paper analyses changes in economic regional interlinkages in Europe over time and investigates the factors that could explain the dynamics of these changes. Our four main findings are the following: (i) we detect a significant surge in regional synchronisation after the Great Recession; (ii) we identify the regions most interrelated with the rest of Europe, namely, Ile de France, Inner London and Lombardia; (iii) we find that sectoral composition explains regional synchronisation in Europe, mainly after the Great Recession and (iv) we document that sectoral composition has important implications for aggregate economic fluctuations, in particular, that similarities in services-related sectors across regions explain a nonlinear relationship between sectoral composition and regional business cycle synchronisation. We also propose a new method to measure time-varying synchronisation in small samples that combines regime-switching models and dynamic model averaging.
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- 2019
13. Dynamics of global business cycle interdependence
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Danilo Leiva-Leon and Lorenzo Ductor
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Economics and Econometrics ,Global business ,05 social sciences ,Moderation ,Business cycles ,Model uncertainty ,0502 economics and business ,Markov-switching ,Business cycle ,Economics ,Network analysis ,Economic geography ,050207 economics ,Emerging market economies ,Finance ,050205 econometrics - Abstract
In this paper, we provide a comprehensive analysis of the time-varying interdependence among the economic cycles of the major world economies during the post-Great Moderation period. We document a significant increase in the global business cycle interdependence occurred in the early 2000s. Such increase is mainly attributed to the emerging market economies, since their business cycles became more synchronized with the rest of the world around that time. Moreover, we find that the increase in global interdependence is highly related to decreasing differences in sectoral composition among countries.
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- 2016
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14. Real-time nowcasting of nominal GDP with structural breaks
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William A. Barnett, Danilo Leiva-Leon, and Marcelle Chauvet
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Real income ,Inflation ,Economics and Econometrics ,Applied Mathematics ,media_common.quotation_subject ,05 social sciences ,Univariate ,Kalman filter ,Missing data ,Interest rate ,Dynamic factor ,0502 economics and business ,Econometrics ,Economics ,Divisia monetary aggregates index ,050207 economics ,050205 econometrics ,media_common - Abstract
This paper provides early assessments of current U.S. Nominal GDP growth, which has been considered as a potential new monetary policy target. The nowcasts are computed using the exact amount of information that policy makers have available at the time predictions are made. However, real time information arrives at different frequencies and asynchronously, which poses the challenge of mixed frequencies, missing data, and ragged edges. This paper proposes a multivariate state space model that not only takes into account asynchronous information inflow it also allows for potential parameter instability (DYMIBREAK). We use small scale confirmatory factor analysis in which the candidate variables are selected based on their ability to forecast nominal GDP. The model is fully estimated in one step using a nonlinear Kalman filter, which is applied to obtain simultaneously both optimal inferences on the dynamic factor and parameters. Differently from principal component analysis, the proposed factor model captures the comovement rather than the variance underlying the variables. We compare the predictive ability of the model with other univariate and multivariate specifications. The results indicate that the proposed model containing information on real economic activity, inflation, interest rates, and Divisia monetary aggregates produces the most accurate real time nowcasts of nominal GDP growth.
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- 2016
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15. Exchange Rate Shocks and Inflation Comovement in the Euro Area
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Jaime Martinez-Martin, Eva Ortega, and Danilo Leiva-Leon
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Inflation ,Exchange rate ,media_common.quotation_subject ,Core component ,Headline inflation ,Economics ,Monetary economics ,media_common ,Lower degree - Abstract
This paper decomposes the time-varying effect of exogenous exchange rate shocks on euro area countries inflation into country-specific (idiosyncratic) and region-wide (common) components. To do so, we propose a flexible empirical framework that is based on dynamic factor models subject to drifting parameters and exogenous information. We show that exogenous shocks to the euro/USD account for over 50% of the nominal euro/USD exchange rate fluctuations in more than 1/3 of the quarters over the past six years – especially in turning points periods. Our main results indicate that headline inflation in euro area countries, and in particular its energy-related component, has significantly become more affected by these exogenous exchange rate shocks since the early 2010s, in particular, for the largest economies of the region. While such increasing sensitivity relies solely on a sustained surge in the degree of comovement for headline inflation, it is also based on a higher region-wide effect of the shocks for the case of energy inflation. Instead, purely exogenous exchange rate shocks do not seem to have a significant effect on the core component of headline inflation, which also displays a lower degree of comovement across euro area countries.
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- 2019
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16. An Application of Dynamic Factor Models to Nowcast Regional Economic Activity in Spain
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Javier J. Pérez, Alberto Urtasun, María Gil, and Danilo Leiva-Leon
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Estimation ,Nowcasting ,Order (exchange) ,Computer science ,Dynamic factor ,Bayesian probability ,Econometrics ,Missing data ,Relevant information - Abstract
The goal of this paper is to propose a model to produce nowcasts of GDP growth of Spanish regions, by means of dynamic factor models. This framework is capable to incorporate in a parsimonious way the relevant information available at the time that each forecast is made. We employ a Bayesian perspective to provide robust estimation of all the ingredients involved in the model. Accordingly, we introduce the Bayesian Factor model for Regions (BayFaR), which allows for the inclusion of missing data and combines quarterly data on regional real output growth (taken from the database of the AIReF and from the individual regional statistics institutes, when available) and monthly information associated to indicators of regional real activity. We apply the BayFaR to nowcast the GDP growth of the four largest regions of Spain, and illustrate the real-time nowcasting performance of the proposed framework for each case. We also apply the model to nowcast Spanish GDP in order to be able to assess the relative growth of each region.
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- 2019
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17. Mapping China’s Time-Varying House Price Landscape
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Danilo Leiva-Leon, Andrew Tsang, and Michael Funke
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House price ,education.field_of_study ,Synchronization (computer science) ,Population ,Econometrics ,Sorting ,Economics ,National level ,China ,education ,Empirical evidence ,Air quality index - Abstract
The recent increase in China’s house prices at the national level masks tremendous variation at the city level – a feature largely overlooked in the macroprudential literature. This paper measures the evolving heterogeneity in China’s house price dynamics across 70 major cities and assesses its relationship with housing market characteristics. We gauge the heterogeneity of house price dynamics using a novel regime-switching modelling approach to estimate the time-varying patterns of China’s city-level housing price synchronization. The estimates indicate an increasing synchronization leading up to 2015, and a decoupling pattern thereafter, which is associated to the heterogeneous strength of regional macroprudential policies. After sorting city-level housing prices into four clusters sharing similar cyclical features, we document high synchronization within clusters, but low synchronization among them. The empirical evidence suggests that differentials in the growth of population, income, and air quality are relevant explanatory factors of housing price synchronization among cities.
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- 2019
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18. Mapping China’s time-varying house price landscape
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Andrew Tsang, Danilo Leiva-Leon, and Michael Funke
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Economics and Econometrics ,education.field_of_study ,05 social sciences ,Population ,Sorting ,Urban Studies ,House price ,0502 economics and business ,Synchronization (computer science) ,Econometrics ,Economics ,National level ,050207 economics ,Empirical evidence ,China ,education ,Air quality index ,050205 econometrics - Abstract
The recent increase in China’s house prices at the national level masks tremendous variation at the city level – a feature largely overlooked in the macroprudential literature. This paper measures the evolving heterogeneity in China’s house price dynamics across 70 major cities and assesses its relationship with housing market characteristics. We gauge the heterogeneity of house price dynamics using a novel regime-switching modelling approach to estimate the time-varying patterns of China’s city-level housing price synchronization. The estimates indicate an increasing synchronization leading up to 2015, and a decoupling pattern thereafter, which is associated to the heterogeneous strength of regional macroprudential policies. After sorting city-level housing prices into four clusters sharing similar cyclical features, we document high synchronization within clusters, but low synchronization among them. The empirical evidence suggests that differentials in the growth of population, income, and air quality are relevant explanatory factors of housing price synchronization among cities.
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- 2019
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19. Monetary Policy, Stock Market and Sectoral Comovement
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Danilo Leiva-Leon and Pierre Guérin
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Autoregressive model ,Monetary policy ,Economics ,Stock market ,Monetary economics ,Stock return ,Stock (geology) ,Factor analysis - Abstract
This paper evaluates the role that sectoral comovement plays in the propagation of monetary policy shocks on the stock market. In doing so, we introduce a factor-augmented vector autoregressive model with heterogeneous regime-switching factor loadings, denoted as MS2-FAVAR, that allows us to jointly assess (i) potential changes in the degree of comovement between each sector-specific stock return and the aggregate stock market as well as (ii) the propagation of monetary policy shocks taking into account such changes in comovement. We find that the efects of monetary policy shocks on stock returns are substantially amplied when industries experience a stronger degree of comovement, suggesting that a more interconnected stock market is more prone to the propagation of monetary policy shocks. The MS2-FAVAR model is also well-suited to perform a network analysis to characterize linkages in large datasets.
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- 2017
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20. Markov-Switching Three-Pass Regression Filter
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Pierre Guérin, Danilo Leiva-Leon, and Massimiliano Marcellino
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symbols.namesake ,Computational complexity theory ,Markov chain ,Filter (video) ,Computer science ,Monte Carlo method ,symbols ,Inference ,Markov process ,Algorithm ,Regression ,Factor analysis - Abstract
We introduce a new approach for the estimation of high-dimensional factor models with regime-switching factor loadings by extending the linear three-pass regression filter to settings where parameters can vary according to Markov processes. The new method, denoted as Markov-switching three-pass regression filter (MS-3PRF), is suitable for data sets with large cross-sectional dimensions, since estimation and inference are straightforward, as opposed to existing regime-switching factor models where computational complexity limits applicability to few variables. In a Monte Carlo experiment, we study the finite sample properties of the MS-3PRF and find that it performs favorably compared with alternative modelling approaches whenever there is structural instability in factor loadings. For empirical applications, we consider forecasting economic activity and bilateral exchange rates, finding that the MS-3PRF approach is competitive in both cases.
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- 2017
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21. The Evolution of Regional Economic Interlinkages in Europe
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Danilo Leiva-Leon, María Dolores Gadea Rivas, and Ana Gómez-Loscos
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Ile de france ,Economy ,Synchronization (computer science) ,Business cycle ,Economics ,Frequency data ,media_common.cataloged_instance ,Economic geography ,Regime switching ,European union ,Main channel ,Great recession ,media_common - Abstract
This paper studies the dynamics of the propagation of regional business cycle shocks in Europe and uncovers new features of its underlying mechanisms. To address the lack of high frequency data at the regional level, we propose a new method to measure time-varying synchronization in small samples that combines regime-switching models and dynamic model averaging. The results indicate that: (i) in just two years, the Great Recession synchronized Europe twice as much as the European Union integration process did over several decades; (ii) Ile de France is the region acting as the main channel for the transmission of business cycle shocks in Europe; followed by Inner London and Lombardia; and (iii) we identify a nonlinear relationship between sectoral composition and regional synchronization, which was amplified in the wake of the Great Recession. Similarities in services sectors are primarily responsible for this nonlinear relationship.
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- 2017
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22. Mapping China's Time-Varying House Price Landscape
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Michael Funke, Danilo Leiva-Leon, and Andrew Tsang
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- 2017
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23. Dynamics of global business cycles interdependence
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Danilo Leiva-Leon and Lorenzo Ductor
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Global business ,Dynamics (music) ,business.industry ,Agriculture ,Business cycle ,Economics ,International trade ,Economic geography ,Emerging market economies ,business ,Moderation - Abstract
In this paper, we rely on regime-switching models to provide a comprehensive analysis of the time-varying interdependence among the economic cycles of the major world economies during the post-Great moderation period. We document a structural increase in the global business cycles interdependence occurred in the early 2000s. A clustering analysis reveals that such increase is mainly attributed to the emerging market economies, since their business cycles became more synchronized with the rest of the world around that time. Moreover, we find that the break in global interdependence can be explained by decreasing differences in sectoral composition among countries, specifically in the agricultural component.
- Published
- 2016
24. Country Shocks, Monetary Policy Expectations and ECB Decisions. A Dynamic Non-linear Approach
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Gabriel Perez-Quiros, Maximo Camacho, and Danilo Leiva-Leon
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Inflation ,Supply shock ,media_common.quotation_subject ,05 social sciences ,Monetary policy ,Monetary economics ,Two stages ,Shock (economics) ,Demand shock ,0502 economics and business ,Economics ,Market expectations ,050207 economics ,050205 econometrics ,media_common - Abstract
Previous studies have shown that the effectiveness of monetary policy depends, to a large extent, on the market expectations of its future actions. This paper proposes an econometric framework to address the effect of the current state of the economy on monetary policy expectations. Specifically, we study the effect of contractionary (or expansionary) demand (or supply) shocks hitting the euro area countries on the expectations of the ECB's monetary policy in two stages. In the first stage, we construct indexes of real activity and inflation dynamics for each country, based on soft and hard indicators. In the second stage, we use those indexes to provide assessments on the type of aggregate shock hitting each country and assess its effect on monetary policy expectations at different horizons. Our results indicate that expectations are responsive to aggregate contractionary shocks, but not to expansionary shocks. Particularly, contractionary demand shocks have a negative effect on short-term monetary policy expectations, while contractionary supply shocks have negative effect on medium- and long-term expectations. Moreover, shocks to different economies do not have significantly different effects on expectations, although some differences across countries arise.
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- 2016
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25. Model Averaging in Markov-Switching Models: Predicting National Recessions with Regional Data
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Pierre Guérin and Danilo Leiva-Leon
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Scheme (programming language) ,Economics and Econometrics ,Nowcasting ,Computer science ,Survey of Professional Forecasters ,media_common.quotation_subject ,Bayesian probability ,Outcome (game theory) ,Recession ,0502 economics and business ,Economics ,ddc:330 ,Econometrics ,Business cycle ,050207 economics ,C53 ,Business fluctuations and cycles ,Physics::Atmospheric and Oceanic Physics ,050205 econometrics ,E32 ,computer.programming_language ,media_common ,Markov chain ,E37 ,jel:C53 ,05 social sciences ,jel:E32 ,jel:E37 ,Markov-switching ,Forecasting ,Business Cycles ,Forecast combination ,Weighting ,Econometric and statistical methods ,computer ,Finance ,Simulation methods - Abstract
This paper introduces new weighting schemes for model averaging when one is interested in combining discrete forecasts from competing Markov-switching models. In particular, we extend two existing classes of combination schemes – Bayesian (static) model averaging and dynamic model averaging – so as to explicitly reflect the objective of forecasting a discrete outcome. Both simulation and empirical exercises show that our new combination schemes outperform competing combination schemes in terms of forecasting accuracy. In the empirical application, we estimate and forecast U.S. business cycle turning points with state-level employment data. We find that forecasts obtained with our best combination scheme provide timely updates of the U.S. business cycles., Les auteurs présentent de nouvelles méthodes de pondération pour la combinaison de prévisions de variables discrètes issues de différents modèles de Markov à changement de régime. Plus particulièrement, ils étendent deux classes existantes de méthodes de combinaison – combinaison de prévisions établies au moyen de modèles bayésiens (statiques) et combinaison dynamique de prévisions – de manière à correspondre explicitement à l’objectif assigné à l’exercice de prévision d’une variable discrète. Les simulations et l’application empirique montrent qu’en ce qui a trait à l’exactitude des prévisions, les nouvelles méthodes de combinaison surclassent les méthodes de combinaison concurrentes. S’agissant de l’application empirique, les auteurs estiment et prévoient les points de retournement du cycle de l’économie américaine à l’aide de données sur l’emploi provenant des États. Ils constatent que les prévisions obtenues à partir de leur meilleure méthode de combinaison fournissent des renseignements en temps opportun sur les cycles économiques aux États-Unis.
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- 2015
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26. The Propagation of Industrial Business Cycles
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Danilo Leiva-Leon and Maximo Camacho
- Subjects
symbols.namesake ,Commerce ,Transmission (telecommunications) ,business.industry ,Econometrics ,Phase (waves) ,symbols ,Economics ,Business cycle ,Distribution (economics) ,business ,Nonparametric density estimation ,Gibbs sampling - Abstract
This paper examines the business cycle linkages that propagate industry-specific business cycle shocks throughout the economy in a way that (sometimes) generates aggregated cycles. The transmission of sectoral business cycles is modelled through a multivariate Markov-switching model, which is estimated by Gibbs sampling. Using nonparametric density estimation approaches, we find that the number and location of modes in the distribution of industrial dissimilarities change over the business cycle. There is a relatively stable trimodal pattern during expansionary and recessionary phases characterized by highly, moderately and lowly synchronized industries. However, during phase changes, the density mass spreads from moderately synchronized industries to lowly synchronized industries. This agrees with a sequential transmission of the industrial business cycle dynamics.
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- 2014
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27. Real vs. nominal cycles: a multistate Markov-switching bi-factor approach
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Danilo Leiva-Leon
- Subjects
Inflation ,Economics and Econometrics ,Supply shock ,Markov chain ,media_common.quotation_subject ,jel:E32 ,jel:C22 ,Deflation ,Recession ,Shock (economics) ,jel:E27 ,Demand shock ,Econometrics ,Business cycle ,Economics ,Business Cycles, Inflation Cycles, Housing Price Cycles, Dynamics Factors, Markov-Switching ,Social Sciences (miscellaneous) ,Analysis ,media_common - Abstract
This paper proposes a probabilistic model based on comovements and nonlinearities useful to assess the type of shock affecting each phase of the business cycle. By providing simultaneous inferences on the phases of real activity and inflation cycles, contractionary episodes are dated and categorized into demand, supply and mix recessions. The impact of shocks originated in the housing market over the business cycle is also assessed, finding that recessions are usually accompanied by housing deflationary pressures, while expansions are mainly influenced by housing demand shocks, with the only exception occurred during the period surrounding the “Great Recession,” affected by expansionary housing supply shocks.
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- 2014
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28. Real-Time Nowcasting of Nominal GDP Under Structural Breaks
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Danilo Leiva-Leon, William Barnett, and Marcelle Chauvet
- Subjects
Inflation ,Nowcasting ,Computer science ,media_common.quotation_subject ,Dynamic factor ,Econometrics ,Univariate ,Divisia monetary aggregates index ,Kalman filter ,Missing data ,media_common ,Interest rate - Abstract
This paper provides a framework for the early assessment of current U.S. nominal GDP growth, which has been considered a potential new monetary policy target. The nowcasts are computed using the exact amount of information that policy-makers have available at the time predictions are made. However, real-time information arrives at different frequencies and asynchronously, which poses challenges of mixed frequencies, missing data and ragged edges. This paper proposes a multivariate state-space model that not only takes into account asynchronous information inflow, but also allows for potential parameter instability. We use small-scale confirmatory factor analysis in which the candidate variables are selected based on their ability to forecast nominal GDP. The model is fully estimated in one step using a non-linear Kalman filter, which is applied to obtain optimal inferences simultaneously on both the dynamic factor and parameters. In contrast to principal component analysis, the proposed factor model captures the comovement rather than the variance underlying the variables. We compare the predictive ability of the model with other univariate and multivariate specifications. The results indicate that the proposed model containing information on real economic activity, inflation, interest rates and Divisia monetary aggregates produces the most accurate realtime nowcasts of nominal GDP growth.
- Published
- 2014
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29. A New Approach to Infer Changes in the Synchronization of Business Cycle Phases
- Author
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Danilo Leiva-Leon
- Subjects
Independent business ,media_common.quotation_subject ,Aggregate (data warehouse) ,Propagation pattern ,jel:C45 ,jel:E32 ,jel:C32 ,Business Cycles, Markov-Switching, Network Analysis ,Interdependence ,Synchronization (computer science) ,Econometrics ,Business cycle ,Economics ,media_common ,Network analysis - Abstract
This paper proposes a Markov-switching framework to endogenously identify the following: (1) regimes where economies synchronously enter recessionary and expansionary phases; and (2) regimes where economies are unsynchronized, essentially following independent business cycles. The reliability of the framework to track changes in synchronization is corroborated with Monte Carlo experiments. An application to the case of U.S. states reports substantial changes over time in the cyclical affiliation patterns of states. Moreover, a network analysis discloses a change in the propagation pattern of aggregate contractionary shocks across states, suggesting that regional economies in the United States have become more interdependent since the early 1990s.
- Published
- 2013
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