One Money, Many Markets

2021 ◽  
Author(s):  
Giancarlo Corsetti ◽  
Joao B Duarte ◽  
Samuel Mann
Keyword(s):  
High Frequency ◽  
Factor Model ◽  
Wage Rigidity ◽  
Dynamic Factor ◽  
Dynamic Factor Model ◽  
Monetary Shocks ◽  
Financial Variables ◽  
Consumer Prices ◽  
Study Heterogeneity ◽  
Frequency Identification

Abstract We study heterogeneity in the transmission of monetary shocks across euro-area countries using a dynamic factor model and high-frequency identification. Deploying a novel methodology to assess the degree of heterogeneity, we find it to be low in financial variables and output but significant in consumption, consumer prices, and variables related to local housing and labour markets. We show that a large proportion of the variation in the responses to monetary shocks can be accounted for by differences in some characteristics of these markets across EA member countries: the share of adjustable mortgage contracts, homeownership rates, shares of hand-to-mouth and wealthy hand-to-mouth consumers, as well as wage rigidity.

scholarly journals One Money, Many Markets

2020 ◽  
Vol 20 (108) ◽  
Author(s):  
Giancarlo Corsetti ◽  
Joao Duarte ◽  
Samuel Mann
Keyword(s):  
Open Economy ◽  
Factor Model ◽  
Small Open Economy ◽  
Dynamic Factor ◽  
Monetary Shocks ◽  
Financial Variables ◽  
Consumer Prices ◽  
Frequency Identification ◽  
Cross Country ◽  
Adjustable Rate

We study the transmission of monetary shocks across euro-area countries using a dynamic factor model and high-frequency identification. We develop a methodology to assess the degree of heterogeneity, which we find to be low in financial variables and output, but significant in consumption, consumer prices, and variables related to local housing and labor markets. Building a small open economy model featuring a housing sector and calibrating it to Spain, we show that varying the share of adjustable-rate mortgages and loan-to-value ratios explains up to one-third of the cross-country heterogeneity in the responses of output and private consumption.

The more, the better? Forecasting gains from high-frequency housing prices in a Markov-switching dynamic factor model

2021 ◽  
pp. 1-18
Author(s):  
MeiChi Huang
Keyword(s):  
High Frequency ◽  
Factor Model ◽  
Housing Prices ◽  
Markov Switching ◽  
House Price ◽  
Dynamic Factor ◽  
Dynamic Factor Model ◽  
Metropolitan Statistical Area ◽  
Marginal Contributions ◽  
Switching Dynamic

Abstract This paper extracts housing boom-bust cycle signals from metropolitan statistical area (MSA)-level housing prices using a Markov-switching dynamic factor model. To mitigate the estimation bias, it utilizes high-frequency housing prices that follow the methodology of the monthly Case–Shiller house price indices. The housing bust phases specified from weekly and daily housing prices precede those based on monthly prices by approximately 2 years. MSAs with top signal-to-noise ratios offer greater marginal contributions to improvements in forecasting housing cycles than MSAs with bottom ratios for all frequencies. The results highlight the importance of indicator quality and provide evidence against “The more, the better” since incorporating more MSA-level housing prices into housing factors does not guarantee more satisfactory housing cycle forecasts.

scholarly journals On business cycle forecasting

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Huiwen Lai ◽  
Eric C. Y. Ng
Keyword(s):  
Business Cycles ◽  
Factor Model ◽  
Mixed Data ◽  
Dynamic Factor ◽  
Dynamic Factor Model ◽  
Forecast Errors ◽  
Data Sampling ◽  
Target Variable ◽  
Financial Variables ◽  
Mixed Data Sampling

Abstract We develop a recession forecasting framework using a less restrictive target variable and more flexible and inclusive specification than those used in the literature. The target variable captures the occurrence of a recession within a given future period rather than at a specific future point in time (widely used in the literature). The modeling specification combines an autoregressive Logit model capturing the autocorrelation of business cycles, a dynamic factor model encompassing many economic and financial variables, and a mixed data sampling regression incorporating common factors with mixed sampling frequencies. The model generates significantly more accurate forecasts for U.S. recessions with smaller forecast errors and stronger early signals for the turning points of business cycles than those generated by existing models.

Dynamic factor model for network traffic state forecast

2018 ◽  
Vol 118 ◽  
pp. 281-317 ◽  
Author(s):  
Tao Ma ◽  
Zhou Zhou ◽  
Constantinos Antoniou
Keyword(s):  
Network Traffic ◽  
Factor Model ◽  
Dynamic Factor ◽  
Dynamic Factor Model ◽  
Traffic State

scholarly journals Dynamic factor model with infinite‐dimensional factor space: Forecasting

2018 ◽  
Vol 33 (5) ◽  
pp. 625-642 ◽  
Author(s):  
Mario Forni ◽  
Alessandro Giovannelli ◽  
Marco Lippi ◽  
Stefano Soccorsi
Keyword(s):  
Factor Model ◽  
Dynamic Factor ◽  
Dynamic Factor Model ◽  
Factor Space ◽  
Infinite Dimensional

Measuring the Output Gap using Large Datasets

2021 ◽  
pp. 1-45
Author(s):  
Matteo Barigozzi ◽  
Matteo Luciani
Keyword(s):  
Factor Model ◽  
Theoretical Models ◽  
Output Growth ◽  
Output Gap ◽  
Dynamic Factor ◽  
Dynamic Factor Model ◽  
Goods And Services ◽  
Long Run ◽  
Slow Drift ◽  
Macroeconomic Indicators

Abstract We propose a new measure of the output gap based on a dynamic factor model that is estimated on a large number of U.S. macroeconomic indicators and which incorporates relevant stylized facts about macroeconomic data (co-movements, non-stationarity, and the slow drift in long-run output growth over time). We find that, (1) from the mid-1990s to 2008, the U.S. economy operated above its potential; and, (2) in 2018:Q4, the labor market was tighter than the market for goods and services. Because it is mainly data-driven, our measure is a natural complementary tool to the theoretical models used at policy institutions.

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