House Prices and the Stance of Monetary Policy

Jarociński, Marek; Smets, Frank

doi:10.20955/r.90.339-366

Cited by 172 publications

(173 citation statements)

References 21 publications

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“…Notable examples of the latter are the stress tests recently conducted in the US and the euro area in order to assess the vulnerability of their banking systems. For recent examples of conditional forecasts, see Lenza, Pill, and Reichlin (2010); Giannone, Lenza, and Reichlin (2010); Jarociński and Smets (2008); Bloor and Matheson (2011) ;Giannone, Lenza, Pill, and Reichlin (2012); Stock and Watson (2012a); Giannone, Lenza, Momferatou, and Onorante (2014). Recently, Clark and McCracken (2014) propose and evaluate a range of tests of predictive ability for conditional forecasts from estimated models.…”

Section: Introductionmentioning

confidence: 99%

Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections

Banbura

Giannone

Lenza

2015

International Journal of Forecasting

121

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In 2014 all ECB publications feature a motif taken from the €20 banknote.NOTE: This Working Paper should not be reported as representing the views of the European Central Bank (ECB). The views expressed are those of the authors and do not necessarily refl ect those of the ECB. AbstractThis paper describes an algorithm to compute the distribution of conditional forecasts, i.e. projections of a set of variables of interest on future paths of some other variables, in dynamic systems. The algorithm is based on Kalman filtering methods and is computationally viable for large models that can be cast in a linear state space representation. We build large vector autoregressions (VARs) and a large dynamic factor model (DFM) for a quarterly data set of 26 euro area macroeconomic and financial indicators. Both approaches deliver similar forecasts and scenario assessments. In addition, conditional forecasts shed light on the stability of the dynamic relationships in the euro area during the recent episodes of financial turmoil and indicate that only a small number of sources drive the bulk of the fluctuations in the euro area economy. JEL Classification: C11, C13, C33, C53Keywords: Vector Autoregression, Bayesian Shrinkage, Dynamic Factor Model, Conditional Forecast, Large Cross-Sections.ECB Working Paper 1733, September 2014 1 Non-technical summaryVector autoregressions (VARs) are very flexible and general models and provide a reliable empirical benchmark for alternative econometric representations such as dynamic stochastic general equilibrium (DSGE) models, which are more grounded in theory but, at the same time, impose more structure on the data.Recent literature has shown that VARs are viable tools also for large sets of data. In this paper we construct a large VAR for the euro area and we apply it to unconditional forecasting as well as for conditional forecasts and scenarios. These, along with structural analysis (assessing the effects of structural shocks), have been the main applications of VARs. Whereas large VARs have been used for unconditional forecasting and structural analysis, limited attention has been devoted as yet to conditional forecasting. This is because popular algorithms for deriving conditional forecasts have been computationally challenging for large data sets. We overcome this problem by computing the conditional forecasts recursively using Kalman filtering techniques.Conditional forecasts and, in particular, scenarios are projections of a set of variables of interest on future paths of some other variables. This is in contrast to unconditional forecasts, where no knowledge of the future path of any variables is assumed. The prior knowledge, albeit imperfect, of the future evolution of some economic variables may carry information for the outlook of other variables. For example, future fiscal packages would affect the future evolution of economic activity and, thus, might provide important off-model information. Moreover, it may be of interest to assess the impact of specific future events on a ...

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Section: Introductionmentioning

confidence: 99%

Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections

Banbura

Giannone

Lenza

2015

International Journal of Forecasting

121

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“…In identifying a housing demand shock, we basically follow Jarocinski and Smets (2008) and Musso et al (2011). However, we try to decompose the housing demand shock in two types of separate shocks: a housing preference shock and a population shock.…”

Section: Housing Demand Shocksmentioning

confidence: 99%

“…An key issue is the role and the relative importance of credit supply shocks and standard monetary policy shocks as identified by Musso et al (2011) for the US and the EU. Studies that take into account specific issues of housing are Jarocinski and Smets (2008), Goodhart and Hofmann (2008), Musso et al (2011), as well as Hirata et al (2012).…”

Section: Introductionmentioning

confidence: 99%

Credit cycles and real activity: the Swiss case

Bäurle

Scheufele

2018

Empir Econ

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract This paper analyzes credit supply and demand shocks for the Swiss economy. Using a medium scale BVAR model we are able to take into account various interactions of housing prices, credit supply and demand, interest rates and real activity measures. To identify meaningful economic shocks, we used a combination of zero and sign restrictions. Generally, the empirical analysis implies that the effects of credit supply and demand shocks on real activity are limited, only playing a substantive role in specific episodes. Furthermore, the credit supply shocks (i.e. bank lending shocks and monetary policy shocks) explain a large fraction of housing price and credit fluctuations. Indeed, they tend to be more important for housing prices than housing demand shocks. There is further evidence that shocks related to credit supply, monetary policy shocks dominate bank lending shocks. For Switzerland as a small open economy it turns out to be very crucial to take into account foreign demand which is able to explain a substantial variation in real and financial variables. Terms of use: Documents in EconStor mayJEL classification: C11, C32, E30, E44, E51, E52

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“…In addition, expansionary monetary policy tends to improve the current account balance through a weaker currency that spurs exports and reduces imports. In addition, the historical relationship between monetary policy and home prices indicate that monetary policy could have been responsible for only a small portion of the increase in home prices seen in the early 2000s [6][7][8][9]. One possible reason for monetary policy to have influenced home prices more than historical relationships warrant could be financial innovation [9].…”

Section: Literature Review: Controversiesmentioning

confidence: 99%

An Examination of the Influence of Household Financial Decision Making on the US Housing Market Crisis

Mukerji

Saeed

Tan

2015

Systems

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This paper investigates the impact of what the extant literature has come to view as some of the major causes of the 2007 US housing market crisis. In particular we investigate the hypothesized effect of, lax financial regulations, the "savings glut" that is invested in the US from abroad, government support for increased home ownership, rising homeowners' equity due to the real-estate boom, expansionary monetary policy, and bankruptcy reform. We examine how these hypothesized causes, working through household and institutional level decision-making, based on information availability and incentives, influenced the outcomes in the market for homes. Using a system dynamics model of household finance, we overlay the hypothesized causes chronologically to extrapolate their real-world simultaneous impact and test the hypothesis that they could have together led to the crisis, by simulating and checking against observed data. We find that with the exception of lax financial regulations, each cause by itself provides only a partial explanation of the crisis. Interestingly, the controversial expansionary monetary policy of the Federal Reserve, blamed by some for fueling the crisis, actually prevents the housing market boom from becoming too large. However on the downside, it discourages household savings and causes the fall in home prices to be deeper, due to weak household finances that result from low savings. We confront our model's assumptions and outcomes with US economic data. We find our model assumptions are justified and simulation results are strongly supported by the data. OPEN ACCESSSystems 2015, 3 379

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House Prices and the Stance of Monetary Policy

Cited by 172 publications

References 21 publications

Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections

Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections

Credit cycles and real activity: the Swiss case

An Examination of the Influence of Household Financial Decision Making on the US Housing Market Crisis

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