Limits of Floating Exchange Rates: The Role of Foreign Currency Debt and Import Structure

Towbin, Pascal; Weber, Sebastian

doi:10.5089/9781455219001.001

Cited by 25 publications

(27 citation statements)

References 51 publications

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“…By contrast, small changes might be perceived as random noise and be ignored. To account for these types of nonlinearities, we follow Towbin and Weber (2013) who implement an interacted VAR (iVAR) model. The iVAR model allows for time variation in the reaction of real activity, because the coefficients are allowed to depend on the state of the economy.…”

Section: Macroeconomic Uncertainty and Real Activitymentioning

confidence: 99%

Dimensions of Macroeconomic Uncertainty: A Common Factor Analysis

Henzel

Rengel

2016

Economic Inquiry

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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. Uncertainty about the future course of the economy is a possible driver of aggregate fluctuations. To identify the different dimensions of uncertainty in the macroeconomy we construct a large dataset covering all types of economic uncertainty. We then identify two fundamental factors which account for the common dynamics in this dataset. These factors are interpreted as macroeconomic uncertainty. The first factor mainly captures business cycle uncertainty while the second factor is identified as oil and commodity price uncertainty. While both types of uncertainty foreshadow a decline in output, surprise increases in oil and commodity price uncertainty appear to be more important for fluctuations in real activity. Terms of use: Documents in EconStor mayJEL-Code: C320, C380, E320.

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Section: Macroeconomic Uncertainty and Real Activitymentioning

confidence: 99%

Dimensions of Macroeconomic Uncertainty: A Common Factor Analysis

Henzel

Rengel

2016

Economic Inquiry

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“…Second, we study the role of securitization using the index of de‐jure mortgage‐backed securitization across countries constructed by Hoffmann and Nitschka (). To exploit time variation in this index, we estimate an interacted panel VAR model, as in Towbin and Weber (). The interaction of all variables with the index allows the parameters to vary with the degree of mortgage securitization.…”

Section: Introductionmentioning

confidence: 99%

“…Second, we study the role of securitization using the index of de-jure mortgage-backed securitization across countries constructed by Hoffmann and Nitschka (2008). To exploit time variation in this index, we estimate an interacted panel VAR model, as in Towbin and Weber (2013).…”

Section: Introductionmentioning

confidence: 99%

Capital Inflows, Financial Structure and Housing Booms

Sá

Towbin

Wieladek

2013

Journal of the European Economic Association

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118

110

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We use a panel VAR to study the effect of shocks to capital inflows, which are identified using sign restrictions, on the housing market in OECD countries. To explore how effects of these shocks change with the structure of the mortgage market and the degree of mortgage securitization, we allow the VAR coefficients to vary with mortgage-market characteristics. Our results indicate that capital-inflow shocks have a significant and positive effect on real house prices, real credit available to the private sector, and real residential investment. The responses of these variables are stronger in countries with more developed mortgage markets and in countries where securitization is allowed.

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“…The empirical analysis examines the validity of the theoretical findings by estimating an interacted panel, VAR (IPVAR) model, on 21 U.S. manufacturing industries at the three‐digit NAICS level with quarterly data from 1972:q1 to 2006:q4. Following Towbin and Weber () and Sá, Towbin, and Wieladek (), the model coefficients vary with different openness measures over time, and the time‐varying information in these measures can be fully exploited. The model has two specifications: In the first, the coefficients depend on general trade openness.…”

Section: Introductionmentioning

confidence: 99%

Can trade openness affect the monetary transmission mechanism?

Wen

2019

Rev International Economics

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This paper explores how trade openness influences the monetary transmission mechanism. The theoretical analysis develops an open economy New Keynesian model that features one‐way offshoring from an advanced economy to a less developed one. The model suggests that greater openness to regular trade or to offshoring reduces the effect of monetary policy on domestic economies, although these two influences affect the monetary transmission mechanism via different channels. The empirical section estimates an interacted‐panel VAR model with U.S. industry data and confirms that greater exposure to regular trade or to offshoring significantly reduces the effect of monetary policy.

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Limits of Floating Exchange Rates: The Role of Foreign Currency Debt and Import Structure

Cited by 25 publications

References 51 publications

Dimensions of Macroeconomic Uncertainty: A Common Factor Analysis

Dimensions of Macroeconomic Uncertainty: A Common Factor Analysis

Capital Inflows, Financial Structure and Housing Booms

Can trade openness affect the monetary transmission mechanism?

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