Optimal monetary policy with the cost channel

Ravenna, Federico; Walsh, Carl E.

doi:10.1016/j.jmoneco.2005.01.004

Cited by 409 publications

(532 citation statements)

References 17 publications

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“…Ravenna and Walsh (2006) derive and give supporting empirical evidence for a Phillips curve where an interest rate contributes to firm costs. However, a recurrent theme in discussions about the role of credit markets 1 is that borrowing costs do not give a complete picture, and changes in quantity rationing, when some firms are denied loans, plays an important role.…”

Section: Introductionmentioning

confidence: 79%

“…The household optimization problem is closely related to standard approaches such as Ravenna and Walsh (2006), but the fraction of non-rationed firms affects firm profits received by the household and the aggregate quantity lent by intermediaries. The labor supply relation is standard, but the aggregate quantity of household savings is directly affected by the fraction of quantity rationed firms.…”

Section: Householdsmentioning

confidence: 99%

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Quantity rationing of credit and the Phillips curve

Waters

2013

Journal of Macroeconomics

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Quantity rationing of credit, when some firms are denied loans, has macroeconomic effects not fully captured by measures of borrowing costs. This paper develops a monetary DSGE model with quantity rationing and derives a Phillips Curve relation where inflation dynamics depend on excess unemployment, a risk premium and the fraction of firms receiving financing. Excess unemployment is defined as that which arises from disruptions in credit flows. GMM estimates using data from a survey of bank managers confirms the importance of these variables for inflation dynamics.

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Section: Introductionmentioning

confidence: 79%

Section: Householdsmentioning

confidence: 99%

Quantity rationing of credit and the Phillips curve

Waters

2013

Journal of Macroeconomics

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“…The working capital channel suggests that because firms borrow the cost of production and then use sales proceeds to pay off loans, they are vulnerable to a rise in interest rates, see Barth III and Ramey (2002), Ravenna and Walsh (2006), Christiano, Trabandt, and Walentin (2010). The effect is magnified by a contractionary monetary policy shock that reduces sales and causes an accumulation of inventory costs.…”

Section: Monetary Policy Transmissionmentioning

confidence: 99%

Oil Price Shocks: Bank Size and Firm Size Effects

Smoluk¹

2018

JFE

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This paper uses regional variation to study the propagation of oil price shocks from vector autoregressions. Using data from the lower 48 states, we find strong and distinct asymmetrical patterns in the impulse responses of personal income and housing prices from an oil price shock. Specifically, impulse responses are amplified or dampened depending on the size distributions of banks and firms within a state. More importantly, the small bank and small firm size effects normally associated with the propagation of monetary policy shocks, are shown to propagate oil price shocks. Overall, our results are indicative of multiple transmission channels.JEL Classifications: R11, Q43, E52, G21

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“…Related to the modelling of firm defaults, one way to classify credit risks is to distinguish between microeconomic or idiosyncratic risks, which can be diversified through the law 24 The "cost channels" of monetary policy capture the impact of interest rates and credit conditions on the short-run ability of firms to produce (by investing in working capital). See, for example, Barth and Ramey (2000), Christiano, Eichenbaum, and Evans (2005), Chowdhury, Hoffmann, and Schabert (2006), and Ravenna and Walsh (2006). 25 One explanation for the imperfection in the loan market is the existence of long-term relationships between banks and customers, which are typical for a bank-based financial system as opposed to a market-based financial system.…”

Section: A Selected Review Of the Literaturementioning

confidence: 99%

Business Cycle Effects of Credit Shocks in a DSGE Model with Firm Defaults

Pesaran

2016

SSRN Journal

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This paper proposes a theoretical framework to analyze the relationship between credit shocks, firm defaults and volatility, and to study the impact of credit shocks on business cycle dynamics. Firms are identical ex ante but differ ex post due to different realizations of firm-specific technology shocks, possibly leading to default by some firms. The paper advances a new modelling approach for the analysis of firm defaults and financial intermediation that takes account of the financial implications of such defaults for both households and banks. Results from a calibrated version of the model suggest that, in the steady state, a firm's default probability rises with its leverage ratio and the level of uncertainty in the economy. A positive credit shock, defined as a rise in the loan-todeposit ratio, increases output, consumption, hours and productivity, and reduces the spread between loan and deposit rates. The effects of the credit shock tend to be highly persistent, even without price rigidities and habit persistence in consumption behavior. JEL classification: E32, E44, G21 Bank classification: Business fluctuations and cycles; Economic models; Credit and credit aggregates; Financial institutions RésuméLes auteurs proposent un modèle théorique pour l'analyse de la relation entre les chocs de crédit, les défaillances d'entreprises et la volatilité, ainsi que pour l'étude des effets de ces chocs sur la dynamique du cycle économique. Dans ce modèle, des entreprises identiques ex ante se différencient ex post en réponse aux chocs technologiques réalisés, qui sont propres à chaque entreprise et peuvent provoquer sa défaillance. Le nouveau modèle employé par les auteurs pour examiner les défaillances des firmes et l'intermédiation financière tient compte des conséquences financières de ces défaillances à la fois sur les ménages et sur les banques. Les résultats tirés d'une version étalonnée du modèle indiquent qu'à l'équilibre de long terme, la probabilité de défaut d'une entreprise augmente avec son ratio de levier et le degré d'incertitude dans l'économie. Un choc de crédit positif -à savoir une hausse du ratio des prêts aux dépôts -accroît la production, la consommation, le nombre d'heures travaillées et la productivité et réduit l'écart entre le taux prêteur et le taux de rémunération des dépôts. Les retombées du choc de crédit tendent à perdurer longtemps même en l'absence de rigidité des prix et de persistance des habitudes de consommation.

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Optimal monetary policy with the cost channel

Cited by 409 publications

References 17 publications

Quantity rationing of credit and the Phillips curve

Quantity rationing of credit and the Phillips curve

Oil Price Shocks: Bank Size and Firm Size Effects

Business Cycle Effects of Credit Shocks in a DSGE Model with Firm Defaults

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