# Uncertainty Shocks and Corporate Borrowing Constraints

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

#### 1.1. A Brief Review of Borrowing Constraints and Access to Credit

#### 1.2. Uncertainty Shocks in the Literature

## 2. Empirical Analysis

#### 2.1. Empirical Specification: The VAR Model

#### 2.2. Variables in the System and Identification Strategy

#### 2.3. The VAR Model Results

#### 2.4. Robustness of the VAR Results

#### 2.4.1. Using the EPUI Measure of Uncertainty

#### 2.4.2. Using Alternative Measurement of the Price Mark-Up

#### 2.4.3. Uncertainty Shocks under the WU-Xia Shadow Rate

#### 2.4.4. Is It about Uncertain Future or Bad Economic Times for Business?

## 3. The Theoretical Model

#### 3.1. The Household Sector

#### 3.2. Monopolistic Production Sector

#### 3.2.1. The Borrowing Constraint

#### 3.2.2. Price Indexation

#### 3.2.3. The Firm’s Maximization Problem

#### 3.3. The Government

#### 3.3.1. Fiscal Policy

#### 3.3.2. Monetary Policy

#### 3.4. Market Clearing

## 4. Numerical Results

#### 4.1. Calibration

#### 4.2. The Macroeconomic Effects of Uncertainty Shock

#### 4.2.1. Borrowing Constraints and the Price Markup

#### 4.2.2. Earnings-Based Constraints vs. Asset-Based Constraints

#### 4.2.3. A Special Case: Both Asset-Based and Earnings-Based Constraints Are Binding

## 5. Summary and Conclusions

## Author Contributions

## Funding

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Appendix A. Empirical Section

#### Appendix A.1. Data Construction for VAR Model Estimation

**Sources of Data**: Data used in the empirical analysis come from a number of sources, including the National Income and Product Account (NIPA), Bank of International Settlements (BIS), and the Federal Reserve Bank of St. Louis. In construction of the the relative price of investment data, we followed the procedure by Drechsel (2020). Table A1 explains data construction process.

**Details on Corporate Earnings**: For corporate earnings, We use the item ’Corporate Business Profits before tax, without IVA and CCAdj’ from the FRED website. Figure A1 below shows the time-series of earnings thus constructed along with the proxy of uncertainty.

**Details on Capital Stock**: For capital stock, on the other hand, we take the item’ Total Capital Expenditures, Flow’ is from the FRED website. Figure A2 below shows the time-series of capital stock constructed along with the VXO.

**Figure A1.**Corporate Earnings and the VXO. Notes: The figure shows the time-series of the corporate sector earnings along with the proxy of uncertainty. The figure suggests that corporate earning is procyclical while the uncertainty proxy is countercyclical. The correlation coefficients between the VXO and firms’ earning is −0.30. Both series are normalized.

**Figure A2.**Capital Stock and the VXO. Notes: The figure shows the time-series of the corporate sector earnings along with the proxy of uncertainty. The figure suggests that capital stock is procyclical while the uncertainty proxy is countercyclical. The correlation coefficients between the VXO and firms’ earning is −0.21. Both series are normalized.

Variables | Sources and Data Construction | Data Transformation |
---|---|---|

Price Level | Consumption Deflator (FRED: CONSDEF) | log |

GDP | Real Gross Domestic Product (FRED: GDPC1) | log |

VXO | CBOE S&P 100 Volatility Index (FRED: VXOCLS) | log |

Business Sector Earnings | Sum of Corporate Business Profits before tax, without IVA and CCAdj (FRED: A446RC1Q027SBEA) and Non-financial Income before taxes, flow (FRED: BOGZ1FA146110005Q), deflated with consumption deflator. | log |

Level of Capital Stock | Domestic Nonfinancial Sectors; Total Capital Expenditures, Flow (FRED:BOGZ1FA385050005Q) minus Nonfinancial Corporate Business; Consumption of Fixed Capital, Structures, Equipment, and Intellectual Property Products, Including Equity REIT Residential Structures (NIPA Basis), Flow (FRED: BOGZ1FA106300003Q), valued at the relative price of investment. | log |

Credit Flow to Non-Financial Corporate Sector | Credit to Private non-financial sector from All sectors at Market value, deflated with consumption deflator (BIS). | log |

Relative Price of Investment | Implicit Price Deflators, Nonresidential, Equipment, Fixed Investment (FRED: Y033RD3Q086SBEA), deflated with Consumption Deflator (CONSDEF) | log |

Economic Policy Uncertainty Index (EPUI) | Uncertainty Proxy for Robustness Analysis. Collected from https://www.policyuncertainty.com/ (accessed on 6 November 2021). | log |

Hours Worked | Nonfarm Business Sector: Hours of All Persons (FRED: HOANBS) | log |

Interest Rate | Effective Federal Funds (FRED:DFF) | |

Interest Rate (Robustness Check) | Wu-Xia Shadow Rate. Collected from https://sites.google.com/view/jingcynthiawu/shadow-rates (accessed on 6 November 2021). | |

Business Sector Confidence | Business Tendency Survey, Confidence Indicator for United States (FRED: BSCICP03USM665S) | log |

Markup | Wages and Salaries’ component of Personal Income (NIPA) Divided by Real GDP (GDPC1), Inverse | log |

#### Appendix A.2. Using the Economic Policy Uncertainty Index (EPUI)

**Figure A3.**The Effects of Uncertainty Shocks when Economic Policy Uncertainty Index (EPUI) is used as a Proxyy. Notes: Figure A3 shows the effect of one standard deviation increase in EPUI on the US output, Hours, Credit Flow to Non-financial Corporates, Firms’ Capital Stock, Firms’ Earning, the Relative Price of Investment, and markup. In the impulse response functions, the shades represent the one standard deviation confidence interval while the middle bold line represents the median response of variables due to uncertainty increase. The data are quarterly and span the period 1986Q1-2019Q4. In the VAR model, all the variables are in log levels, with the exception of the federal funds rate.

#### Appendix A.3. Using the Nekarda and Ramey (2021) Measure of the Price Mark-Up

**Figure A4.**The Effects of Uncertainty Shocks when the Nekarda and Ramey (2021) Measure of the Price Mark-Up is Used. Notes: Shaded regions represent 90 percent standard error bands. The data are quarterly and span the period 1986Q1-2019Q4. With the exception of the federal funds rate, all the other variables are in log levels. The figure shows that both the output and markup decrease following an increase in uncertainty.

#### Appendix A.4. Changes in the Ordering of the Price Mark-Up

**Figure A5.**Effect of Uncertainty Shocks under Different Ordering of Variables. Notes: Shaded regions represent 90 percent standard error bands. The data are quarterly and span the period 1986Q1-2019Q4. With the exception of the federal funds rate, all the other variables are in log levels. The figure shows that output and markup decrease following an increase in uncertainty. Moreover, the correlation coefficient between simulated output and markup after uncertainty shock is computed to be 0.11, making markup procyclical.

#### Appendix A.5. Using the Wu-Xia Shadow Rate as a Proxy for Monetary Policy

**Figure A6.**Uncertainty Shock Effects on the US Economy when Wu-Xia Shadow Rate is Used as a Proxy of Monetary Policy. Notes: Figure A6 shows the effect of one standard deviation increase in the VXO on the US output, Hours, Credit Flow to Non-financial Corporates, Firms’ Capital Stock, Firms’ Earning, and the Relative Price of Investment. In the impulse response functions, the shades represent the 90% confidence interval while the middle bold line represents the median response of variables due to uncertainty increase. The data are quarterly and span the period 1986Q1-2019Q4. In the VAR model, all the variables are in log levels, with the exception of the federal funds rate.

#### Appendix A.6. Uncertainty Shocks and Business Confidence

**Figure A7.**Uncertainty Shocks and Business Confidence. Notes: Shaded regions represent 95 percent standard error bands. The data are quarterly and span the period 1986Q1-2019Q4. With the exception of the federal funds rate, all the other variables are in log levels.

#### Appendix A.7. Effect of Uncertainty Shocks When Non-Uncertainty Variables Are Ordered First

**Figure A8.**Effects of Uncertainty Shocks when Non-Uncertainty Variables are Ordered First. Notes: This figure shows the effect of one standard deviation increase in the VXO on the US output, Hours, Credit Flow to Non-financial Corporates, Firms’ Capital Stock, Firms’ Earning, and the Relative Price of Investment. The ordering of variables is as follows: relative price of investment, price level, hours of work, VXO, capital stock, firms’ earning, credit flow to non-financial sector, GDP, and the federal funds rate. In the impulse response functions, the shades represent the 90% confidence interval while the middle bold line represents the median response of variables due to uncertainty increase. The data are quarterly and span the period 1986Q1-2019Q4. In the VAR model, all the variables are in log levels, with the exception of the federal funds rate.

## Appendix B. Effects of Uncertainty Shocks in a DSGE Model

#### Appendix B.1. Steady State Equations

#### Appendix B.2. Demand Uncertainty Shocks and Borrowing Constraints

**Figure A9.**Effects of Demand Uncertainty Shocks in a DSGE Model with Alternative Formulations of Borrowing Constraints. Notes: Figure A9 shows the impulse responses of macroeconomic variables to a standard deviation shock in demand uncertainty, when different measurements of credit frictions are used. The black lines represent the responses of the model with asset-based constraints while the cyan colored lines are outcome of earnings-based constraints. The structural parameters to generate these IRFs are shown in Table 2. We calculate ${\rho}_{{\sigma}_{D}}=0.4$ and ${\sigma}_{{\sigma}_{t}^{D}}=0.058$.

#### Appendix B.3. Uncertainty Shocks at the Zero Lower Bound (ZLB) in a DSGE Model

**Figure A10.**Impulse Responses at the ZLB When Earning-Based Constraints Are Binding. Notes: The figure displays model IRFs of different model variables to technology uncertainty shocks when the nominal interest rate is subjected to the Zero Lower Bound infinitely, under two alternative calibrations in which only the earnings-based constraint (cyan colored lines) or only the assets-based constraint (black colored lines) is present. The structural parameters to generate these IRFs are shown in Table 2. Based on a VAR analysis (not shown), we calculate ${\rho}_{\sigma}$ = 0.0122 and ${\sigma}_{{\sigma}_{t}^{A}}$ = 0.063.

#### Appendix B.4. Effects of First Moment Shocks on Credit Dynamics

**Figure A11.**Technology Shock, Earning-Based Borrowing Constraints, and Credit Dynamics. Notes: Figure A11 displays model IRFs of different model variables to technology shocks, under three alternative calibrations in which only the earnings-based constraint (cyan colored lines), only the collateral constraint (lines with black diamonds), or without any constraints (impulses with blue stars) is present. The parameters to generate these IRFs are shown in Table 2. We set ${\rho}_{A}$ = 0.95 and ${\sigma}_{A}$ = 0.01.

## Notes

1 | For more on the credit channel see (Balke et al. 2017; Valencia 2017; Cesa-Bianchi and Fernandez-Corugedo 2018; Choi et al. 2018; Brand et al. 2019). |

2 | For more on these debt covenants see (Crouzet 2017; Benmelech et al. 2020; Donaldson et al. 2020; Roberts and Sufi 2009; Lian and Ma 2020; Drechsel 2020; Kermani and Ma 2020). |

## References

- Abel, Andrew B. 1983. Optimal Investment under Uncertainty. American Economic Review 73: 228–33. [Google Scholar]
- Adjemian, Stephane, Houtan Bastani, Michel Juillard, Fredrick Karame, Ferhat Mihoubi, George Perendia, Johannes Pfeifer, Marco Ratto, and Sebastien Villemot. 2011. Dynare Reference Manual, Version 4. Available online: https://www.dynare.org/wp-repo/dynarewp001.pdf (accessed on 10 February 2022).
- Aghion, Phillip, George M. Angeletos, Abhijit Banerjee, and Kalina Manova. 2010. Volatility and growth: Credit Constraints and the Composition of Investment. Journal of Monetary Economics 57: 246–65. [Google Scholar] [CrossRef] [Green Version]
- Auerbach, Alan J., and Yuriy Gorodnichenko. 2012. Measuring the Output Responses to Fiscal Policy. American Economic Journal: Economic Policy 4: 1–27. [Google Scholar] [CrossRef] [Green Version]
- Baker, Scott. R., Nicholas Bloom, and Steven J. Davis. 2016. Measuring Economic Policy Uncertainty. The Quarterly Journal of Economics 131: 1593–636. [Google Scholar] [CrossRef]
- Balke, Nathan S., Enrique Martínez-García, and Zheng Zeng. 2017. Understanding the Aggregate Effects of Credit Frictions and Uncertainty. Working Paper No. 317. Dallas: Federal Reserve Bank of Dallas. [Google Scholar]
- Bansal, Ravi, and Amir Yaron. 2004. Risks for the Long Run: A Potential Resolution of Asset-Pricing Puzzles. The Journal of Finance 4: 1481–509. [Google Scholar] [CrossRef] [Green Version]
- Basu, Susanto, and Brent Bundick. 2017. Uncertainty Shocks in a Model of Effective Demand. Econometrica 85: 937–58. [Google Scholar] [CrossRef] [Green Version]
- Begley, Joy. 1994. Restrictive Covenants Included in Public Debt Agreements: An Empirical Investigation. Working Paper. Vancouver: University of British Columbia. [Google Scholar]
- Bernanke, Ben S. 1983. Irreversibility, uncertainty, and cyclical investment. Quarterly Journal of Economics 98: 85–106. [Google Scholar] [CrossRef] [Green Version]
- Benmelech, Efraim, Nittai Kumar, and Raghuram Rajan. 2020. The Decline of Secured Debt. NBER Working Paper 26637. Cambridge: NBER. [Google Scholar] [CrossRef]
- Bernanke, Ben S., Mark Gertler, and Simon Gilchrist. 1999. Chapter 21 The financial accelerator in a quantitative business cycle framework. In Handbook of Macroeconomics. New York: Elsevier, Vol. 1, pp. 1341–93. ISBN 9780444501585. [Google Scholar] [CrossRef]
- Bloom, Nicholas. 2009. The Impact of Uncertainty Shocks. Econometrica 77: 623–85. [Google Scholar]
- Bordo, Michael D., John V. Duca, and Christoffer Koch. 2016. Economic Policy Uncertainty and the Credit Channel: Aggregate and Bank Level U.S. Evidence Over Several Decades. NBER Working Paper 22021, NBER, Cambridge. Available online: http://www.nber.org/papers/w22021 (accessed on 10 February 2022).
- Born, lBenjamin, and lJohannes Pfeifer. 2014. Policy risk and the business cycle. Journal of Monetary Economics 68: 68–85. [Google Scholar] [CrossRef] [Green Version]
- Bradley, Michael, and Michael R. Roberts. 2015. The Structure and Pricing of Corporate Debt Covenants. Quarterly Journal of Finance 5: 1–37. [Google Scholar] [CrossRef]
- Brand, Thomas, Marlene Isore, and Fabien Tripier. 2019. Uncertainty shocks and firm creation: Search and monitoring in the credit market. Journal of Economic Dynamics and Contorl 99: 19–53. [Google Scholar] [CrossRef] [Green Version]
- Carriero, Andrea, Haroon Mumtaz, Konstatinos Theodoridis, and Angeliki Theophilopoulou. 2015. The Impact of Uncertainty Shocks under Measurement Error: A Proxy SVAR Approach. Journal of Money, Credit, and Banking 47: 1221–38. [Google Scholar] [CrossRef] [Green Version]
- Cesa-Bianchi, Ambrogio, and Emilio Fernandez-Corugedo. 2018. Uncertainty, Financial Frictions, and Nominal Rigidities: A QUantitative Investigation. Journal of Money, Credit, and Banking 50: 603–36. [Google Scholar] [CrossRef] [Green Version]
- Choi, Sangyup, Davide Furceri, Yi Huang, and Prakash Loungani. 2018. Aggregate uncertainty and sectoral productivity growth: The role of credit constraints. Journal of International Money and Finance 88: 314–30. [Google Scholar] [CrossRef] [Green Version]
- Crouzet, Nicolas. 2017. Aggregate implications of corporate debt choices. Review of Economic Studies 85: 1635–82. [Google Scholar] [CrossRef]
- De Fiore, Fiorella, and Herald Uhlig. 2011. Bank finance versus bond finance. Journal of Money, Credit, and Banking 43: 1399–421. [Google Scholar] [CrossRef] [Green Version]
- Donaldson, Jason R., Denis Gromb, and Georgia Piacentino. 2020. Conflicting Priorities: A Theory of Covenants and Collateral. 2019 Meeting Papers No 157. Cartegena: Society for Economic Dynamics. [Google Scholar]
- Drechsel, Thomas. 2020. Earning-Based Borrowing Constraints and Macroeconomic Fluctuations. New York: Mimeo. [Google Scholar]
- Fernandez-Villaverde, Jesus, Pablo A. Guerron-Quintana, Juan F. Rubio-Ramirez, and Martin Uribe. 2011. Risk Matters: The Real Effects of Volatility Shocks. The American Economic Review 101: 2530–61. [Google Scholar] [CrossRef] [Green Version]
- Frank, Murrry Z., and Vidhan K. Goyal. 2009. Capital structure decisions: Which factors are reliably important? Financial Management 38: 1–37. [Google Scholar]
- Gilchrist, Simon, Jae W. Sim, and Egon Zakrajsek. 2017. Uncertainty, Financial Frictions, and Investment Dynamics. NBER Working Paper 20038. Cambridge: National Bureau of Economic Research, Inc. [Google Scholar]
- Hartman, Richard. 1972. The effects of price and cost uncertainty on investment. The Journal of Economic Theory 5: 258–66. [Google Scholar] [CrossRef]
- Holmstrom, Bengt, and Jean Tirole. 1997. Financial Intermediation, Loanable Funds, and the Real Sector. The Quarterly Journal of Economics 112: 663–91. [Google Scholar] [CrossRef]
- Ireland, Peter N. 2007. Changes in the Federal Reserve’s Inflation Target: Causes and Consequences. Journal of Money, Credit, and Banking 39: 1851–82. [Google Scholar] [CrossRef]
- Ivashina, Victoria, Luc Laeven, and Enrique M. Benito. 2020. Loan Types and Bank Lending Channel. Working Paper. Madrid: Bank of Spain. [Google Scholar]
- Jermann, Urban, and Vincenzo Quadrini. 2012. Macroeconomic Effects of Financial Shocks. American Economic Review 102: 238–71. [Google Scholar] [CrossRef] [Green Version]
- Kermani, Amir, and Yueran Ma. 2020. Two Tales of Debt. NBER Working Paper 27641. Cambridge: National Bureau of Economic Research. [Google Scholar] [CrossRef]
- Kiyotaki, Nobuhiro, and John Moore. 1997. Credit Cycles. Journal of Political Economy 105: 211–48. [Google Scholar] [CrossRef]
- Leduc, Sylvian, and Keith Sill. 2007. Monetary Policy, Oil Shocks, and TFP: Accounting for the Decline in U.S. Volatility. Review of Economic Dynamics 10: 595–614. [Google Scholar] [CrossRef] [Green Version]
- Leduc, Sylvian, and Keith Sill. 2013. Expectations and Economic Fluctuations: An Analysis Using Survey Data. The Review of Economics and Statistics 95: 1352–67. [Google Scholar] [CrossRef]
- Leduc, Sylvia, and Zheng Liu. 2016. Uncertainty Shocks are Aggregate Demand Shocks. Journal of Monetary Economics 82: 20–35. [Google Scholar] [CrossRef] [Green Version]
- Lian, Chen, and Yueran Ma. 2020. Anatomy of corporate borrowing constraints. The Quarterly Journal of Economics 136: 229–91. [Google Scholar] [CrossRef]
- Lutkepohl, Helmut. 2005. New Introduction to Multiple Series Analysis. Berlin: Springer. [Google Scholar]
- Malitz, Ileen. 1986. On Financial Contracting: The Determinants of Bond Covenants. Financial Management 15: 18–25. [Google Scholar] [CrossRef]
- Mendoza, Enrique G. 2010. Sudden Stops, Financial Crises, and Leverage. American Economic Review 100: 1941–66. [Google Scholar] [CrossRef]
- Mumtaz, Haroon, and Paolo Surico. 2018. Policy uncertainty and aggregate fluctuations. Journal of Applied Econometrics 33: 319–31. [Google Scholar] [CrossRef]
- Nekarda, Christopher J., and Valleri A. Ramey. 2021. The Cyclical Behavior of the Price-Cost Markup. Journal of Money Credit and Banking 52: 319–53. [Google Scholar] [CrossRef]
- Oi, Walter Y. 1961. The desirability of price instability under perfect competition. Econometrica 29: 58–64. [Google Scholar] [CrossRef]
- Rauh, Joshua D., and Amir Sufi. 2010. Capital Structure and Debt Structure. The Review of Financial Studies 23: 4242–280. [Google Scholar] [CrossRef] [Green Version]
- Roberts, Michael R., and Amir Sufi. 2009. Renegotiation of financial contracts: Evidence from Private Credit Agreements. Journal of Financial Economics 93: 159–84. [Google Scholar] [CrossRef]
- Rotemberg, Julio J. 1982. Sticky Prices in the United States. Journal of Political Economy 90: 1187–211. [Google Scholar] [CrossRef] [Green Version]
- Smets, Frank, and Rafael Wouters. 2007. Shocks and Frictions in U.S. Business Cycles: A Bayesian DSGE Approach. American Economics Review 97: 586–606. [Google Scholar] [CrossRef] [Green Version]
- Smith, Clifford W., and Jerold B. Warner. 1979. On Financial Contraction: An Analysis of Bond Covenants. Journal of Financial Economics 7: 117–61. [Google Scholar] [CrossRef]
- Stiglitz, Joseph E., and Andrew Weiss. 1981. Credit Rationing in Markets with Imperfect Information. The American Economic Review 71: 393–410. [Google Scholar]
- Valencia, Fabian. 2017. Aggregate Uncertainty and the Supply of Credit. Journal of Banking and Finance 81: 150–65. [Google Scholar] [CrossRef] [Green Version]
- Wu, Jing C., and Fan D. Xia. 2016. Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound. Journal of Money, Credit, and Banking 48: 253–91. [Google Scholar] [CrossRef]

**Figure 2.**Impulse Responses to Uncertainty Shock in the VAR Model. Notes: Shaded regions represent 95 percent standard error bands. The data are quarterly and span the period 1986Q1-2019Q4. With the exception of the federal funds rate, all the other variables are in log levels.

**Figure 3.**Impulse Responses to a productivity Uncertainty Shock Under Alternative Borrowing Constraints. Notes: The Figure presents the model IRFs of selected variables to a productivity uncertainty shocks. The cyan colored lines represent the model with earnings-based constraint, the black diamonds represent the model with asset-based constraints, whereas the responses with blue stars represent the baseline model without any financial frictions. The parameter sets that generate the IRFs are presented in Table 2. In addition, we set ${\rho}_{\sigma}$ = 0.4 and ${\sigma}_{{\sigma}_{t}^{A}}$ = 0.058.

**Figure 4.**Impulse Responses of the Price Markup to a Productivity Uncertainty Shock under Alternative Borrowing Constraints. Notes: The figure displays model IRFs of the markup to a productivity uncertainty shock. The cyan colored lines represent the model with earnings-based constraint, the black diamonds represent the model with asset-based constraints, whereas the responses with blue stars represent the baseline model without any financial frictions. The parameter sets that generate the IRFs are presented in Table 2. In addition, we set ${\rho}_{\sigma}$ = 0.4 and ${\sigma}_{{\sigma}_{t}^{A}}$ = 0.058.

**Figure 5.**Impulse Responses to Uncertainty Shocks When Both Asset and Earnings Based Constraints are Binding. Notes: The figure displays the model IRFs of selected variables to productivity uncertainty shocks when borrowing by the corporate sector is constrained by both earnings and assets. The parameters to generate these IRFs are shown in Table 2. To obtain the IRFs we estimate ${\rho}_{\sigma}$ = 0.4 and ${\sigma}_{{\sigma}_{t}^{A}}$ = 0.058.

Papers | Share of Assets-Based Loans (%) | Share of Earnings-Based Loans (%) | Share of Loans Backed by Both (%) | Information Unavailable (%) | Sample Size Firms |
---|---|---|---|---|---|

Rauh and Sufi (2010) | 6.5 | 24.7 | 13.2 | 55.6 | 2453 firms |

Lian and Ma (2020) | 20 | 80 | 58,241 (Cash-Based loans) 58,227 (Assets-Based loans) | ||

Drechsel (2020) | 30 | 35 | 35 | 50,000 loan deals (15,000 firms) | |

Ivashina et al. (2020) | 41.8 | 51.7 |

Parameter | Description | Value |
---|---|---|

Structural Parameters | ||

$\beta $ | Hoursehold’s Discount Factor | 0.99 |

$\chi $ | Disutility of Work | 0.564 |

$\psi $ | Price Adjustment Cost | 110 |

$\pi $ | Inflation Target | 1.0045 |

${\Psi}_{\pi}$ | Policy Weight on Inflation | 1.5 |

${\Psi}_{R}$ | Persistence of Interest Rate | 0.9 |

${\Psi}_{y}$ | Policy Weight on Output | 0.125 |

$\Phi $ | Capital Adjustment Cost | 2 |

$\rho $ | Probability of Debt being Backed by Earnings | 1 |

$\alpha $ | Output Elasticity of Capital | 0.33 |

$\nu $ | Elasticity of Labor Supply | 1 |

$\u03f5$ | Price Elasticity of Demand | 6 |

${\theta}_{k}$ | Loan to Value Ratio | 0.067 |

${\theta}_{\pi}$ | Loan to Earnings Ratio | 5.77 |

N | Steady State Work | 0.33 |

Shock Process | ||

${\rho}_{A}$ | Persistence of Technology Shock | 0.95 |

${\rho}_{\sigma}$ | Persistence of Shock to Volatility of Technology Shock | 0.4 |

${\sigma}_{{\sigma}_{t}^{A}}$ | Volatility of Uncertainty Shock | 0.058 |

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Kamara, A.; Koirala, N.P.
Uncertainty Shocks and Corporate Borrowing Constraints. *Int. J. Financial Stud.* **2023**, *11*, 21.
https://doi.org/10.3390/ijfs11010021

**AMA Style**

Kamara A, Koirala NP.
Uncertainty Shocks and Corporate Borrowing Constraints. *International Journal of Financial Studies*. 2023; 11(1):21.
https://doi.org/10.3390/ijfs11010021

**Chicago/Turabian Style**

Kamara, Ahmed, and Niraj P. Koirala.
2023. "Uncertainty Shocks and Corporate Borrowing Constraints" *International Journal of Financial Studies* 11, no. 1: 21.
https://doi.org/10.3390/ijfs11010021