Estimating a banking-macro model using a multi-regime VAR. (English) Zbl 1315.91049
Schleer-van Gellecom, Frauke (ed.), Advances in non-linear economic modeling. Theory and applications. Berlin: Springer (ISBN 978-3-642-42038-2/hbk; 978-3-642-42039-9/ebook). Dynamic Modeling and Econometrics in Economics and Finance 17, 3-40 (2014).
Summary: This paper indroduces a Banking-Macro Model and estimates the linkages using a Multi-Regime Vector Auto Regression (MRVAR). The model of the banking-macro link is a simplified version of the model by M. K. Brunnermeier and Yu. Sannikov [“A macroeconomic model with a financial sector”, Am. Econ. Rev. 104, No. 2, 379–421 (2014; doi:10.1257/aer.104.2.379)]. The banking sector is represented as a wealth fund that accumulates capital assets, can heavily borrow and pays bonuses. We presume that the banking sector faces not only loan losses but is also exposed to a deterioration of its balances sheets due to adverse movements in asset prices. In contrast to previous studies that use the financial accelerator – which is locally amplifying but globally stable and mean reverting – our model shows local instability and globally multiple regimes. Whereas the financial accelerator leads, in terms of econometrics, to a one-regime VAR, we demonstrate the usefulness of the MRVAR approach. We estimate our model for the U.S. with a MRVAR using data on a constructed financial stress index and industrial production. We also conduct impulse-response analyses which allowing us to explore regime dependent shocks. We show that the shock profiles depend on the regime the economy is in and the size of the shocks. As to the recently discussed unconventional monetary policy of quantitative easing, we find that the relative effects of monetary shocks depend on the size of the shocks.
For the entire collection see [Zbl 1283.91005].
For the entire collection see [Zbl 1283.91005].
MSC:
| 91B62 | Economic growth models |
| 91B84 | Economic time series analysis |
| 91B64 | Macroeconomic theory (monetary models, models of taxation) |
| 91G70 | Statistical methods; risk measures |
| 62P20 | Applications of statistics to economics |
References:
| [1] | Adrian, T.; Shin, H. S., Money, liquidity, and monetary policy, American Economic Review, American Economic Association, 99, 2, 600-605 (2009) · doi:10.1257/aer.99.2.600 |
| [2] | Adrian, T., Moench, A., & Shin, H. S. (2010). Macro risk premium and intermediary balance sheet quantities (Staff Report no 428). Federal Reserve Bank of New York. |
| [3] | Becker, S.; Gruene, L.; Semmler, W., Comparing second order approximation with dynamic programming, Computational Economics, 30, 1, 65-91 (2007) · Zbl 1282.91209 · doi:10.1007/s10614-007-9087-1 |
| [4] | Bernanke, B.; Gertler, M.; Gilchrist, S.; Taylor, J.; Woodford, M., The financial accelerator in a quantitative business cycle framework, Handbook of macroeconomics (1999), Amsterdam: North-Holland, Amsterdam |
| [5] | Brunnermeier, M. K., & Sannikov, Y. (2014, forthcoming). A macroeconomic model with the financial sector. American Economic Review. |
| [6] | Brunnermeier, M.; Pedersen, L., Market liquidity and funding liquidity, The Review of Financial Studies, 22, 6, 2210-2238 (2009) |
| [7] | Caballero, R. J.; Krishnamurthy, A., Global Imbalances and Financial Fragility, American Economic Review, American Economic Association, 99, 2, 584-588 (2009) · doi:10.1257/aer.99.2.584 |
| [8] | Carlstrom, C.; Fuerst, T.; Paustian, M., Optimal monetary policy in a model with agency costs, Fed conference on monetary policy and the financial market (2009) |
| [9] | Chan, W.-S.; Wong, A. C. S.; Tong, H., Some nonlinear threshold autoregressive time series models for actuarial use, North American Actuarial Journal, 8, 37-61 (2004) · Zbl 1085.62119 · doi:10.1080/10920277.2004.10596170 |
| [10] | Chiarella, C.; Flaschel, P.; Franke, R.; Semmler, W., Financial market and the macro economy (2009), London: Routledge, London |
| [11] | Christensen, I.; Dib, A., The financial accelerator in an estimated new Keynesian model, Review of Economic Dynamics, 11, 155-178 (2008) · doi:10.1016/j.red.2007.04.006 |
| [12] | Curdia, V., & Woodford, M. (2009a). Credit frictions and optimal monetary policy. BIS Working Paper, No. 278. |
| [13] | Curdia, V., & Woodford, M. (2009b). Credit spreads and monetary policy. Paper presented at the Conference on Financial Market and Monetary Policy, Federal Reserve Board, June 5-6. |
| [14] | Del Negro, M., Eggersson, G., Ferrero, A., & Kiyotaki, N. (2010). The great escape: a quantitative evaluation of the Fed’s non-standard policies (Working paper). Federal Reserve Bank of New York. |
| [15] | DiNapoli, T. P., & Bleiwas, K. B. (2010). Securities industries in New York city (Report 10-2011). |
| [16] | Ehrmann, M.; Ellison, M.; Valla, N., Regime-dependent impulse response functions in a Markov-switching vector autoregressive model, Economics Letters, 78, 295-299 (2003) · doi:10.1016/S0165-1765(02)00256-2 |
| [17] | Ernst, E., Mittnik, S., & Semmler, W. (2010). Interaction of labor and credit market frictions: a theoretical and empirical analysis. Paper prepared for the Winter Meeting of the Econometric Society, Atlanta, January 2010. |
| [18] | Geanakoplos, J. (2010). The Leverage cycle (Cowles Foundation Discussion Paper No. 1715). Yale University—Cowles Foundation. |
| [19] | Geanakoplos, J.; Farmer, J., The virtues and vices of equilibrium and the future of financial economics, Complexity, 14, 3, 11-38 (2009) · doi:10.1002/cplx.20261 |
| [20] | Geneva Report on the World Economy 11 (2009). Fundamental principles of financial regulations. ICMB. |
| [21] | Gertler, M.; Karadi, P., A model of unconventional monetary policy, Journal of Monetary Economics, 58, 1, 17-34 (2011) · doi:10.1016/j.jmoneco.2010.10.004 |
| [22] | Gilchrist, S.; Zagrajsek, S., Credit spreads and business cycle fluctuations, American Economic Review, 102, 1692-1720 (2012) · doi:10.1257/aer.102.4.1692 |
| [23] | Gilchrist, S., Ortiz, A., & Zagrajsek, S. (2009). Credit risk and the macroeconomy: evidence from an estimated DSGE model. Manuscript, Boston University. |
| [24] | Gorton, G. (2009). Information, liquidity, and the (ongoing) panic of 2007. Prepared for the Federal Reserve Bank of Kansas City, Jackson Hole Conference, August 2008. |
| [25] | Gorton, G. B., Slapped by the invisible hand: the panic of 2007 (2010), Oxford: Oxford University Press, Oxford |
| [26] | Greenwald, B.; Stiglitz, J., Financial market imperfections and business cycles, Quarterly Journal of Economics, 108, 77-114 (1993) · doi:10.2307/2118496 |
| [27] | Gruene, L.; Semmler, W., Using dynamic programming with adaptive grid scheme for optimal control problems in economics, Journal of Economic Dynamics & Control, 28, 2427-2456 (2004) · Zbl 1202.49026 · doi:10.1016/j.jedc.2003.11.002 |
| [28] | Gruene, L.; Semmler, W., Default risk, asset pricing, and debt control, Journal of Financial Econometrics, 3, 1, 79-106 (2005) · Zbl 1418.91586 · doi:10.1093/jjfinec/nbi006 |
| [29] | Gruene, L.; Semmler, W.; Sieveking, M., Creditworthiness and threshold in a credit market model with multiple equilibria, Economic Theory, 25, 2, 287-315 (2004) · Zbl 1107.91361 |
| [30] | Gruene, L.; Semmler, W.; Bernard, L., Firm value, diversified capital assets and credit risk, Journal of Credit Risk, 3, 4, 81-113 (2007) |
| [31] | Hakkio, C. S., & Keeton, W. R. (2009). Financial stress: what is it, how can it be measured, and why does it matter? Federal Reserve Bank of Kansas City. |
| [32] | Hall, E. R., Forward looking decision making (2010), Princeton: Princeton University Press, Princeton · Zbl 1194.91010 |
| [33] | Hamilton, J. D., A new approach to the economic analysis of nonstationary time series and the business cycle, Econometrica, 57, 357-384 (1989) · Zbl 0685.62092 · doi:10.2307/1912559 |
| [34] | Hatzius, J., Hooper, P., Mishkin, F., Schoenholtz, K., & Watson, M. (2010). Financial condition indexes: a fresh look after the financial crisis. www.Princeton.edu/ mwatson. |
| [35] | He, Z., & Krishnamurthy, A. (2008). Intermediary asset pricing. Manuscript, University of Chicago. |
| [36] | Hsiao, C.-Y., & Semmler, W. (2009). Harmonic fitting for long run economic data. Manuscript. www.newschool.edu/nssr/cem (there financial market). |
| [37] | Illing, M.; Liu, Y., Measuring financial stress in a developed country: an application to Canada, Journal of Financial Stability, 2, 3, 243-265 (2006) · doi:10.1016/j.jfs.2006.06.002 |
| [38] | Kindleberger, C.; Aliber, R., Manias, panics, and crashes: a history of financial crises (2005), New York: Wiley, New York · doi:10.1057/9780230628045 |
| [39] | Koop, G.; Pesaran, M.; Potter, S., Impulse responses in nonlinear multivariate models, Journal of Econometrics, 74, 119-147 (1996) · Zbl 0865.62086 · doi:10.1016/0304-4076(95)01753-4 |
| [40] | Lux, T. (2009). Mass psychology in action: identification of social interaction effects in the German stock market (Working paper, No: 1514). Kieler Institut for the World Economy. |
| [41] | Merton, R. C., On the pricing of corporate debt. The risk structure of the interest rate, Journal of Finance, 2, 449-470 (1974) |
| [42] | Minsky, H. P., John Maynard Keynes (1975), New York: Columbia University Press, New York |
| [43] | Minsky, H. P., Can it happen again? (1982), Armonk: ME Sharpe, Armonk |
| [44] | Mittnik, S.; Semmler, W., Regime dependence of the fiscal multiplier, Journal of Economic Behavior & Organization, 83, 3, 502-522 (2012) · doi:10.1016/j.jebo.2012.02.005 |
| [45] | Mittnik, S.; Semmler, W., The real consequences of financial stress, Journal of Economic Dynamics & Control, 37, 8, 1479-1499 (2013) · Zbl 1327.91051 · doi:10.1016/j.jedc.2013.04.014 |
| [46] | Mittnik, S.; Zadrozny, P., Asymptotic distributions of impulse responses, step responses, and variance decompositions of estimated linear dynamic models, Econometrica, 61, 857-887 (1993) · Zbl 0780.62016 · doi:10.2307/2951765 |
| [47] | Reinhart, C.; Rogoff, K. S., This time is different: eight centuries of financial folly (2009), Princeton: Princeton University Press, Princeton |
| [48] | Semmler, W.; Bernard, L., Boom-bust cycles: leveraging, complex securities, and asset prices, Journal of Economic Behavior & Organization, 81, 2, 442-465 (2012) · doi:10.1016/j.jebo.2011.07.002 |
| [49] | Semmler, W.; Lucas, B., Boom-bust cycles: leveraging, complex securities, and asset prices, Journal of Economic Behavior and Organization, 82, 2, 442-465 (2009) |
| [50] | Semmler, W.; Gruene, L.; Oehrlein, L. C., Dynamic consumption and portfolio decisions with time varying asset returns, Journal of Wealth Management, 12, 3, 21-47 (2009) · doi:10.3905/JWM.2009.12.2.021 |
| [51] | Stein, J., Stochastic optimal control and U.S. financial debt crisis (2012), Heidelberg: Springer, Heidelberg · Zbl 1250.91006 · doi:10.1007/978-1-4614-3079-7 |
| [52] | Tong, H.; Chen, C. H., On a threshold model, Pattern recognition and signal processing (1978), Amsterdam: Kluwer, Amsterdam |
| [53] | Tong, H., Threshold models in non-linear time series analysis (1983), New York: Springer, New York · Zbl 0527.62083 · doi:10.1007/978-1-4684-7888-4 |
| [54] | Tsay, R. S., Testing and modelling multivariate threshold models, Journal of the American Statistical Association, 93, 1188-1202 (1998) · Zbl 1063.62578 · doi:10.1080/01621459.1998.10473779 |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
