Conventional and unconventional monetary policy reaction to uncertainty in advanced economies: evidence from quantile regressions

Christina Christou; Ruthira Naraidoo; Rangan Gupta

doi:10.1515/snde-2018-0056

Article

Conventional and unconventional monetary policy reaction to uncertainty in advanced economies: evidence from quantile regressions

Christina Christou , Ruthira Naraidoo and Rangan Gupta

Published/Copyright: December 16, 2019

Published by

Become an author with De Gruyter Brill

Submit Manuscript Author Information

From the journal Studies in Nonlinear Dynamics & Econometrics Volume 24 Issue 3

Abstract

This paper investigates how the Federal Reserve (Fed) and the Bank of England, Bank of Japan and the European Central Bank reacted in the aftermath of the financial crisis by making use of both conditional and unconditional interest rate quantiles regressions and data on shadow short rate of interest and a measure of uncertainty. Firstly, the unconditional quantile regression offers some support for increased reaction by the Fed as the ZLB is approached. Secondly, the decreased reaction of the Fed and other monetary policy makers towards uncertainty particularly at lower conditional quantiles of interest rates lends support to expansionary mechanism in place during this time. Hence uncertainty is key to policy reaction, and more so during episodes of crisis.

Keywords: advanced economies; conditional and unconditional quantile regressions; interest rate rule; shadow rate of interest; uncertainty; zero lower bound

JEL Classification: C22; E52

Funding source: Open University of Cyprus

Award Identifier / Grant number: 2018/20

Funding statement: This work was supported by the Open University of Cyprus, Funder Id: http://dx.doi.org/10.13039/100012996, Grand No: 2018/20, Essays in Economics.

Appendix

Table 3:

Forward Looking Taylor-type rule: IVQ (Lee 2007) results using 3^rd-order power series.

	τ											Wald test
	0.05	0.10	0.20	0.30	0.40	0.50	0.60	0.70	0.80	0.90	0.95
Panel A: Inflation coefficients α_π
EA	0.9855**	0.5613**	0.1964*	0.1382**	−0.0531**	−0.0129**	−0.2096**	0.4300***	0.3431**	0.2855***	0.1680**	1.84 [0.0544]
Japan	−0.1245	−0.7521	−0.9478	−0.5560**	−0.4393**	−0.6678	−0.3321	0.2027	0.1027	0.1264	0.1334	0.64 [0.7821]
UK	0.5238	1.0574**	0.4919**	0.0701***	−0.0509**	−0.0432	−0.0562	0.2365	0.1296	0.4274	0.1338***	0.54 [0.8577]
US	1.1323	1.4597***	0.8343***	0.1478***	0.7185***	1.2776***	1.4844***	1.8906***	2.1985***	2.4423***	2.6881***	3.03 [0.0011]
Panel B: Gap coefficients α_y
EA	0.0146***	0.0506***	0.2194***	0.2432***	0.2720***	0.2708***	0.2229***	0.2181***	0.2034***	0.2286***	0.2094***	3.49 0.0003
Japan	0.1856	0.2015	0.0674***	0.0514***	0.0702**	0.0227***	0.0591***	0.0257***	0.0243**	0.0213**	0.0240***	1.73 [0.0767]
UK	0.2063***	0.3578***	0.2922**	0.3227***	0.2056***	0.2775***	0.2697***	0.3099***	0.3731***	0.2441***	0.1445***	1.46 [0.1537]
US	−0.4042	−0.2878	0.0074***	0.0185***	0.0495***	0.0597***	0.2080***	0.3070***	0.3638***	0.5211***	0.5512***	4.33 [0.0000]
Panel C: EPU coefficients α_epu
EA	−0.0828***	−0.0793***	−0.0590***	−0.0463***	−0.0429***	−0.0417***	−0.0401***	−0.0353***	−0.0249***	−0.0223***	−0.0223***	8.11 [0.0000]
Japan	−0.0369***	−0.0497***	−0.0301***	−0.0170***	−0.0101***	−0.0139***	−0.0042***	0.0025***	−0.0003***	0.0011***	−0.0020***	0.77 [0.6547]
UK	−5.9176***	−5.6592***	−5.1127***	−5.1449***	−5.0611***	−4.6806***	−4.8604***	−4.9421***	−4.1461***	−4.1799***	−3.8998***	2.61 [0.0050]
US	−0.1171***	−0.0946***	−0.0958***	−0.1128***	−0.1070***	−0.0955***	−0.0791***	−0.0564***	−0.0384***	−0.0271***	−0.0078***	14.61 [0.0000]

Monetary rule: where i_t is the end of month shadow interest rate, π_t+12 is the forward looking inflation, y_t is the output gap, and epu_t is the economic policy uncertainty index. Instruments: 1–3 lags of inflation, output gap and epu. *, **, *** indicate statistical significance at 10%, 5% and 1% statistical levels, respectively. Wald statistic tests the null hypothesis of parameter equality across quantiles. IVQ regressions calculations use power series with k = 3. Figures in square brackets are p-values.

Table 4:

Forward looking Taylor-type rule: IVQ (Lee 2007) results using 5^th-order power series.

	τ											Wald test
	0.05	0.10	0.20	0.30	0.40	0.50	0.60	0.70	0.80	0.90	0.95
Panel A: Inflation coefficients α_π
EA	0.7156	0.6712	0.0660	0.1400	−0.1049	−0.1784	−0.1406	0.3669	0.2570	0.2908	0.0440	2.28 [0.0182]
Japan	0.3570	−0.4202	−1.0473*	−0.5144	−0.4767	−0.5719	−0.3461	0.1648	0.1684	0.1025	0.0439	0.64 [0.7821]
UK	0.6694	0.7858	0.5123	0.6291*	0.1803	0.1125	0.2017	−0.0681	0.1606	0.3624	−0.5817	0.58 [0.8317]
US	1.5754**	1.0651*	1.2215*	0.6256	0.6331	1.0380	1.2652*	1.8998***	2.0167***	2.4165***	1.5310**	1.94 [0.0389]
Panel B: Gap coefficients α_y
EA	0.0465	0.0116	0.1801*	0.2346***	0.2698***	0.2440***	0.2334***	0.1945***	0.1899***	0.2056***	0.2293***	2.35 [0.0117]
Japan	0.1965	0.1963	0.0958	0.0737	0.0664	0.0308	0.0614*	0.0404*	0.0273*	0.0189*	0.0171*	1.40 [0.1796]
UK	0.0607	0.1224	0.2271**	0.2352***	0.2428***	0.2236***	0.2278***	0.2942***	0.3955***	0.2071	0.1453	1.82 [0.0588]
US	−0.5042***	−0.3328***	−0.1187	−0.0409	−0.0102	0.0831	0.2796**	0.3844***	0.5259***	0.5676***	0.7133***	5.45 [0.0000]
Panel C: EPU coefficients α_epu
EA	−0.0797***	−0.0751***	−0.0592***	−0.0456***	−0.0430***	−0.0408***	−0.0413***	−0.0351***	−0.0261***	−0.0220***	−0.0193***	6.65 [0.0000]
Japan	0.0039	0.0156	−0.0063	−0.0084*	−0.0082*	−0.0075*	−0.0051*	0.0006	0.0009	0.0002	−0.0013*	0.77 [0.6547]
UK	−5.8716***	−5.7361***	−5.2243***	−5.1449***	−5.0436***	−5.0415***	−4.9331***	−4.8439***	−4.8017***	−4.4973***	−3.8969***	2.90 [0.0020]
US	−0.0802***	−0.0904***	−0.0912***	−0.0901***	−0.0904***	−0.0762***	−0.0564***	−0.0391***	−0.0087	0.0017	0.0036	26.03 [0.0000]

Monetary rule: it = α0(τ) + απ(τ)πt+12 + αy(τ)yt + αepu(τ)eput, where i_t is the end of month shadow interest rate, π_t₊₁₂ is the forward looking inflation, y_t is the output gap, and epu_t is the economic policy uncertainty index. Instruments: 1–3 lags of inflation, output gap and epu. *, **, *** indicate statistical significance at 10%, 5% and 1% statistical levels, respectively. Wald statistic tests the null hypothesis of parameter equality across quantiles. IVQ regressions calculations use power series with k = 5. Figures in square brackets are p-values.

Table 5:

Backward-looking Taylor-type rule: IVQ (Lee 2007) results using 2^nd-order polynomial.

	τ
	0.05	0.10	0.20	0.30	0.40	0.50	0.60	0.70	0.80	0.90	0.95
Panel A: inflation coefficients α_π
EA	0.6978**	0.4473**	0.3162*	0.1867**	0.3488**	0.3469**	0.5015**	0.4372***	0.6058**	0.3232***	0.4689**	2.02 [0.0317]
Japan	−0.7498	−1.3574	−0.9381	−0.4288**	−0.0373**	0.2270	0.2098	0.5097	0.1833	0.0542	0.0442	0.99 [0.4534]
UK	−0.1041	0.2358**	−0.3443**	−0.4066***	−0.4695**	−0.6316	−0.4131	−0.2923	0.0544	−0.1206	−0.0682***	0.22 [0.9938]
US	0.3953	0.5544***	0.6996***	0.8303***	0.6530***	1.1338***	1.2956***	1.4050***	1.5551***	2.5220***	2.1431***	0.89 [0.5427]
Panel B: Gap coefficients α_y
EA	0.3040***	0.3088***	0.2711***	0.2684***	0.2452***	0.2370***	0.2025***	0.1896***	0.1512***	0.0933***	0.0541***	0.80 [0.6316]
Japan	−0.2670	−0.0183	0.0662***	0.0302***	0.0212**	0.0367***	0.0434***	0.0587***	0.0293**	0.0233**	0.0257***	1.98 [0.0356]
UK	0.2060***	0.4369***	0.3385**	0.2961***	0.2908***	0.2934***	0.3527***	0.3985***	0.3174***	0.2847***	0.1630***	0.79 [0.0000]
US	−0.2820	−0.2431	0.0496***	0.4184***	0.0322***	0.0321***	0.2533***	0.3740***	0.6344***	0.5869***	0.5550***	2.91 [0.0016]
Panel C: EPU coefficients α_epu
EA	−0.0898***	−0.0899***	−0.0841***	−0.0825***	−0.0810***	−0.0792***	−0.0777***	−0.0756***	−0.0750***	−0.0691***	−0.0730***	15.80 [0.0000]
Japan	−0.0780***	−0.0731***	−0.0643***	−0.0712***	−0.0603***	−0.0438***	−0.0331***	0.0073***	−0.0015***	0.0025***	−0.0037***	0.97 [0.4696]
UK	−6.0397***	−5.3121***	−4.9415***	−4.7365***	−4.4479***	−4.1389***	−3.8003***	−3.3262***	−3.2612***	−3.3176***	−3.8631***	2.60 [0.0053]
US	−0.0803***	−0.0903***	−0.0902***	−0.0938***	−0.0890***	−0.0810***	−0.0678***	−0.0491***	−0.0282***	−0.0033***	−0.0004***	8.97 [0.0000]

Monetary rule: it = α0(τ) + απ(τ)πt−12 + αy(τ)yt + αepu(τ)eput, where i_t is the end of month shadow interest rate, π_t_–12 is the backward looking inflation, y_t is the output gap, and epu_t is the economic policy uncertainty index. Instruments: 1–3 lags of inflation, output gap and epu. *, **, *** indicate statistical significance at 10%, 5% and 1% statistical levels, respectively. Wald statistic tests the null hypothesis of parameter equality across quantiles. IVQ regressions calculations use power series with k = 2. Figures in square brackets are p-values.

Table 6:

Contemporaneous Taylor-type rule: IVQ (Lee 2007) results using 2^nd-order power series.

	τ											Wald test
	0.05	0.10	0.20	0.30	0.40	0.50	0.60	0.70	0.80	0.90	0.95
Panel A: Inflation coefficients α_π
EA	0.0983**	0.6857**	0.6691*	0.4013**	0.1192**	0.0211**	0.0334**	0.3481***	0.2995**	0.2288***	0.0231**	0.68 [0.7442]
Japan	−0.0976	−0.5565	−0.7669	−0.7158**	−0.5436**	−0.2532	0.1621	0.2267	0.0376	0.0231	0.0754	0.91 [0.5233]
UK	0.03831	0.1723**	0.0583**	−0.2631***	−0.3839**	−0.1409	0.2816	0.6047	0.1386	−0.0481	0.1586***	0.33 [0.9726]
US	1.6387	0.7714***	1.1919***	0.8855***	0.6120***	0.7107***	0.9847***	1.2719***	2.3995***	1.8278***	2.3486***	0.78 [0.6480]
Panel B: Gap coefficients α_y
EA	0.1831***	0.0797***	0.1898***	0.2442***	0.2368***	0.2952***	0.2356***	0.2144***	0.1834***	0.2292***	0.2079***	1.64 [0.0950]
Japan	0.2032	0.0893	0.1860***	0.0878***	0.0663**	0.0121***	0.0681***	0.0321***	0.0246**	0.0179**	0.0079***	2.08 [0.0266]
UK	0.2649***	0.3495***	0.3248**	0.3175***	0.2795***	0.2485***	0.3199***	0.3908***	0.3516***	0.2984***	0.1347***	0.74 [0.6890]
US	−0.3247	−0.2405	0.0368***	0.0101***	0.0220***	0.0677***	0.3109***	0.4347***	0.3981***	0.5660***	0.6702***	3.87 [0.0001]
Panel C: EPU coefficients α_epu
EA	−0.0815***	−0.0778***	−0.0731***	−0.0582***	−0.0458***	−0.0448***	−0.0431***	−0.0367***	−0.0282***	−0.0215***	−0.0210***	14.04 [0.0000]
Japan	−0.0101***	−0.0135***	−0.0226***	−0.0096***	−0.0093***	−0.0103***	−0.0047***	0.0017***	−0.0001***	0.0003***	−0.0015***	0.72 [0.7026]
UK	−5.7669***	−5.2337***	−4.9601***	−4.8114***	−4.5953***	−4.3952***	−4.0064***	−3.5966***	−3.6440***	−3.7882***	−3.8795***	4.25 [0.0000]
US	−0.0791***	−0.0814***	−0.0839***	−0.0882***	−0.0894***	−0.0794***	−0.0506***	−0.0368***	−0.0069***	0.0089***	0.0180***	13.09 [0.0000]

Monetary rule: it = α0(τ) + απ(τ)πt + αy(τ)yt + αepu(τ)eput, where i_t is the end of month shadow interest rate, π_t is the inflation, y_t is the output gap, and epu_t is the economic policy uncertainty index. Instruments: 1–3 lags of inflation, output gap and epu. *, **, *** indicate statistical significance at 10%, 5% and 1% statistical levels, respectively. Wald statistic tests the null hypothesis of parameter equality across qantiles. IVQ regressions calculations use power series with k = 2. Figures in square brackets are p-values.

Table 7:

Forward looking Taylor-type rule with annualised inflation: IVQ (Lee 2007) results using 2^nd-order power series.

	τ											Wald test
	0.05	0.10	0.20	0.30	0.40	0.50	0.60	0.70	0.80	0.90	0.95
Panel A: Inflation coefficients α_π
EA	0.7739***	1.2431***	1.2799***	0.7421***	0.5484**	0.3304*	0.0863	0.0739	0.1374	0.2447*	0.2620**	1.94 [0.0410]
Japan	−0.7488	−0.3195	−0.5923***	−0.6272***	−0.4285***	−0.4297***	−0.3278**	0.0928	0.0499	0.0483	0.0553	0.74 [0.6885]
UK	−0.1516	−0.0541	−0.1021	−0.0586	−0.0177	−0.0926	−0.1121	0.1828	−0.4259**	−0.9451***	−1.0793***	0.59 [0.4497]
US	1.0465***	0.9584***	1.1359***	1.3528***	1.5158***	1.5006***	1.1580***	1.0909***	1.1112***	1.1219***	1.0888***	1.79 [0.0609]
Panel B: Gap coefficients α_y
EA	−0.0622	−0.0328	0.0714	0.1642*	0.2028**	0.2745***	0.2753***	0.2289***	0.1914***	0.2090***	0.2149***	1.83 [0.0561]
Japan	0.2392	0.2033	0.1227**	0.1002*	0.0692	0.0058	0.0331	0.0555**	0.0254*	0.0182*	0.0200**	1.69 [0.0822]
UK	0.0404	0.2817*	0.1834*	0.2621***	0.2103***	0.2030***	0.2425***	0.3086***	0.4397***	0.4334***	0.4385***	1.15 [0.3239]
US	0.0158	−0.1093	−0.2190*	−0.2168*	−0.1083	0.0671	0.2200**	0.3137***	0.3598***	0.3888***	0.1675*	9.39 [0.0000]
Panel C: EPU coefficients α_epu
EA	−0.0625***	−0.0434***	−0.0343***	−0.0388***	−0.0379***	−0.0432***	−0.0423***	−0.0363***	−0.0279***	−0.0215***	−0.0199***	9.08 [0.0000]
Japan	0.0060	0.0056	−0.0105*	−0.0092*	−0.0088*	−0.0094*	−0.0109*	0.0035	0.0006	−0.0001	−0.0000	0.80 [0.6256]
UK	−5.7971***	−5.4694***	−5.0885***	−4.9765***	−4.9029***	−4.6028***	−4.5861***	−4.4474***	−3.3961***	−3.3852***	−3.4977***	3.21 [0.0007]
US	−0.0671***	−0.0734***	−0.0769***	−0.0780***	−0.0784***	−0.0706***	−0.0586***	−0.0473***	−0.0400***	−0.0289***	−0.0016***	20.66 [0.0000]

Monetary rule: it = α0(τ) + απ(τ)πt+12 + αy(τ)yt + αepu(τ)eput, where i_t is the end of month shadow interest rate, π_t₊₁₂ is the forward looking annualised inflation, y_t is the output gap, and epu_t is the economic policy uncertainty index. Instruments: 1–3 lags of inflation, output gap and epu. *, **, *** indicate statistical significance at 10%, 5% and 1% statistical levels, respectively. Wald statistic tests the null hypothesis of parameter equality across quantiles. IVQ regressions calculations use power series with k = 5. Figures in square brackets are p-values.

References

Agnello, L., V. Castro, G. Dufrénot, F. Jawadi, and R. M. Sousa. 2018. Unconventional Monetary Reaction Functions. University of Minho, Mimeo.Search in Google Scholar

Baker, S., N. Bloom, and S. Davis. 2016. “Measuring Economic Policy Uncertainty.” The Quarterly Journal of Economics 131 (4): 1593–1636.10.3386/w21633Search in Google Scholar

Bernanke, Ben S., and Vincent R. Reinhart. 2004. “Conducting Monetary Policy at Very Low Short-Term Interest Rates.” American Economic Review 94: 85–90.10.1257/0002828041302118Search in Google Scholar

Blinder, I. S. 1999. Central Banking in Theory and Practice. 1st ed. Cambridge, MA: The MIT Press.Search in Google Scholar

Bloom, N. 2009. “The Impact of Uncertainty Shocks.” Econometrica 77: 623–685.10.3386/w13385Search in Google Scholar

Brainard, W. 1967. “Uncertainty and the Effectiveness of Policy.” American Economic Review 58 (2): 411–425.Search in Google Scholar

Caggiano, G., E. Castelnuovo, and G. Nodari. 2018. “Risk Management-Driven Policy Rate Gap.” CESifo Working Papers, no. 7177.10.2139/ssrn.3228337Search in Google Scholar

Castro, V., and R. M. Sousa. 2012. “How do Central Banks React to Wealth Composition and Asset Prices?.” Economic Modelling 29 (3): 641–653.10.1016/j.econmod.2012.01.006Search in Google Scholar

Chen, J. 2015. “Factor Instrumental Variable Quantile Regression.” Studies in Nonlinear Dynamics & Econometrics 19 (1): 71–92.10.1515/snde-2013-0014Search in Google Scholar

Chen, J., and M. Kashiwagi. 2017. “The Japanese Taylor Rule Estimated using Censored Quantile Regressions.” Empirical Economics 52 (1): 357–371.10.1007/s00181-016-1074-8Search in Google Scholar

Chevapatrakul, T., T. Kim, and P. Mizen. 2009. “The Taylor Principle and Monetary Policy Approaching a Zero Bound on Nominal Rates: Quantile Regression Results for the United States and Japan.” Journal of Money, Credit and Banking 41: 1705–1723.10.1111/j.1538-4616.2009.00269.xSearch in Google Scholar

Christou, C., R. Naraidoo, R. Gupta, and W-J. Kim. Forthcoming. Monetary Policy Reaction Functions of the TICKs: A Quantile Regression Approach. Emerging Markets Finance and Trade. DOI: https://doi.org/10.1080/1540496X.2017.1422429.10.1080/1540496X.2017.1422429Search in Google Scholar

Carlstrom, C., and T. Fuerst. 2000. Forward-looking versus backward-looking Taylor rules. Working Paper No. 009, Federal Reserve Bank of Cleveland.10.26509/frbc-wp-200009Search in Google Scholar

Clarida, R., J. Gali, and M. Gertler. 2000. “Monetary Policy Rules and Macroeconomic Stability: Evidence and Some Theory.” Quarterly Journal of Economics 115 (1): 147–180.10.3386/w6442Search in Google Scholar

Colombo, V. 2013. “Economic Policy Uncertainty in the US: Does it Matter for the Euro Area?” Economics Letters 121: 39–42.10.1016/j.econlet.2013.06.024Search in Google Scholar

Conley, T., and D. Galenson. 1998. “Nativity and Wealth in Mid-Nineteenth-Century Cities.” Journal of Economic History 58 (2): 468–493.10.1017/S0022050700020581Search in Google Scholar

Cukierman, A., and S. Gerlach. 2003. “The Inflation Bias Revisited; Theory and Some International Evidence.” The Manchester School 71 (5): 541–565.10.1111/1467-9957.00366Search in Google Scholar

Dolado, J., R. María Dolores, and F. J. Ruge-Murcia. 2004. “Non-Linear Monetary Policy Rules: Some New Evidence for the US.” Studies in Nonlinear Dynamics and Econometrics 8: article 2.10.2202/1558-3708.1155Search in Google Scholar

Dolado, J., María-Dolores, R., and M. Naveira. 2005. “Are Monetary- Policy Reaction Functions Asymmetric ?: The Role of Nonlinearity in the Phillips Curve.” European Economic Review 49: 485–503.10.1016/S0014-2921(03)00032-1Search in Google Scholar

Ehrmann, M., and F. Smets. 2003. “Uncertain Potential Output: Implications for Monetary Policy.” Journal of Economic Dynamics and Control 27 (9): 1611–1638.10.1016/S0165-1889(02)00073-8Search in Google Scholar

Estrella, A., and F. S. Mishkin. 2000. “Rethinking the Role of NAIRU in Monetary Policy: Implications of Model Formulation and Uncertainty.” National Bureau of Economic Research Working Paper, No. 6518.10.3386/w6518Search in Google Scholar

Evans, C., J. D. M. Fisher, F. Gourio, and S. Krane. 2015. “Risk Management for Monetary Policy Near the Zero Lower Bound, Brookings Papers on Economic Activity,” Spring, 141–196.10.2139/ssrn.2644142Search in Google Scholar

Firpo, S., N. M. Fortin, and T. Lemieux. 2009. “Unconditional Quantile Regressions.” Econometrica 77: 953–973.10.3386/t0339Search in Google Scholar

Giannoni, M. P. 2002. “Does Model Uncertainty Justify Caution? Robust Optimal Monetary Policy in a Forward Looking Model.” Macroeconomic Dynamics 6 (6): 111–144.10.1017/S1365100502027062Search in Google Scholar

Gosling, A., S. Machin, and C. Meghir. 2000. “The Changing Distribution of Male Wages in the UK.” Review of Economic Studies 67 (4): 635–666.10.1111/1467-937X.00148Search in Google Scholar

Greenspan, A. 2003. “Opening Remarks,” Speech at the Symposium on Monetary Policy and Uncertainty: Adapting to a Changing Economy, Jackson Hole, Wyoming, August.Search in Google Scholar

Gupta, R., C.-K.-M. Lau, and M. E. Wohar. 2018. “The Impact of US Uncertainty on the Euro Area in Good and Bad Times: Evidence from a Quantile Structural Vector Autoregressive Model.” Empirica. DOI: https://doi.org/10.1007/s10663-018-9400-3.10.1007/s10663-018-9400-3Search in Google Scholar

Gupta, R., J. Ma, M. Risse, and M. E. Wohar. Forthcoming. “Common Business Cycles and Volatilities in US States and MSAs: The Role of Economic Uncertainty.” Journal of Macroeconomics.10.1016/j.jmacro.2018.06.009Search in Google Scholar

Hayashi, F. 2000. Econometrics. 1st ed. Princeton, NJ: Princeton University Press.Search in Google Scholar

Hansen, L. P. 1982. “Large Sample Properties of Generalized Method of Moments Estimators.” Econometrica 50: 1029–1054.10.2307/1912775Search in Google Scholar

Jawadi, F., and R. M. Sousa. 2013. “Money Demand in the Euro Area, the US and the UK: Assessing the Role of Nonlinearity.” Economic Modelling 32: 507–515.10.1016/j.econmod.2013.02.009Search in Google Scholar

Jawadi, F., and R. M. Sousa. 2014. “Modelling the Relationship between Consumption and Wealth in the US, the UK and the Euro Area.” Revue d’ Economie Politique 124 (4): 639–652.10.3917/redp.244.0639Search in Google Scholar

Jawadi, F., S. K. Mallick, and R. M. Sousa. 2014. “Nonlinear Monetary Policy Reaction Functions in Large Emerging Economies: The Case of Brazil and China.” Applied Economics 46 (9): 973–984.10.1080/00036846.2013.851774Search in Google Scholar

Jawadi, F., R. M. Sousa, and R. Traverso. 2017. “On the Macroeconomic Impact and the Wealth Effects of Unconventional Monetary Policy.” Macroeconomic Dynamics 21 (5): 1189–1204.10.1017/S1365100515000292Search in Google Scholar

Jones, P. M., and E. Olson. 2015. “The International Effects of US Uncertainty.” International Journal of Finance and Economics 20: 242–252.10.1002/ijfe.1513Search in Google Scholar

Jurado, K., S. C. Ludvigson, and S. Ng. 2015. “Measuring Uncertainty.” The American Economic Review 105 (3): 1177–1215.10.3386/w19456Search in Google Scholar

Kohn, Donald L. 1996. “Commentary on Charles Freeman: What Operating Procedures Should Be Adopted to Maintain Price Stability? Practical Issues” 1996 Symposium Proceedings: Achieving Price Stability, Federal Reserve Bank of Kansas City, Jackson Hole, Wyoming.Search in Google Scholar

Krippner, L. 2013. “A Tractable Framework for Zero Lower Bound Gaussian Term Structure Models.” Discussion Paper, Reserve Bank of New Zealand, 2013/02.10.2139/ssrn.2310990Search in Google Scholar

Kuttner, Kenneth N., and Adam S. Posen. 2001. “The Great Recession: Lessons from Macroeconomic Policy from Japan.” Brookings Papers on Economic Activity 32: 93–186.10.1353/eca.2001.0019Search in Google Scholar

Lee, S. 2007. “Endogeneity in Quantile Regression Models: A Control Function Approach.” Journal of Econometrics 141: 1131–1158.10.1920/wp.cem.2004.0804Search in Google Scholar

Lee, J. H. 2016. “Predictive Quantile Regression with Persistent Covariates: IVX-QR Approach.” Journal of Econometrics 192: 105–118.10.1016/j.jeconom.2015.04.003Search in Google Scholar

Leon Li, M.-F., and M.-Y. Yen. 2011. “Reexamining Dynamic Covariance Risk in Global Stock Markets using Quantile Regression Analysis.” Acta Oeconomica 61: 33–59.10.1556/aoecon.61.2011.1.3Search in Google Scholar

Liu, X. 2018. “How is the Taylor Rule Distributed under Endogenous Monetary Regimes?” International Review of Finance 18 (2): 305–316.10.1111/irfi.12131Search in Google Scholar

Ma, J., E. Olson, and M. E. Wohar. 2018. “Nonlinear Taylor Rules: Evidence from a Large Dataset.” Studies in Nonlinear Dynamics & Econometrics 22 (1): 1–14.10.1515/snde-2016-0082Search in Google Scholar

Machado, J. A. F., and J. Sousa. 2005. “Asset Prices and Macroeconomic Fundamentals in the Euro Area.” Banco de Portugal Economic Bulletin Autumn: 1–6.Search in Google Scholar

Martin, C., and C. Costas Milas. 2009. “Uncertainty and Monetary Policy Rules In The United States.” Economic Inquiry 47 (2): 206–215.10.1111/j.1465-7295.2008.00160.xSearch in Google Scholar

Miles, W., and S. Schreyer. 2012. “Is Monetary Policy Non-Linear in Indonesia, Korea, Malaysia, and Thailand? A Quantile Regression Analysis.” Asian-Pacific Economic Literature 26: 155–166.10.1111/j.1467-8411.2012.01344.xSearch in Google Scholar

Miles, W., and S. Schreyer. 2014. “Is Monetary Policy Non-Linear in Latin America? A Quantile Regression Approach to Brazil, Chile, Mexico and Peru.” Journal of Developing Areas 48 (2): 169–183.10.1353/jda.2014.0029Search in Google Scholar

Mishkin, F. S. 2010. “Monetary Policy Flexibility, Risk Management, and Financial Disruptions.” Journal of Asian Economics 21 (3): 242–246.10.1016/j.asieco.2009.07.005Search in Google Scholar

Naraidoo, R., and R. Raputsoane. 2015. “Financial Markets and the Response of Monetary Policy to Uncertainty in South Africa.” Empirical Economics 49 (1): 255–278.10.1007/s00181-014-0866-ySearch in Google Scholar

Orphanides, A., R. D. Porter, D. Reifschneider, R. Tetlow, and F. Finan. 2000. “Errors in the Measurement of the Output Gap and The Design of Monetary Policy.” Journal of Economics and Business 52 (1–2): 117–141.10.1016/S0148-6195(99)00031-4Search in Google Scholar

Peersman, G., and F. Smets. 1999. “The Taylor Rule: A Useful Monetary Policy Benchmark for the Euro Area?” International Finance 2 (1): 85–116.10.1111/1468-2362.00020Search in Google Scholar

Rudebusch, G. D. 2001. “Is the Fed too Timid? Monetary Policy in an Uncertain World.” Review of Economics and Statistics 83 (2): 203–217.10.1162/00346530151143752Search in Google Scholar

Ruge-Murcia, F. 2003. “Does the Barro–Gordon Model Explain the Behavior of US Inflation? A Reexamination of the Empirical Evidence.” Journal of Monetary Economics 50 (6): 1375–1390.10.1016/S0304-3932(03)00083-7Search in Google Scholar

Sonderstrom, U. 2002. “Monetary Policy with Uncertain Parameters,” Scandinavian Journal of Economics 104 (1): 125–145.10.1111/1467-9442.00275Search in Google Scholar

Sousa, J., and R. M. Sousa. 2017. “Predicting Risk Premium Under Changes in the Conditional Distribution of Stock Returns.” Journal of International Financial Markets, Institutions & Money 50: 204–218.10.1016/j.intfin.2017.09.002Search in Google Scholar

Surico, P. 2007. “The Fed’s Monetary Policy Rule and US Inflation: The Case of Asymmetric Preferences.” Journal of Economic Dynamics and Control 31: 305–324.10.1016/j.jedc.2005.11.001Search in Google Scholar

Svensson, L. E. O. 1999. “Inflation Targeting as a Monetary Policy Rule.” Journal of Monetary Economics 43 (3): 607–654.10.3386/w6790Search in Google Scholar

Taylor, J. B. 1993. “Discretion versus Policy Rules in Practice.” Carnegie-Rochester Conference Series on Public Policy 39 (1): 195–214.10.1016/0167-2231(93)90009-LSearch in Google Scholar

Taylor, J. 1999. “A Quantile Regression Approach to Estimating the Distribution of Multiperiod Returns.” Journal of Derivatives 7 (1): 64–78.10.3905/jod.1999.319106Search in Google Scholar

Wolters, M. 2012. “Estimating Monetary Policy Reaction Functions using Quantile Regression.” Journal of Macroeconomics 34: 342–361.10.1016/j.jmacro.2011.12.004Search in Google Scholar

Wu, J. C., and F. D. Xia. 2016. “Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound.” Journal of Money, Credit and Banking 48 (2–3): 253–291.10.3386/w20117Search in Google Scholar

Supplementary Material

The online version of this article offers supplementary material (DOI: https://doi.org/10.1515/snde-2018-0056).

Published Online: 2019-12-16

You are currently not able to access this content.

Supplementary Material Details

Articles in the same Issue

https://doi.org/10.1515/snde-2018-0056

Keywords for this article

advanced economies; conditional and unconditional quantile regressions; interest rate rule; shadow rate of interest; uncertainty; zero lower bound