Abstract
The effect of external Quantitative Easing (QE) on a small open economy like India is analyzed using a dynamic stochastic general equilibrium (DSGE) model. The modeling is motivated by some broad empirical regularities of the Indian economy during the pre and post-QE periods . QE is modeled as a negative shock to the short term foreign policy rate with a mean reverting pattern. The mean reversion reflects the phasing out of the QE operation. In addition, we analyze the “news” effect of the tapering out phase of QE. Our model has standard real and nominal frictions as in any New Keynesian model. Monetary policy is modeled by the forward looking inflation targeting Taylor rule . We show that the impact and news effects of QE work through this terms of trade via the uncovered interest parity condition. Using our DSGE model, we also compare the effect of a QE shock with a domestic fiscal spending shock. The model impulse response functions qualitatively support some key empirical regularities of the Indian economy during the QE era.
This work is an extension of the NCAER project (working paper no 109) generously funded by the Think Tank Initiative of the Canadian International Development Research Center. Yongdae Lee and Ajaya Sahoo are gratefully acknowledged for their competent and timely research assistance.
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Notes
- 1.
By the end of phase 1 of QE, the Fed had injected $2.1 trillion into the US economy. During November 2010, Fed started the second round with a purchase of $600 billion worth of Treasury securities along with an added investment of $250–300 billion in treasuries from the profits of previous investments. In phase 3, the initial budget was $40 billion/month, which was raised to $85 billion by December 2012. Source www.macroeconomicanalysis.com.
- 2.
One may still debate about the validity of UIP condition in the context of India due to the prevalence of currency and capital controls. However, these controls already started loosening with the advent of financial reforms in India. Given that the focus of our paper is on QE and post-QE era when financial and currency reforms are in full swing in India, UIP is a reasonable approximation which is further reinforced by the data support that we provide in the paper.
- 3.
Note that we refer the years as the fiscal years in India. A fiscal year starts from the month of April of a particular year to the end of March of the forthcoming year. So, when we mention our sample period as 2004–2005:Q4 to 2014–2015:Q2, it implies that the observations are taken from January 2005 to September 2014.
- 4.
Data sources of the respective macroeconomic variables are provided in Data Appendix. All series are available at a quarterly frequency. Nominal series were converted to real using the GDP deflator with 2004–2005 as the base year.
- 5.
- 6.
- 7.
The terms of trade is measured as the ratio of unit value of importable to unit value of exportable.
- 8.
Source RBI Speeches–https://www.rbi.org.in/Scripts/BS_ViewSpeeches.aspx.
- 9.
We attempt to obtain maximum possible number of observations in a balanced sample for all the variables (i.e., for RBI repo rate, Federal Fund’s rate, and nominal exchange rate) and it is possible to trace back up to second quarter of 2000–2001. The business cycle component is extracted from policy rate differential and exchange rate depreciation using Christiano–Fitzgerald asymmetric filter for the periodicity between 6 to 32 quarters. The statistical significance of correlation coefficients are assigned by ‘*’ for 10 %, ‘**’ for 5 % and ‘***’ for 1 % level.
- 10.
Similar conditions hold for foreign producers.
- 11.
Such a pricing behavior of exportable is validated by the widespread pricing-to-market behavior.
- 12.
To see how one gets (38), use the fact that \(\frac{P_{H,t}^{*}}{ P_{t}^{*}}=\frac{\xi _{t}P_{H,t}^{*}}{P_{t}}.rx_{t}^{-1}\) and \(\frac{ P_{H,t}^{*}}{P_{x,t}^{*}}=\frac{\xi _{t}P_{H,t}^{*}}{P_{t}} .rx_{t}^{-1}.\frac{P_{t}^{*}}{P_{xt}^{*}}\). Next note that \(\frac{ P_{x,t}^{*}}{P_{t}^{*}}=\frac{\left[ \varphi ^{*}+(1-\varphi ^{*})(P_{F,t}^{*}/P_{H,t}^{*})^{1-\tau ^{*}}\right] ^{1/(1-\tau ^{*})}}{\left[ \nu ^{*}+(1-\nu ^{*})(P_{F,t}^{*}/P_{H,t}^{*})^{1-\theta ^{*}}\right] ^{1/(1-\theta ^{*})}}. \frac{1}{Z_{x,t}^{*}}.\) In our calibration we assume that \(\tau ^{*}=\theta ^{*}\) and \(\nu ^{*}=\varphi ^{*}\) as the baseline, which means \(\frac{P_{x,t}^{*}}{P_{t}^{*}}=\frac{1}{Z_{x,t}^{*}}\) where \(Z_{x,t}^{*}\) is the foreign IST shock which we assume away by normalizing to unity.
- 13.
Details of the derivation of (44) are available from the authors upon request.
- 14.
In a world of law of one price (LOOP), the foreign terms of trade is identical to home terms of trade and thus, the export price setting equation becomes redundant. However, we do not assume LOOP in our model as in Kollmann (2002).
- 15.
The details of the derivation of (48) are available upon request from the authors.
- 16.
The second author is grateful to Yongdae Lee to point out this useful identity.
- 17.
We follow Kollmann (2002) in formulating the interest rate rule as a function of \(\widehat{y}_{H,t}\) (which is the deviation of \(y_{Ht}\) from the steady state). \(\widehat{y}_{H,t}\) is analogous to output gap in a standard new Keynesian model.
- 18.
One may debate whether during the QE phase, the RBI explicitly followed the lead given by major player such as Federal Reserve or ECB in formulating its own monetary policy. There is no clear evidence that it was the case. In fact data (as seen Fig. 2) suggest that Indian Repo rate diverged from US Federal Funds rate since 2009 which is contrary to such a posited leader–follower relationship. We thus assume that the home central bank basically follows a traditional forward looking Taylor rule.
- 19.
An eight-quarter lag is arbitrarily chosen assuming that the market foresees the tapering of QE about 2 years ahead. Changing this lag to four quarters makes no difference to the impulse response analysis except that the actual taper materializes after four quarters instead of eight quarters.
- 20.
Setting a higher steady-state inflation rate for the home country does not change the impulse response properties of the model.
- 21.
As in any standard DSGE model, the impulse response analysis depends on the choice of parameters values. The key model parameter values are carefully chosen in line with the extant literature. Some parameter values (e.g., \(\lambda \)) are not available in the Indian context. We performed some sensitivity analysis (e.g., alternative home biases in consumption and investment) and found that the key impulse results are reasonably robust. The details of this sensitivity analysis is not presented for the sake of brevity.
- 22.
We use the term volatility in the sense of sudden change in the track. Evidently, we are not addressing issue of volatility spillover which requires serious modeling of the second-order effects of QE shocks. This is beyond the scope of this chapter.
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Banerjee, S., Basu, P. (2016). Indian Economy During the Era of Quantitative Easing: A Dynamic Stochastic General Equilibrium Perspective. In: Ghate, C., Kletzer, K. (eds) Monetary Policy in India. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2840-0_17
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