Central Bank Communications and Professional Forecasts: Evidence From India

Abstract

We analyze the influence of qualitative and quantitative communications of the Reserve Bank of India (RBI) on inflation expectations of professional forecasters and draw out implications for policy. Estimating Carroll-type epidemiological models of expectation formation under information rigidities, we get a large speed of adjustment of professional forecasters’ expectations. Analysis of the determinants of inflation forecasts, inflation surprises, and forecaster disagreement reveals significant influence of quantitative RBI communications in the form of inflation projections. This effect is prominent for shorter-horizon forecasts and after adoption of flexible inflation targeting. Macroeconomic fundamentals like lagged inflation and repo rate also significantly influence inflation forecasts. Choice of words in the RBI monetary policy statements has more impact after October 2016, when the monetary policy committee became the decision-making body.

JEL Classification: E31, E52, E58

Keywords

Inflation expectations survey of professional forecasters central bank communications text analysis

1. Introduction

Private sector expectations influence various macroeconomic aggregates. Management of private-sector expectations via central bank communications and actions is important for monetary policymaking (Hubert, 2015b, 2017). The expectations channel is subtle as it depends on private agents’ interpretation of central bank actions and communications. Given lags in monetary policy, communications that influence expectations provide a way to shorten these transmission lags. Most papers in the communications literature study advanced economies (AEs). Studies on emerging market economies (EMEs) using survey-based private sector expectations, in particular, are scarce. Analysis of EMEs with thin information and underdeveloped financial markets can add new insights to the communications literature (Goyal, 2017).

With this objective, we investigate the effects of qualitative and quantitative Reserve Bank of India (RBI) communications on survey-based inflation expectations of professional forecasters (SPF). The analysis, broken into two periods due to the change in the frequency of the SPF survey from quarterly to bi-monthly in March 2014, coincides with a shift in the monetary policy regime to flexible inflation targeting (FIT) from the erstwhile multiple indicator approach, which gives an opportunity to assess the impact of change in communication strategy.¹ RBI inflation forecasts (projections) are used to analyze quantitative aspects of communications, while sentiment scores estimated using text analysis techniques are used to examine qualitative aspects. We focus on India because the newly adopted FIT regime and setting up of a monetary policy committee (MPC) and a consequent change in communication is an opportunity to assess the communications channel of monetary policy transmission. The results will be useful for other countries that are going through or planning such a transition.

Preliminary analysis tests the response of SPF forecasts to RBI projections using epidemiological models of expectations formation under information rigidities (Carroll, 2003), where news is absorbed slowly through a learning process. The rationales for choosing these models are: first, they analyze the expectations formation process of economic agents in response to news in the presence of information processing constraints, which are dominant in EMEs. Second, they provide a realistic alternative to the rational expectations hypothesis by modeling the deviations from full-information rational expectations (FIREs). Third, they explain the inflation-unemployment trade-off better than the rational expectations-based models (Carroll, 2003). Fourth, there are other models with information-processing constraints, such as noisy-information models (Woodford, 2002) and rational inattention models (Mackowiak & Wiederholt, 2009; Sims, 2003). But the epidemiological model has similar results to these other models while explicitly modeling learning and the diffusion of news.²

We find high adjustment speeds of SPF forecasts to news in RBI inflation projections, using models that capture learning through the diffusion of information. To further examine the effect of RBI qualitative and quantitative communications on SPF forecasts, we estimate determinants of three variables capturing different aspects of SPF forecasts: forecast levels, surprises in SPF inflation forecasts, and disagreements across forecasters. Forecast levels, measured using the median inflation expectations of professional forecasters, summarize their consensus view on the inflation outlook. The impact of central bank inflation forecasts on these, throws light on the anchoring of expectations. However, median expectations mask the underlying differences across individual forecasters. Forecaster disagreements provide insights into the possible inflation risks forecasters perceive (Glick & Kouchekinia, 2021). Finally, inflation surprises, or forecast deviations from realized inflation values, indicate forecast accuracy.

RBI projections, oil prices, and lagged inflation emerge as significant drivers of levels of SPF forecasts. Oil prices, which were high and volatile before 2014, influence quarterly SPF forecasts. Lagged inflation has positive and significant effects on forecast levels for both quarterly and bi-monthly surveys. Quantitative communications drive SPF forecasts, predominantly shorter-horizon ones. Qualitative communications and rate cycle do not significantly influence SPF forecasts.

RBI projection surprises mainly drive shorter-horizon SPF inflation surprises. A rise in the repo rate reduces inflation surprises pre- and post-FIT. Lagged inflation, oil prices, and qualitative communications are insignificant both pre- and post-FIT.

Analysis of forecaster disagreement conveys a significant positive influence of RBI projections post-FIT. Qualitative communication fails to significantly affect forecaster disagreement in both periods. However, analysis using interaction dummies for the bi-monthly surveys show significant improvement in the influence of qualitative communications on dispersions after the MPC became the decision-making body of the RBI in October 2016, indicating gradual improvement in the choice of words in monetary policy statements. A positive movement in the rate cycle increases forecaster disagreements in 1-year-ahead forecasts for quarterly surveys. This finding contrasts with theory, but can be due to these periods coinciding with higher uncertainty, especially after the global financial crisis. High uncertainty and slower policy normalization, in addition to high food and fuel inflation, may have increased forecaster disagreements.

This study contributes in many ways to a growing yet sparse literature on the expectations channel of monetary policy transmission in EMEs in the presence of information-processing constraints that affect expectations formation. It also highlights the significance of central bank communications to manage inflation expectations and moderate short-term volatility.

The remainder of the study is structured as follows: Section 2 provides a brief review of the literature followed by the descriptive analysis of data in Section 3. Section 4 gives results for Carroll-type epidemiological model-based estimations. Section 5 provides methodology and empirical analysis followed by discussion in Section 6. Section 7 concludes the analysis.

2. Brief Review of Literature

Literature examines inflation expectations formation, its properties, and the influence of news and communication variables on professional forecasters. Many theoretical models of neo-classical and new Keynesian schools’ model inflation expectations of economic agents assuming FIREs. Since the late 1990s, however, many economists have modeled information rigidities that cause deviations from FIREs. We divide the reviewed literature based on the models of the diffusion of central bank communications as news (epidemiological models), methods of estimating central bank communications (qualitative and quantitative), and the effects of central bank communications on various macroeconomic indicators and expectations.

2.1. Epidemiological Models of Expectations Formation

Expectations formation of households has been studied primarily after the seminal work of Mankiw and Reis (2002). They develop a sticky information (SI) model of inflation, in which information about macroeconomic conditions diffuses slowly to agents and only a fraction of the population updates itself each period about the current state of the economy. Carroll (2003) estimated these SI models using epidemiological models to track the information flow through the population and model inflation expectations of economic agents. He suggests that agents’ expectations depend upon how the information spreads through the population.

Most studies on epidemiological models use household expectations as the non-rational dependent variable and professional forecasts as the news variable. Since most of these studies focus on AEs, they assume the inherent rationality of the professional forecasters. However, Lei et al. (2015) analyze inflation expectations formation of professional forecasters in China along with that of the households. Their findings support updating of inflation expectations of households and professional forecasters to the news received. They find that professional forecasters’ expectations move considerably due to politically motivated news. Therefore, we estimate the response of SPF to the news.

2.2. Measuring Central Bank Communications

Central bank communications are primarily classified into two categories: quantitative and qualitative. Quantitative communications include numerical forecasts for macroeconomic variables like inflation, output growth, interest rates, etc., across different forecast horizons. Ehrmann et al. (2012), Hubert (2014), Hubert (2015a, 2015b), and Kotlowski (2015) use central bank forecasts as a measure of central bank communications. Hubert (2017) uses both qualitative and quantitative techniques to determine the communications measures for the European Central Bank (ECB).

Literature focuses primarily on qualitative communications in the form of central bank statements, minutes, or speeches (Blinder et al., 2008). Different methods are used to quantify central bank communications. The most commonly used ones are manual coding of stances of forward guidance, sentiment score using dictionary methods, and topic modeling using Latent Semantic Analysis (LSA) and Latent Dirichlet Analysis (LDA).³

Manual coding of statements ranges from −1 (dovish) to +1 (hawkish). Some studies code the statements ranging from −2 to +2 or −3 to +3. Earlier studies on central bank communications code the stances manually. Romer and Romer’s (1989) study was the first paper to incorporate this technique for the United States. This technique was followed by a series of papers for the Euro Area (Ehrmann & Fratzscher, 2007; Hubert, 2017; Rosa & Verga, 2007).

The development of coding software and the evolution of machine learning, especially since the beginning of the 2010s decade, led to the use of advanced techniques for quantifying textual content. Apel and Blix-Grimaldi (2012) use the dictionary method to code the sentiments of the Swedish central bank (Sveriges Riksbank) by formulating a dictionary specifically for central banks. Hubert and Labondance (2018) used this dictionary to quantify the Federal Open Market Committee (United States) and the ECB statements.

Studies also use advanced topic modeling techniques to quantify central bank communications. While Moniz and De Jong (2014) use the LDA technique for the Bank of England’s MPC’s minutes of the meeting, Bruno (2017) and Arango-Thomas et al. (2020) use LSA to construct topical communication scores for the central banks of Italy and Colombia, respectively.

We apply the Apel and Blix-Grimaldi (2012) dictionary method for constructing a sentiment score, adapting it for differences in the choice of words for an advanced and an emerging economy central bank. The technique of manual coding is applicable only for statements with explicit forward guidance. RBI’s statements started providing this since the de facto adoption of FIT in 2014, allowing us to apply the technique. But a comparative analysis pre- and post-FIT is not possible. Finally, LSA and LDA are used to model-specific topics across categories rather than sentiments.

2.3. Effects of Communications on Expectations of Macroeconomic Variables

The literature finds professional forecasters’ expectations of various macroeconomic aggregates like inflation, interest rate, output growth, and exchange rates to be driven by central bank communications. While Ehrmann et al. (2012), Hubert (2014), and Kotlowski (2015) discover significant effects of quantitative communications on the forecasts of key economic variables, Ehrmann and Fratzscher (2007), Ullrich (2008), and Arango-Thomas et al. (2020) model the effects of qualitative communications on professional forecasters’ expectations.

The levels of forecasts mask the primary differences across forecasters. As a result, some studies like Montes et al. (2016) and Galvis Ciro and Anzoategui Zapata (2019) model the effects of central bank communications on cross-sectional dispersions across professional forecasters.

In the Indian context, Goyal and Arora (2012) analyze the effect of communications on the exchange rate and its volatility using dummy variable-based communications. Mathur and Sengupta (2019) examine the properties of RBI monetary policy statements across regimes of different governors.

We contribute to the scarce communications literature for developing economies: first, we estimate Carroll-type epidemiological models to assess learning under information frictions in SPF forecasts. We use the RBI’s explicit communication in their announced inflation projections as a news variable. This variable is directly under the RBI’s control. No study has done this. Second, we analyze the expectations formation of SPF forecasters by investigating the effects of central bank communications on three SPF forecast-based variables: (a) Median forecast values; (b) deviation of forecasts from the realized inflation; and (c) dispersions across forecasts. Qualitative communications are estimated using text analysis dictionary methods. None of the earlier studies model RBI communications using text analysis and machine learning techniques. A comparative study of qualitative and quantitative RBI communications gives insights for better communication strategies to manage expectations.

3. Data

3.1. Survey of Professional Forecasters

RBI conducts SPF since September 2007. Data is available since March 2008 (2008 Q1) on a quarterly basis. SPF frequency was changed to bi-monthly after the release of the Patel Committee Report (RBI, 2014), owing to the change in frequency of the RBI monetary policy meetings. The FIT regime was officially adopted in February 2015. However, FIT was implemented de facto after the release of the Patel Committee Report (RBI, 2014). Hence, the episodes of analysis are pre-FIT (March 2008 to December 2013) and post-FIT (March 2014 to November 2019).

RBI circulates the questionnaire across many institutions like investment banks, commercial banks, stock exchanges, international brokerage houses, select educational institutions, credit rating agencies, securities firms, and asset management companies. More than 25 forecasters respond every time. Surveys provide only the aggregate forecast values owing to the clause of anonymity. Forecasts are available for macroeconomic variables like headline and core inflation (both consumer price index (CPI) and wholesale price index (WPI)), exchange rate, oil prices, gross domestic product (GDP) growth rate (aggregate and sector-wise: agriculture, industries, and tertiary), index of industrial production growth rate, private final consumption expenditure growth rate, gross fixed capital formation as a percentage of GDP, imports, exports, repo rate, and corporate profits.

This study focuses on inflation forecasts by professional forecasters. Quarterly forecasts are compiled at the end of every quarter (March, June, September, and December) and formulated for four subsequent quarters. These forecasts are ‘fixed-horizon’ forecasts. They provide possible analysis through multiple time horizons and are not contaminated by varying leads (Hubert, 2015a).

Forecasts since March 2014 are bi-monthly forecasts. They are conducted in January, March, May, July, September, and November. However, forecasts are given for every quarter-end. For instance, forecasts in March and May are conducted for the same four quarters: June, September, and December of the same calendar year and March of the next calendar year. They are not fixed-horizon forecasts and hence run the risk of being contaminated by varying leads. Along with quarterly forecasts, SPF forecasters forecast for two succeeding financial year ends, that is, in July 2015, forecasters form expectations for March 2016 and March 2017. These are ‘fixed-event’ forecasts; different surveys provide forecast values for the same event (financial year end).

A major concern here is varying forecast horizons. Following Dovern et al. (2012), we approximate 1-year-ahead forecasts using weighted averages of financial year end values and 3-month-ahead forecasts using the forecast values of two quarters. Annual forecasts are approximated as follows:

{\tilde{π}}_{t + 12 | t} = \frac{k}{12} {\hat{π}}_{t + k | t} + \frac{12 - k}{12} {\hat{π}}_{t + k + 12 | t}

(1)

where k ∈ {1,3, …, 11} gives forecast horizon values at the time of the survey. For example, the November 2015 1-year-ahead forecasts are approximated using the forecast values for March 2016 and March 2017 by assigning the weights of 5/12 and 7/12 to ${\hat{π}}_{M a r, 2016 | N o v, 2015}$ and ${\hat{π}}_{M a r, 2017 | N o v, 2015}$ respectively.

Similarly, we approximate 3-month-ahead forecasts using forecast values of two adjacent quarters. Weights are assigned based on their distance from the period to be forecasted. The nearer forecast value is given weight 2/3 and the farther forecast is given the value 1/3. For example, two shorter-horizon forecasts given in November 2015 are made for December 2015 and March 2016. Three-month-ahead forecasts are formulated for February 2016. Weights are assigned based on the proximity to February 2016 forecast value. Around 2/3rd of weight is assigned to the March 2016 forecast ( ${\hat{π}}_{M a r c h, 2016 | N o v, 2015}$ ) and 1/3rd is assigned to December 2015 one ( ${\hat{π}}_{D e c, 2015 | N o v, 2015}$ ).⁴

3.2. Macroeconomic Variables

Following Easaw et al. (2013) and Ehrmann (2015), we use 1-month lagged realized inflation (π_t) as a control variable. Quarterly forecasts use WPI-based headline inflation, while bi-monthly surveys use consumer price index – combined (CPI-C)-based headline inflation, as these variables are intermediate targets during their respective periods of analyses.⁵ We control for oil price volatility by incorporating month-on-month growth rate in crude oil prices (Δ_mOIL_t).⁶ Another control variable used here is the rate cycle (RC_t), since it captures the influence of central bank actions on SPF forecasts. It is defined as follows:

R C_{t} = {\begin{matrix} - 1, I f r e p o r a t e i s r e d u c e d f r o m t h e p r e v i o u s p e r i o d \\ 0, I f r e p o r a t e i s u n c h a n g e d f r o m t h e p r e v i o u s p e r i o d \\ 1, I f r e p o r a t e i s i n c r e a s e d f r o m t h e p r e v i o u s p e r i o d \end{matrix}

3.3. Communications Variables

Monetary policy statements are important for estimating communications for three reasons: (1) they announce policy decisions; (2) they act as a focal point for all the private agents who formulate expectations about the economy in general. They also provide a detailed analysis of the current economic situation and future outlook of the economy. (3) The precise timings of monetary policy meetings make it possible to accurately identify communication effects on macroeconomic aggregates (Hubert & Labondance, 2018). Central bank communications are broadly classified as qualitative and quantitative. We use RBI inflation projections as the quantitative communications variable.

Unlike households who are informed indirectly about the RBI projections via newspapers and television media, professional forecasters directly track RBI actions and speeches. Projections appear in the RBI speeches during monetary policy meetings as follows, “… the Reserve Bank will endeavor to condition the evolution of inflation to a level of 5.0 percent by March 2014 …” (RBI, May 2013). These statements also contain fan charts of inflation and GDP growth rate for different quarters. Professional forecasters use this information while forming expectations.

Qualitative communications are estimated using the words in RBI’s monetary policy speeches. RBI Governor’s speeches had a quarterly frequency till January 2014 and bi-monthly from April 2014. SPF was changed to bi-monthly frequency following this change in the monetary policy meetings, which coincides with the de facto adoption of FIT. Quarterly RBI projections are fixed-horizon forecasts. We convert bi-monthly projections to fixed-horizon forecasts using similar data-conversion techniques to those for SPF forecasts. Data used for quarterly analysis ranges from January 2008 to January 2014 and extends from April 2014 to October 2019 for the bi-monthly analysis.

3.3.1. Construction of Qualitative Communications: Cleaning of Monetary Policy Statements

Unstructured text data from monetary policy speeches are analyzed using sophisticated text analysis techniques. Around 24 quarterly statements and 35 bi-monthly statements are used for analysis. We pre-process and clean the text files by getting rid of the unwanted information in the introductory statements and then by stripping spaces and removing punctuation marks, numbers and special characters. Unwanted words known as stopwords in linguistic terms are removed to make the text compact. The remaining words are converted to lower case for a robust analysis. Any text analysis software reads upper-case and lower-case words as two different elements (e.g., ‘Inflation’ and ‘inflation’ are considered as two different words). Conversion of all the words to lower cases eliminates the possibility of these errors. Next, a step called stemming or lemmatization uses Part-of-Speech tagging to identify stem words. This transforms all the words like ‘decrease.’, ‘decreasing’, and ‘decreases’ to the same stem word, ‘decrease. We then arrange all the words in the form of a term-document matrix (TDM). Each term forms one row of the matrix, while each column is denoted by the source document. We use the bag-of-words approach for the analysis.

3.3.2. Quantification of Monetary Policy Statements

Ehrmann and Fratzscher (2007) and Ehrmann et al. (2012) analyze qualitative communications of the European Central Bank. Their approach does not deal with the choice of words in the central bank statements. Apel and Blix-Grimaldi (2012), Hubert and Labondance (2018) estimate the tone of central bank communications using sophisticated text analytics techniques.

We construct the communications variables as follows:

τ_{i, t} = \frac{P_{i, t} - N_{i, t}}{P_{i, t} + N_{i, t}}

(2)

where τ_i,t measures the qualitative communications (tone) using the dictionary ‘i’ for the speech t. P_i,t and N_i,t denote the number of positive words and negative words from dictionary ‘i’ used in speech t.

Dictionary methods are applied to the pre-processed TDM. Our study uses a specific dictionary constructed primarily for central bank communications by Apel and Blix-Grimaldi (2012).⁷ We denote this dictionary AB henceforth. The values of qualitative RBI communications lie between −-1 and 1. We augment the words in the AB dictionary to include words used in Indian monetary policy statements. For example, the AB dictionary does not have information on food and fuel prices, which are the major drivers of headline inflation in India.⁸ In addition, some words frequently occurring in the RBI monetary policy statements are rise, improve, pick, uptick, mute, augur, elevate, slowdown, subdued, benign, flatten, fall, surge, shrink, firm, etc. Augmenting the original AB dictionary, thus, gives a better understanding of the sentiments conveyed by RBI. Hence, qualitative communications are estimated using two dictionaries: the original AB dictionary (ab_org) and the augmented AB dictionary (ab_aug).

The TDM uses each word as a separate element. Classifying words based on positive and negative sentiments can be tricky. For instance, the word ‘rise’ conveys a positive sentiment, but ‘price rise’ or ‘inflation rise’ conveys a negative sentiment. This issue is resolved using a technique called n-gram tokenization, which clubs adjacent words to form a separate element in the TDM. We use 3-gram tokenization for our analysis. Our sample matrix contains a minimum of one word and a maximum of three words per row. Suppose a sentence contains the phrase ‘crude oil prices firmed’, then using a maximum of 3-gram tokenization augments the TDM with the following words: ‘crude’, ‘oil’, ‘prices’, ‘firmed’, ‘crude oil’, ‘oil prices’, and ‘prices firmed’, ‘crude oil prices’, and ‘oil prices firmed’. Of these, the phrase ‘oil prices firmed’ conveys a negative sentiment.

3.4. Timeline of Variables

The analysis uses information available at the time forecasts are made. Figures 1 and 2 give a clear picture of the time of the release of dependent and explanatory variables for both quarterly and bi-monthly surveys.

Figure 1.

Time Frame for Quarterly Surveys

Figure 2.

Source: RBI SPF, DBIE, FRED Stats.

Where SPF gives the forecasts by the professional forecasters, π is inflation, π^RBI gives the inflation projections by the RBI, R is the repo rate, Δ_mOIL is the month-on-month change in oil prices. Repo rate and inflation for March surveys are taken at the time of forecasts and oil prices are taken from the beginning of the month. RBI projections are taken from the latest available monetary policy statement in January. Similarly, macroeconomic variables are taken at the time of forecasts for the bi-monthly surveys conducted in May. RBI projections are taken from the latest available monetary policy statement in April.

4. Do Epidemiological Models Fit SPF Forecasts Well?

As in Mankiw and Reis (2002) and Carroll (2003), private agents update their information based on some source of news and their own past experiences. In the SI models, private agents do not update their expectations every period as they face costs of information processing. Carroll (2003) shows that the agents update their forecasts with the latest information available to them as they pay attention to the news. SPF forecasts are modeled using these epidemiological models as given below:

π_{t + 1 | t}^{e} = ρ N_{t + 1 | t} + (1 - ρ) π_{t | t - 1}^{e}

(3)

where ρ is the speed of adjustment of professional forecasters to the news received. N_t_+1|t captures the source of news. We use RBI projections as the news variable in Equation (3), modifying it for the quarterly forecasts as follows:

π_{i, t + h | t}^{e, S P F} + = β + ρ π_{i, t + h | t}^{R B I} + (1 - ρ) π_{i, t + h - 1 | t - 1}^{e, S P F} + δ_{i} D u m F C H_{i} + ε_{i, t}

(4)

where $π_{i, t + h | t}^{e, S P F}$ denotes ‘h’ periods ahead median inflation expectations of professional forecasters. $π_{i, t + h | t}^{R B I}$ captures RBI inflation projections with the same forecast horizon as that of the dependent SPF forecast.

In line with Carroll (2003), we augment Equation (4) by adding recently published prices. In our case, this is realized inflation figures of the previous month. The augmented equation is:

\begin{array}{l} π_{i, t + h - 1 | t - 1}^{e, S P F} = β + α_{1} π_{i, t + h | t}^{R B I} + α_{2} π_{i, t + h - 1 | t - 1}^{e, S P F} \\ \begin{matrix} + α_{3} π_{i, t (m - 1)} + δ_{i} D u m F C H_{i} + ε_{i, t} \end{matrix} \end{array}

(5)

where we impose the restrictions α₁ + α₂ + α₃ = 1. π_i,t _(m–1) is one-month lagged inflation to account for the delays in the publication of official figures.⁹

These epidemiological models for the bi-monthly analysis can be written as follows:

π_{t + h - 1 | t - 1}^{e, S P F} = β + ρ π_{t + h | t}^{R B I} + (1 - ρ) π_{t + h - 1 | t - 1}^{e, S P F} + ε_{t}

(6)

π_{t + h | t}^{e, S P F} = β + α_{1} π_{t + h | t}^{R B I} + a_{2} π_{t + h - 1 | t - 1}^{e, S P F} + a_{3} π_{t (m - 3)} + ε_{t}

(7)

Results of Equations (4)–(7) estimated using ordinary least squares (heteroscedasticity and autocorrelation adjusted) are given in Tables 1 and 2. The speeds of adjustment are higher than those of professional forecasters for developed nations. Carroll (2003) suggests suppressing the constant term as professional forecasters incorporate most of the information and do not rely on social communication for information transfer (captured by the constant). Hence, the coefficients of RBI projections with and without the constant term should not differ significantly. Speeds of adjustment are high and significant for the shorter-horizon quarterly forecasts (₹0.65). They become insignificant for their longer-horizon counterparts. These results are similar to those for the Euro Area (Badarinza & Buchmann, 2009).

Table 1.

Speeds of Adjustment (Quarterly SPF)

	SPF WPI Forecasts (Shorter Horizon)				SPF WPI Forecasts (Longer Horizon)
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
$π_{i,t + h - 1\|t - 1}^{eSPF}$	0.29	0.29	0.17	0.17	0.64**	0.64**	0.68***	0.67***
	(0.24)	(0.23)	(0.27)	(0.27)	(0.25)	(0.24)	(0.23)	(0.23)
RBI projections	0.71***	0.71***	0.62**	0.62**	0.36	0.36	0.41	0.41
	(0.24)	(0.23)	(0.24)	(0.23)	(0.25)	(0.24)	(0.26)	(0.25)
WPI inflation			0.21	0.21			−0.08	−0.08
			(0.20)	(0.19)			(0.07)	(0.07)
Constant	−0.03		0.07		−0.02		−0.00
	(0.54)		(0.56)		(0.31)		(0.31)

Observations	46	46	45	45	43	43	43	43

Source: The authors.

Notes: Robust standard errors given in the parentheses.

Level of significance: * corresponds to 10%; ** corresponds to 5%; *** corresponds to 1%.

RBI: Reserve Bank of India; SPF: Survey of professional forecasters; WPI: Wholesale price index.

Table 2.

Speeds of Adjustment (Bi-monthly SPF)

	SPF CPI-C Forecasts (3-month Ahead)				SPF CPI-C Forecasts (1-year Ahead)
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
$π_{i,t + h - 1\|t - 1}^{eSPF}$	0.59***	0.60***	0.28**	0.33**	0.58***	0.57***	0.51***	0.53***
	(0.17)	(0.16)	(0.13)	(0.14)	(0.16)	(0.19)	(0.18)	(0.16)
RBI projections	0.41**	0.40**	0.30**	0.32**	0.42**	0.43**	0.23	0.27*
	(0.17)	(0.16)	(0.12)	(0.14)	(0.16)	(0.19)	(0.20)	(0.16)
CPI-C inflation			0.43***	0.35***			0.26***	0.20***
			(0.06)	(0.04)			(0.07)	(0.04)
Constant	−0.11		0.14*		−0.12*		0.12
	(0.08)		(0.07)		(0.07)		(0.09)

Observations	34	34	34	34	33	33	33	33

Source: The authors.

Notes: Robust standard errors given in the parentheses.

Level of significance: * corresponds to 10%; ** corresponds to 5%; *** corresponds to 1%.

CPI-C: Consumer price index–combined; RBI: Reserve Bank of India; SPF: Survey of professional forecasters.

Lagged inflation does not influence quarterly forecasts but drives bi-monthly forecasts. Bi-monthly models show significant effects of all the variables used. Persistence is high for shorter-horizon and longer-horizon forecasts (₹0.5). Speed of adjustment is lower than the quarterly SPF forecasts but significant for both 3-month-ahead and 1-year-ahead forecasts (₹0.4). Thus, Carroll-type epidemiological models fit well and show that professional forecasters use the information in the form of RBI inflation projections, especially post-FIT.

5. Estimation of Forecast Determinants

While the epidemiological models highlight the role of news in the form of RBI projections, we further investigate the impact of qualitative and quantitative communications on SPF forecasts in three ways. First, we examine the direct effect of communications on SPF forecasts by analyzing the expectations formation process. Second, we analyze the effect of communications on the difference between inflation expectations and realized inflation (also called inflation surprises). Finally, we investigate how RBI communications drive dispersions/disagreements across SPF forecasts. We control for international crude oil price volatility, lagged inflation, and RBI’s repo rate cycle in all the models.

The estimation is for two time periods. Quarterly analysis from March 2008 to December 2013 uses headline WPI forecasts and bi-monthly analysis from March 2014 to November 2019 uses headline CPI-C forecasts as the RBI changed its intermediate target from WPI before 2014 to CPI-C after the de facto adoption of FIT. The estimation uses ordinary least squares with heteroscedasticity and autocorrelation adjusted standard errors.¹⁰

5.1. Inflation Expectations Formation

Central bank communications influence professional forecasters’ expectations in AES (Ehrmann, 2015; Hubert, 2015a). This sub-section analyzes the effects of RBI communications on the levels of SPF forecasts in India. We estimate the following equation with quarterly data:

\begin{array}{l} π_{i, t + h - 1 | t}^{e, S P F} = γ_{0} + γ_{1} π_{i, t + h - 1 | t - 1}^{e, S P F} + γ_{2} π_{i, t (m - 1)} + γ_{3} Δ_{m} O I L_{i, t} + γ_{4} R C_{i, t} \\ \begin{matrix} + γ_{5} R B I C o m m_{i, t (m - 2)} + δ_{i} D u m F C H_{i} + ε_{i, t} \end{matrix} \end{array}

(8)

where $π_{i, t + h - 1 | t}^{e, S P F}$ denotes median inflation expectations of professional forecasters forecasted ‘h’ periods ahead. γ₁ controls for persistence in SPF forecasts. Lagged inflation [π_i,t_(m–1)] has a subscript of t(m-1) indicating that these values are taken for one-month prior inflation but available at the time of forecast. The variable RBIComm_i,t_(m–2) = { $π_{i, t (m - 2)}^{R B I} {,τ}_{i, t (m - 2)}$ } captures RBI quantitative and qualitative communications used interchangeably.¹¹ It has a subscript of t(m-2) as these values were taken from the monetary policy statements released two months prior to the SPF surveys. RC_i,t gives the rate cycle at the time of forecast. We control for forecast horizons using dummies as the analysis is conducted for two separate periods: shorter horizons (3-month-ahead and 6-month-ahead forecasts) and longer horizons (with 9-month-ahead and 12-month-ahead forecasts).¹²

Quarterly SPF forecasts (both shorter and longer horizon) have significant persistence. Lagged inflation is insignificant in most of the models. Oil price changes primarily affect shorter-horizon forecasts. The rate cycle positively influences shorter-horizon forecasts, indicating that SPF forecasters expect inflation to increase with monetary policy tightening. The positive effects of oil prices and a rise in the repo rate (upward movement in the rate cycle) are in line with the developed market results for the Euro Area (Hubert, 2017). RBI projections too impact only the shorter-horizon forecasts. Qualitative communications fail to drive the SPF forecasts. Significant positive effects of RBI projections support the hypothesis of the larger influence of communication variables on the shorter-horizon expectations (Badarinza & Buchmann, 2009; Hubert, 2015b; Goyal & Parab, 2021).

Bi-monthly surveys contain a maximum of 35 observations from March 2014 to November 2019. MPC became the decision-making body in October 2016. We analyze the effect of qualitative communications by incorporating an MPC dummy which takes the value 1 from November 2016 to November 2019 to segregate the effects of communications by an individual versus by a committee. Equations for bi-monthly analysis are given below.

\begin{matrix} π_{t + h | t}^{e, S P F} = γ_{0} + γ_{1} π_{t + h - 1 | t - 1}^{e, S P F} + γ_{2} π_{t} + γ_{3} Δ_{m} O I L_{t} + γ_{4} R C_{t} \\ + γ_{5} R B I C o m m_{t} + ε_{t} \end{matrix}

(9)

\begin{matrix} π_{t + h | t}^{e, S P F} = γ_{0} + γ_{1} π_{t + h - 1 | t - 1}^{e, S P F} + γ_{2} π_{t} + γ_{3} Δ_{m} O I L_{t} + γ_{4} R C_{t} + \\ γ_{5} τ_{t} + γ_{6} D u m M P C + γ_{7} D u m M P C * τ_{t} + ε_{t} \end{matrix}

(10)

where $D u m M P C = {\begin{matrix} 1, & i f t \geq N o v e m b e r 2016 \\ 0, & o t h e r w i s e \end{matrix}$

Communications variables are lagged by one month for bi-monthly analysis. The analysis is conducted separately for 3-month-ahead and 1-year-ahead SPF inflation forecasts. Results for 3-month-ahead forecasts are given in Table 3.¹³

Table 3.

Determinants of SPF Forecasts (Bi-monthly 3-month Ahead)

	(1)	(2)	(3)	(4)	(5)
$π_{t + h - 1\|t - 1}^{eSPF}$	0.25*	0.51***	0.57***	0.48***	0.49***
$π_{t + h - 1\|t - 1}^{eSPF}$	(0.14)	(0.07)	(0.08)	(0.08)	(0.11)
CPI-C inflation	0.45***	0.43***	0.47***	0.47***	0.49***
	(0.06)	(0.07)	(0.07)	(0.07)	(0.07)
Oil prices	−0.01***	−0.01**	−0.01**	−0.01**	−0.01**
	(0.00)	(0.00)	(0.01)	(0.00)	(0.00)
Rate cycle	−0.12	−0.14	−0.22**	−0.13	−0.17
	(0.08)	(0.09)	(0.11)	(0.08)	(0.11)
RBI projections	0.36***
	(0.12)
MPC dummy			0.36		0.11
			(0.25)		(0.23)
ab_org (τ_t)		−0.04	−0.13
		(0.10)	(0.21)
Interaction dummy			0.16
Interaction dummy			(0.23)
ab_aug (τ_t)				0.10	0.29
				(0.10)	(0.20)
Interaction dummy					−0.33
Interaction dummy					(0.24)
Constant	0.16	0.34*	−0.32	0.37*	0.17
	(0.16)	(0.19)	(0.44)	(0.18)	(0.55)
Observations	34	34	34	34	34
R-squared	0.96	0.95	0.96	0.95	0.96

Source: The authors.

Notes: Robust standard errors given in the parentheses.

Level of significance: * corresponds to 10%; ** corresponds to 5%; *** corresponds to 1%.

AB: Apel and Blix-Grimaldi dictionary; ab_aug: Augmented AB dictionary; ab_org: Original AB dictionary; CPI-C: Consumer price index–combined; MPC: Monetary policy committee; RBI: Reserve Bank of India; SPF: Survey of professional forecasters.

Inertial effects are significant in both models but larger for longer-horizon forecasts. Lagged inflation has a larger influence on shorter-horizon forecasts. Oil price volatility adversely affects shorter-horizon forecasts, albeit smaller in magnitude. The rate cycle does not drive SPF forecasts. The influence of RBI projections is significant and positive for shorter-horizon forecasts, but turns insignificant for the longer-horizon ones. Qualitative communications too fail to show any significant influence.

5.2. Influence of Communications on Inflation Surprises

In line with the analysis by Ullrich (2008), we add inflation surprises as a dependent variable. This variable is defined based on Andersen et al. (2003) and Ehrmann (2015) as the difference between current realized inflation and inflation expectations formulated in the past about the current period ( $π_{t} - π_{t | t - h}^{e}$ ).

Inflation surprises are often used as explanatory variables in the literature since they give the news component of sudden unanticipated shocks. Very few studies use them as dependent variables (Ullrich, 2008). Analysis of inflation surprises helps to understand the shocks in theoretical inflation expectations models in the literature. The determinants of inflation surprises for quarterly surveys are estimated using the following equation:

\begin{matrix} π_{i, t + h | t}^{s u r p} = γ_{0} + γ_{1} π_{i, t + h - 1 | t - 1}^{s u r p} + γ_{2} π_{i, t} + γ_{3} Δ_{m} O I L_{i, t} + γ_{4} R C_{i, t} \\ + γ_{5} R B I C o m m_{i, t} + δ_{i} D u m F C H_{i} + ε_{i, t} \end{matrix}

(11)

where $π_{i, t + h | t}^{s u r p}$ captures inflation surprises at time ‘t’. The quantitative communication variable is replaced with RBI projection surprises [ $π_{i, t}^{R B I, s u r p}$ ]. This variable is estimated as the difference between realized inflation and RBI projections for that period ( $π_{1} - π_{i, t | t - h}^{e, S P F}$ ). A positive influence of this variable on inflation surprises indicates the co-movement of SPF inflation forecasts with the RBI projections. We control for forecast horizon effects using dummy variables.¹⁴

Inflation surprises are highly persistent. This result is in line with Ullrich (2008), who obtains persistence of around 0.65 for the Euro Area. WPI inflation has a negative and significant influence on shorter-horizon forecasts but is insignificant for longer-horizon forecasts. Oil prices, too, are insignificant. Coefficients of the rate cycle are positive and significant. A rise in this variable is associated with an increase in inflation surprises. This may be capturing food price or output gap shocks since these are not controlled for. RBI projection surprises, as well as qualitative communications, do not drive SPF forecast surprises.

A similar analysis conducted for bi-monthly inflation surprises for both three-month-ahead and 1-year-ahead SPF forecasts segregates the effects of qualitative communications post-MPC using an interaction dummy. The equations for bi-monthly analysis are given below.

\begin{matrix} π_{t + h | t}^{e, s u r p} = γ_{0} + γ_{1} π_{t + h - 1 | t - 1}^{e, s u r p} + γ_{2} π_{t} + γ_{3} Δ_{m} O I L_{t} \\ + γ_{4} R C_{t} + γ_{5} R B I C o m m_{t} + ε_{t} \end{matrix}

(12)

\begin{matrix} π_{t + h | t}^{e, s u r p} = γ_{0} + γ_{1} π_{t + h - 1 | t - 1}^{e, s u r p} + γ_{2} π_{t} + γ_{3} Δ_{m} O I L_{t} + γ_{4} R C_{t} \\ + γ_{5} τ_{t} + γ_{6} D u m M P C + γ_{7} D u m M P C * τ_{t} + ε_{t} \end{matrix}

(13)

Results of Equations (12) and (13) for 3-month-ahead forecasts are given in Table 4.¹⁵

Table 4.

Determinants of Inflation Surprises (Bi-Monthly 3-Month-Ahead Forecasts)

	(1)	(2)	(3)	(4)	(5)
$π_{t + h - 1\|t - 1}^{e, surp}$	−0.12	0.64***	0.55***	0.65***	0.47***
$π_{t + h - 1\|t - 1}^{e, surp}$	(0.14)	(0.13)	(0.14)	(0.13)	(0.16)
CPI-C inflation	−0.06	0.16	0.43**	0.18	0.53**
	(0.09)	(0.11)	(0.17)	(0.13)	(0.21)
Oil prices	0.01	0.03**	0.02	0.03**	0.01
	(0.01)	(0.01)	(0.01)	(0.01)	(0.02)
Rate cycle	−0.11	−0.44*	−0.52**	−0.47*	−0.52**
	(0.14)	(0.24)	(0.24)	(0.25)	(0.23)
RBI projection surprises	0.94***
RBI projection surprises	(0.13)
MPC dummy			0.78		1.27**
			(0.60)		(0.49)
ab_org (τ_t)		−0.24	−0.09
		(0.24)	(0.55)
Interaction dummy			−0.05
Interaction dummy			(0.61)
ab_aug (τ_t)				−0.08	−1.11
				(0.54)	(0.83)
Interaction dummy					1.38
Interaction dummy					(1.03)
Constant	0.26	−1.07**	−2.70**	−1.06**	−3.51***
	(0.36)	(0.40)	(1.00)	(0.48)	(1.16)

Observations	32	32	32	32	32
R-squared	0.83	0.54	0.59	0.53	0.63

Source: The authors.

Notes: Robust standard errors given in the parentheses.

Level of significance: * corresponds to 10%; ** corresponds to 5%; *** corresponds to 1%.

Lagged inflation surprises have persistent effects except for the models with RBI projections. The influence of lagged inflation is positive in some cases but predominantly insignificant. Oil prices influence shorter-horizon forecasts in some scenarios. This effect vanishes for the longer-horizon forecasts. An upward movement in the rate cycle displays desired negative effects on inflation surprises. Interest rate tightening should drive down SPF forecasts in anticipation of lower inflation in the future. Unlike the quarterly analysis, RBI projection surprises significantly influence inflation surprises for both shorter-horizon and longer-horizon forecasts, the effect being higher for the former. We find no evidence for the influence of qualitative communications on inflation surprises. Our findings resemble those for the developed markets concerning the effects of lagged inflation surprises and lagged inflation. However, unlike Ullrich (2008), who finds a significant influence of the wording indicator (qualitative communications), we discover an insignificant effect. This indicates a need to improve the choice of words to anchor unanticipated surprises.

5.3. Communication Effects on Dispersion Across SPF Forecasts

Literature shows that dispersions across expectations are a key to macroeconomic dynamics (Mankiw et al., 2003). They can lead to the wrong investment decisions and affect resource allocation (Sims, 2003). Moreover, significant disagreements in the market resemble a demand shock with high unemployment (Beckmann & Czudaj, 2018). A necessary condition for successful policy intervention is that the cross-section dispersion of expectations should be minimal (Dovern et al., 2012). Reduction in forecaster disagreements points to higher policy credibility.

We use range (maximum–minimum) as a measure of dispersion across SPF forecasts (Galvis Ciro & Anzoategui Zapata, 2019; Montes et al., 2016). Quarterly disagreements are constructed using the readily available fixed-horizon forecasts. Bi-monthly disagreements are converted to fixed-horizon using techniques similar to the SPF forecasts. The quarterly analysis is conducted by clubbing 3-month-ahead forecasts with 6-month-ahead ones and 9-month-ahead forecasts with 12-month-ahead ones. Unlike levels forecasts, dispersion values are not available for forecast horizons of 12 months and beyond. Hence, we analyze 3-month-ahead and 6-month-ahead dispersions.

Quarterly disagreements are modeled using the following equation.

\begin{matrix} d i s (π_{i, t + h | t}^{e, S P F}) = γ_{0} + γ_{1} d i s (π_{i, t + h - 1 | t - 1}^{e, S P F}) + γ_{2} π_{i, t} + γ_{3} Δ_{m} O I L_{i, t} \\ + γ_{4} R C_{i, t} + γ_{5} R B I C o m m_{i, t} + δ_{i} D u m F C H_{i} + ε_{i, t} \end{matrix}

(14)

where $π_{i, t + h | t}^{e, S P F}$ denotes dispersions across professional forecasters. γ₁ is the coefficient for the inertia of dispersions.

Apart from the inertial effects of lagged disagreements, macroeconomic controls are not significant for shorter-horizon forecasts. Oil prices have negative and significant effects, while the rate cycle has a positive influence on 1-year-ahead dispersions. An increase in RBI projections decreases forecaster disagreement for shorter-horizon forecasts. Quarterly SPF forecasts tend to converge when RBI expects higher inflation in the near future. Qualitative communications are not significant except for the one with the original AB dictionary at longer horizons.¹⁶

The bi-monthly analysis for 3-month-ahead and 6-month-ahead dispersions includes an MPC dummy to capture the interaction effects of the qualitative communications variable. Equations for the analysis are given below.

\begin{matrix} d i s (π_{t + h | t}^{e, S P F}) = γ_{0} + γ_{1} d i s (π_{t + h | t - 1}^{e}) + γ_{2} π_{t} + γ_{3} Δ_{m} O I L_{t} \\ + γ_{4} R C_{t} + γ_{5} R B I C o m m_{t} + ε_{t} \end{matrix}

(15)

\begin{matrix} d i s (π_{t + h | t}^{e, S P F}) = γ_{0} + γ_{1} d i s (π_{t + h | t - 1}^{e}) + γ_{2} π_{t} + γ_{3} Δ_{m} O I L_{t} + γ_{4} R C_{t} \\ + γ_{5} τ_{t} + γ_{6} D u m M P C + γ_{7} D u m M P C * τ_{t} + ε_{t} \end{matrix}

(16)

Table 5 gives the results of Equations (15) and (16) for 3-month-ahead SPF forecaster disagreements.¹⁷ The persistence effects of lagged dispersions, as well as of lagged inflation, are insignificant. Oil prices largely have negative effects on shorter-horizon forecaster disagreements. These effects are insignificant for the longer-horizon dispersions. The rate cycle too fails to influence forecaster disagreements.

Table 5.

Determinants of Forecaster Disagreements (Bi-Monthly 3-Month-Ahead Forecasts)

	(1)	(2)	(3)	(4)	(5)
dis( $π_{t + h - 1\|t - 1}^{e, SPF}$ )	−0.09	0.15	0.20	0.14	0.09
dis( $π_{t + h - 1\|t - 1}^{e, SPF}$ )	(0.20)	(0.16)	(0.17)	(0.17)	(0.15)
CPI-C inflation	−0.22	0.05	−0.01	0.03	−0.11
	(0.15)	(0.09)	(0.13)	(0.08)	(0.12)
Oil prices	−0.02	−0.02*	−0.01	−0.02*	−0.01
	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)
Rate cycle	−0.08	−0.03	0.04	−0.01	0.02
	(0.19)	(0.20)	(0.20)	(0.20)	(0.20)
RBI projections	0.35*
	(0.18)
MPC dummy			−0.36		−0.48
			(0.46)		(0.34)
ab_org (τ_t)		0.28	0.50
		(0.21)	(0.35)
Interaction dummy			−0.36
			(0.49)
ab_aug (τ_t)				0.21	0.67**
				(0.28)	(0.26)
Interaction dummy					−0.84*
					(0.45)
Constant	1.11***	1.36***	1.78**	1.37***	2.34***
	(0.38)	(0.45)	(0.79)	(0.48)	(0.74)

Observations	33	33	33	33	33
R-squared	0.25	0.18	0.20	0.15	0.24

Source: The authors.

Notes: Robust standard errors given in the parentheses.

Level of significance: * corresponds to 10%; ** corresponds to 5%; *** corresponds to 1%.

Higher RBI inflation projections increase forecaster disagreements, more for the shorter-horizon forecasts. Qualitative communications fail to display desired negative effects. However, negative and significant coefficients of the interaction terms between the MPC dummy and qualitative communications with ab_aug dictionary for 3-month-ahead forecasts point to an improvement in the choice of words used in the monetary policy statements post-MPC. The effects of qualitative communications on dispersions post-MPC are significantly different from pre-MPC counterparts, with the desired negative sign. SPF forecasts converge significantly after MPC became the decision-making body in October 2016. Comparison of results across AB dictionaries shows significant differences. The insignificant influence of ab_org and significant one using ab_aug indicates that an appropriate choice of words is useful in reducing forecaster disagreements. The influence found of lagged inflation on forecaster disagreements differs significantly from AEs and other EMEs. Most studies find a positive influence of inflation on forecaster disagreements (Galvis Ciro & Anzoategui Zapata, 2019; Mankiw et al., 2003; Montes et al., 2016). This may be due to the fewer observations available for the analysis, which resulted in the use of a smaller number of macroeconomic controls. However, the role of higher central bank inflation forecasts in increasing dispersions and qualitative communications in moderating the volatility is in line with the literature.

5.4. Robustness Checks

As a robustness check, we first substitute the rate cycle variable with the repo rate for all the models.¹⁸ Results are qualitatively similar for the levels of SPF forecasts. Some differences are observed for inflation surprises. While the rate cycle positively affects quarterly inflation surprises, the repo rate has a negative influence. Inertial effects increase for the bi-monthly models with repo rate, especially for 1-year-ahead inflation surprises.

Robustness tests of disagreements show significant variations. While the rate cycle has insignificant influence in most of the cases and positive effects for 1-year-ahead dispersions in quarterly data, the influence of repo rate is predominantly negative and significant for quarterly models and positive for the bi-monthly ones. In addition, lagged inflation positively affects inflation dispersions for the models with repo rate, in line with the results of AEs and other EMEs. The influence of RBI projections, however, remains the same for both types of models, signifying their robust influence. The influence of qualitative communications turns insignificant for these models.¹⁹

6. Discussion

Literature highlights the importance of private-sector forecasts in the monetary policy transmission process. Anchoring their expectations enhances the credibility and trust of a central bank. Hence, the role of central bank actions and communications in managing private sector expectations gains importance in the monetary policy literature.

Our estimations support the literature on expectations management using central bank forecasts. However, they do not undermine the role of monetary policy actions. Inflation expectations can be anchored in two ways. First, align them with the projections of the central bank. Second, reduce cross-sectional disagreements across forecasts (Dovern et al., 2012). We find original evidence for both types of anchoring of SPF forecasts. RBI partly sets the future inflation rate influencing SPF inflation forecasts, which are the main determinants of future inflation. They are positively related to the dispersions indicating lower RBI forecasts reduce forecaster disagreements. Also, qualitative communications reduce dispersions after MPC became the decision-making body. As for central bank actions, an increase in the repo rate (positive value of the rate cycle) reduces inflation surprises. Comparing results across various forecast horizons shows a larger influence of RBI communications (both quantitative and qualitative) on the shorter-horizon forecasts.

Our estimation of Carroll-type epidemiological models shows the effects of news on inflation expectations formation in the presence of information rigidities. Rigidities are higher for longer-horizon forecasts, suggesting their larger dependence on economic fundamentals. Robust analyses provide evidence for a higher speed of adjustment in comparison to developed nations.

7. Conclusions

This study provides original evidence on the influence of quantitative and qualitative RBI communications on SPFs. RBI forecasts represent quantitative communications, while qualitative communications are captured using text analysis techniques (Apel & Blix-Grimaldi, 2012). Estimation is for two periods (pre-FIT—March 2008 to December 2013: quarterly and post-FIT—March 2014 to November 2019: bi-monthly) for three measures of inflation expectations, namely inflation forecast levels, inflation surprises, and forecaster disagreement. Results support the role of RBI communications, especially inflation projections, in anchoring SPF forecasts.

First, estimation of Carroll-type epidemiological models displays the presence of information frictions in the expectations formation process of professional forecasters. Frictions are marginally lower for longer-horizon forecasts, indicating their larger dependence on macroeconomic fundamentals than on the news components. High estimated speed of adjustment supports the use of communications to affect expectations.

Second, RBI projections influence levels of SPF forecasts, with a higher magnitude for shorter-horizon forecasts. In addition, inflation expectations show significant inertia pre-FIT and post-FIT. Oil prices affect SPF forecasts primarily in the pre-FIT period due to high volatility. This effect dampens post-FIT, also indicating better anchoring of expectations. Lagged inflation plays a vital role in driving SPF forecasts, particularly post-FIT.

Third, inflation surprises display a positive association with lagged inflation for the bi-monthly surveys. RBI projection surprises influence inflation surprises post-FIT, with a higher magnitude for shorter-horizon forecasts. Inflation surprises are less during upward movement in the rate cycle.

Finally, RBI projections reduce forecaster disagreements for bi-monthly surveys. Qualitative communications were not significant in the initial years of the adoption of de facto FIT, but their influence increased after MPC became the decision-making body. We find robust evidence for the impact of RBI communications on SPF forecasts and a gradual improvement in RBI communications and credibility with a more effective choice of words after October 2016. A longer time series analysis as more data becomes available would help to evaluate communication effects during times of uncertainty.

Our results indicate that the anchoring of private-sector expectations can be an important part of RBI’s inflation-targeting mandate and of communications in the form of inflation forecasts. Choice of words can also be a part of its policymaking toolkit.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Notes

References

Andersen

T. G.

, Bollerslev

, Diebold

F. X.

, & Vega

(2003). Micro effects of macro announcements: Real-time price discovery in foreign exchange. American Economic Review, 93(1), 38–62.

Apel

, & Blix-Grimaldi

(2012). The information content of central bank minutes [Research Paper No. 92]. Riksbank Research Paper Series. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2092575

Arango-Thomas

L. E.

, Pantoja

, Velásquez

C. A.

, & Arango

L. E.

(2020). Effects of the central bank’s communications in Colombia: A content analysis. Borradores de Economía; No. 1024. Banco de la Republica de Colombia. https://doi.org/10.32468/be.1024

Badarinza

, & Buchmann

(2009). Inflation Perceptions and Expectations in the Euro Area: The Role of News [Working Paper No. 1088]. European Central Bank. https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1088.pdf

Beckmann

, & Czudaj

R. L.

(2018). Monetary policy shocks, expectations, and information rigidities. Economic Inquiry, 56(4), 2158–2176.

Bholat

, Hansen

, Santos

, & Schonhardt-Bailey

(2015). Text mining for central banks. Bank of England.

Blinder

A. S.

, Ehrmann

, Fratzscher

, De Haan

, & Jansen

D.-J.

(2008). Central bank communication and monetary policy: A survey of theory and evidence. Journal of Economic Literature, 46(4), 910–945.

Bruno

(2017). Central bank communications: Information extraction and semantic analysis. Paper presented at the R User Conference, UseR; July 4–7, 2017; Brussels, Belgium, 253.

Carroll

C. D.

(2003). Macroeconomic expectations of households and professional forecasters. The Quarterly Journal of Economics, 118(1), 269–298.

10.

Coibion

, Gorodnichenko

, & Kamdar

(2018). The formation of expectations, inflation, and the Phillips Curve. Journal of Economic Literature, 56(4), 1447–1491.

11.

Dovern

, Fritsche

, & Slacalek

(2012). Disagreement among forecasters in G7 countries. The Review of Economics and Statistics, 94(4), 1081–1096. https://www-jstor-org-443.web.bisu.edu.cn/stable/23355342

12.

Ehrmann

(2015). Targeting inflation from below: How do inflation expectations behave? International Journal of Central Banking, 11(4), 213–249.

13.

Ehrmann

, Eijffinger

, & Fratzscher

(2012). The role of central bank transparency for guiding private sector forecasts. The Scandinavian Journal of Economics, 114(3), 1018–1052.

14.

Ehrmann

, & Fratzscher

(2007). Communication by central bank committee members: Different strategies, same effectiveness? Journal of Money, Credit and Banking, 39(2–3), 509–541.

15.

Easaw

, Golinelli

, & Malgarini

(2013). What determines households’ inflation expectations? Theory and evidence from a household survey. European Economic Review, 61, 1–13.

16.

Galvis Ciro

J. C.

, & Anzoátegui Zapata

J. C.

(2019). Disagreement in inflation expectations: Empirical evidence for Colombia. Applied Economics, 51(40), 4411–4424.

17.

Glick

, & Kouchekinia

(2021). Disagreement about US and Euro-Area inflation forecasts. FRBSF Economic Letter, 2021(08), 01–05.

18.

Goyal

(2017). Monetary policy transmission in India. In India development report (pp. 3–35). Oxford University Press.

19.

Goyal

, & Arora

(2012). The Indian exchange rate and central bank action: An EGARCH analysis. Journal of Asian Economics, 23(1), 60–72.

20.

Goyal

, & Parab

(2021). What influences aggregate inflation expectations of households in India? Journal of Asian Economics, 72, 101260.

21.

Hubert

(2014). FOMC forecasts as a focal point for private expectations. Journal of Money, Credit and Banking, 46(7), 1381–1420.

22.

Hubert

(2015a). Do central bank forecasts influence private agents? Forecasting performance versus signals. Journal of Money, Credit and Banking, 47(4), 771–789.

23.

Hubert

(2015b). ECB projections as a tool for understanding policy decisions. Journal of Forecasting, 34(7), 574–587.

24.

Hubert

(2017). Qualitative and quantitative central bank communication and inflation expectations. The BE Journal of Macroeconomics, 17(1), 1–41.

25.

Hubert

, & Labondance

(2018). Central bank sentiment [Working paper]. NBP. https://Www.Nbp.Pl/Badania/Seminaria/14xi2018

26.

Kotlowski

(2015). Do central bank forecasts matter for professional forecasters? [Working Paper No. 204]. National Bank of Poland Working Paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2648871

27.

Lei

, Lu

, & Zhang

(2015). News on inflation and the epidemiology of inflation expectations in China. Economic Systems, 39(4), 644–653.

28.

Mackowiak

, & Wiederholt

(2009). Optimal sticky prices under rational inattention. American Economic Review, 99(3), 769–803.

29.

Mankiw

N. G.

, & Reis

(2002). Sticky information versus sticky prices: A proposal to replace the New Keynesian Phillips Curve. The Quarterly Journal of Economics, 117(4), 1295–1328.

30.

Mankiw

N. G.

, Reis

, & Wolfers

(2003). Disagreement about inflation expectations. NBER Macroeconomics Annual, 18, 209–248.

31.

Mathur

, & Sengupta

(2019). Analysing monetary policy statements of the Reserve Bank of India. Indira Gandhi Institute of Development Research, Working Paper Series, 2019–012. http://www.igidr.ac.in/pdf/publication/WP-2019-012.pdf

32.

Montes

, Oliveira

, Curi

, & Nicolay

(2016). Effects of transparency, monetary policy signalling and clarity of central bank communication on disagreement about inflation expectations. Applied Economics, 48(7), 590–607.

33.

Moniz

, & de Jong

(2014). Predicting the impact of central bank communications on financial market investors’ interest rate expectations. European Semantic Web Conference, 144–155.

34.

Romer

C. D.

& Romer

D. H.

1989, Does monetary policy matter? A new test in the spirit of Friedman and Schwartz. NBER Macroeconomics Annual 1989, 4, 121–184.

35.

Reserve Bank of India. (2013). Monetary policy statement 2013–14. Reserve Bank of India.

36.

Reserve Bank of India. (2014, January). Report of the expert committee to revise and strengthen the monetary policy framework (Chairman: Dr Urjit Patel).

37.

Rosa

, & Verga

(2007). On the consistency and effectiveness of central bank communication: Evidence from the ECB. European Journal of Political Economy, 23(1), 146–175.

38.

Sims

C. A.

(2003). Implications of rational inattention. Journal of Monetary Economics, 50(3), 665–690.

39.

Ullrich

(2008). Inflation expectations of experts and ECB communication. The North American Journal of Economics and Finance, 19(1), 93–108.

40.

Woodford

(2002). Imperfect common knowledge and the effects of monetary policy. In Aghion

, Frydman

, Stiglitz

& Woodford

(Eds), Knowledge, information and expectations in modern macroeconomics: In honor of Edmund S. Phelps (pp. 25–58). Princeton University Press.