## _wp1159 - Bibliography

## Source details

**Canonical URL:** [_wp1159 - Bibliography](https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2011/_wp1159.pdf)

## Other formats

- [Markdown version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2011/_wp1159.pdf.md)
- [Structured JSON version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2011/_wp1159.pdf.json)

---

### Introduction and scope
- Focus: extend empirical literature on the channels through which inflation affects the real economy for the eight member countries of the WAEMU area.
- WAEMU member countries analyzed: Benin, Burkina Faso, Côte d’Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo.
- Dataset: consistently defined dataset covering January 1994 through December 2009.
- Key objectives:
  - Measure inflation uncertainty using a GARCH model of inflation (accounting also for lagged, seasonal, regional, and global effects).
  - Study the nexus between inflation and inflation uncertainty in a bi-variate VAR context.
  - Construct a measure of relative price variability (RPV) from the harmonized consumer price index (HCPI) and examine the nexus between inflation and RPV in linear, symmetric/asymmetric, and unexpected-inflation specifications.

### Empirical approach and measures
- Inflation uncertainty:
  - Derived from a GARCH model of inflation with lagged, seasonal, regional, and global controls.
  - Studied jointly with inflation in a bi-variate VAR framework; Granger causality tests and impulse-response functions employed.
- Relative price variability (RPV):
  - Constructed from HCPI using RPV_t = sum_{i=1}^n w_i (π_{it} - π_t)^2 with n=12 for WAEMU HCPIs.
  - Examined via specifications: OLS-I (linear), OLS-II (nonlinear |π_t|), OLS-III (asymmetric deflation effect), OLS-IV (expected vs. unexpected inflation), OLS-V (asymmetric unexpected inflation).
- Temporal coverage: January 1994 through December 2009.
- Regional/global controls: global inflation proxied by US inflation (Specification I) and euro zone inflation (Specification II); regional inflation constructed as weighted average excluding the country.

### Main empirical findings — transmission and heterogeneity
- Transmission channels:
  - Inflation raises inflation uncertainty (Friedman-Ball hypothesis supported for all WAEMU countries).
  - Inflation raises relative price variability (RPV); RPV better explained by nonlinear specifications in |π_t|.
- Heterogeneity:
  - Pattern, magnitude, and timing of both channels vary substantially by country.
  - Cross-country inflation shocks are highly correlated in most pairs; exceptions and low correlations include Guinea-Bissau (negative correlation with Benin: -0.28) and low pair correlations such as Niger–Senegal: 0.07.
- Key cross-country correlation examples (sample 1994:01–2009:12, Table 6):
  - BEN–BFA: 0.66; BEN–CIV: 0.76; BEN–GNB: -0.28; BEN–MLI: 0.46; BEN–NER: 0.15; BEN–SEN: 0.49; BEN–TGO: 0.71.
  - BFA–CIV: 0.67; BFA–TGO: 0.59; CIV–TGO: 0.76; MLI–TGO: 0.35; NER–TGO: 0.18; SEN–TGO: 0.38.
  - Pair highs: 0.76 in Burkina Faso–Mali and 0.76 in Côte d’Ivoire–Benin.
- Variance decomposition (VAR(12), 24-month horizon, sample 1994:01–2009:12, Specification I with global = US; Specification II with global = euro zone) — selected figures (percentages at 24-month horizon):
  - Benin: Global (US) 5.75; Regional 2.74; Domestic 1.79. Specification II: Global (Euro zone) 9.54; Regional 7.74; Domestic 2.8.
  - Burkina Faso: Global (US) 7.63; Regional 6.25; Domestic 6.21. Specification II: Global 3.83; Regional 2.05; Domestic 4.1.
  - Côte d’Ivoire: Global (US) 4.44; Regional 8.44; Domestic 7.29. Specification II: Global 9.94; Regional 3.34; Domestic 6.8.
  - Guinea-Bissau: Global (US) 9.1; Regional 6.88; Domestic 4.29. Specification II: Global 9.3; Regional 5.88; Domestic 4.8.
  - Mali: Global (US) 9.23; Regional 8.45; Domestic 2.41. Specification II: Global 1.33; Regional 6.45; Domestic 2.3.
  - Niger: Global (US) 8.15; Regional 7.43; Domestic 4.51. Specification II: Global 11.65; Regional 1.53; Domestic 6.9.
  - Senegal: Global (US) 4.94; Regional 9.94; Domestic 5.27. Specification II: Global 7.25; Regional 0.54; Domestic 2.3.
  - Togo: Global (US) 5.45; Regional 7.13; Domestic 7.41. Specification II: Global 2.35; Regional 1.03; Domestic 6.7.
- HCPI composition and volatility drivers:
  - Food and non-alcoholic beverages share — WAEMU average: 41.4; Benin: 38.2; Burkina Faso: 32.1; Côte d'Ivoire: 32.2; Guinea-Bissau: 59.7; Mali: 40.8; Niger: 37.5; Senegal: 48.3; Togo: 40.3.
  - Comparative note: 41.4 percent in the WAEMU but only about 15 percent in the US and the euro zone.

### GARCH estimation and inflation uncertainty
- GARCH specification:
  - Mean equation: INF_t on lags of INF, regional inflation REG_t, depreciation DEP_t, seasonal lags at 6, 9, 12, and WAEMU/Guinea-Bissau depreciation dummies.
  - Error u_t ~ N(0, σ_t^2); conditional variance GARCH(p,q) with sum of p and q reported.
- Model diagnostics and properties:
  - GARCH(p,q) models fit mean and variance well for all countries; LBQ^2-statistics indicate heteroscedasticity removed.
  - Sum of p and q close to one for Benin, Guinea-Bissau, Mali, Senegal, and Togo, indicating persistent volatility shocks.
  - Core inflation (HPCI excluding food and non-alcoholic beverages) reduces adjusted R^2 below 10 percent, implying inflation uncertainty mainly driven by food price volatility.
- Selected country GARCH estimates and diagnostics (Table 7 highlights):
  - Benin (GARCH(1,2)):
    - Mean: Constant 0.0033 (z-Stat. 3.85); INF_t-1 -0.161 (z-Stat. -2.25); REG_t 0.542 (z-Stat. 4.18); DUM-Jan94 0.189 (z-Stat. 5.10).
    - Variance: u_{t-1}^2 0.940 (z-Stat. 18.36); σ_{t-1}^2 0.413 (z-Stat. 2.92).
    - Adj. R-squared 0.55; Akaike -5.81; Schwarz -5.42; LBQ^2 [1] 0.02; [3] 2.58; [6] 3.80.
  - Burkina Faso (GARCH(1,1)) — Variance: u_{t-1}^2 0.571 (z-Stat. 1.62); σ_{t-1}^2 0.100 (z-Stat. 0.84); Adj. R-squared 0.41.
  - Côte d’Ivoire (GARCH(0,1)): REG_t 0.939 (z-Stat. 5.83); DUM-Jan94 0.099 (z-Stat. 3.92); Variance constant 0.0005 (z-Stat. 5.10).
  - Guinea-Bissau (GARCH(1,2)): REG_t 0.153 (z-Stat. 2.01); DUM-Feb94 -0.150 (z-Stat. -5.39); u_{t-1}^2 0.817 (z-Stat. 12.41).
- Dynamics: inflation uncertainty mildly cyclical and low except around January 1994 WAEMU establishment, the 2008 food and fuel crisis, and Guinea-Bissau civil war (June 1998–May 1999).

### Granger causality and VAR impulse responses
- Granger causality (two-step tests):
  - Tests of INF → UNC and UNC → INF conducted using lag lengths 3, 6, 9, 12.
  - Results support Friedman-Ball hypothesis across all countries: inflation Granger-causes inflation uncertainty at conventional significance levels (lag-length dependent).
  - Evidence for Cukierman hypothesis (uncertainty → inflation) in Benin, Senegal, and Togo at some lag lengths; bi-directional causality occurs in those countries at some lags.
- Selected exact Granger test entries (Table 8; notation: ***, **, * indicate 1, 5 and 10 percent significance; (+)/(-) indicate sign of coefficient sum):
  - Benin: 3 lags 6.25***(+) / 1.89 ; 6 lags 5.57***(+) / 2.16**(+) ; 9 lags 8.86***(+) / 1.63(+) ; 12 lags 3.73***(+) / 1.27(+).
  - Senegal: 3 lags 13.86***(+) / 0.54 ; 6 lags 14.29***(+) / 2.73**(-) ; 9 lags 6.17***(+) / 3.73***(+) ; 12 lags 2.89**(+) / 1.48(-).
  - Togo: 3 lags 20.02***(+) / 2.92**(+) ; 6 lags 11.21***(+) / 1.67 ; 9 lags 27.14***(+) / 1.36(-) ; 12 lags 1.49(+) / 0.82(+).
- VAR(12) impulse responses (Cholesky decomposition):
  - Following a one standard deviation positive inflation shock, inflation uncertainty:
    - Quickly rises from its mean, fluctuates at high levels, then typically returns to the mean after periods 10 to 15.
    - Cross-country differences: magnitude, volatility, timing, and persistence vary widely; in Guinea-Bissau the effect persists beyond 50 periods.
  - Robustness: patterns unchanged when using core instead of headline inflation and when using seasonally adjusted inflation rates.

### RPV measurement and empirical results
- RPV definition: RPV_t = sum_{i=1}^n w_i (π_{it} - π_t)^2, n=12 for WAEMU HCPIs.
- Visual evidence: average HCPI inflation and RPV jointly fluctuate in all WAEMU countries; magnitudes differ by country.
- Specification findings:
  - OLS-I (linear): RPV_t = α_1 + β_1 π_t + ε_t
    - Rejected in most countries except Benin, Guinea-Bissau, and Niger where β_1 ≈ 0.01 and significant.
  - OLS-II (nonlinear): sqrt(RPV_t) = α_2 + β_2 |π_t| + ε_t
    - Fits data better for all countries. β_2 ranges from 0.55 (Guinea-Bissau) to 1.03 (Togo).
    - Examples: Benin α_2 0.01*** β_2 0.85*** R^2 0.41 DW 1.96; Guinea-Bissau α_2 0.01*** β_2 0.55*** R^2 0.38 DW 2.22; Niger α_2 0.01*** β_2 0.71*** R^2 0.69 DW 1.70; Togo α_2 0.01*** β_2 1.03*** R^2 0.50 DW 1.63.
  - OLS-III (asymmetric inflation/deflation): evidence of asymmetric impact in Burkina Faso, Côte d’Ivoire, Mali, and Senegal (γ_3 significant).
- Unexpected vs. expected inflation (OLS-IV and OLS-V, Table 11):
  - OLS-IV: RPV = α4 + β4|Πe| + μ4|Π - Πe| + ε_t
    - μ4 (unexpected inflation coefficient) generally about 0.51 to 0.74; Togo μ4 = 1.09.
    - β4 (expected inflation coefficient) significant in half the countries, between 0.28 (Côte d’Ivoire) and 0.64 (Niger); β4 < μ4 except for Niger.
  - OLS-V (asymmetric unexpected inflation): Wald test rejects equality of positive and negative unexpected inflation coefficients at the 10 percent level in all countries except Mali, Niger and Senegal.
    - Estimated unexpected-deflation coefficients ψ5 range 0.36 (Benin) to 1.09 (Togo).
    - Unexpected-inflation coefficients η5 vary 0.53 (Senegal) to 1.09 (Togo).
  - Interpretation: unexpected inflation typically has a stronger impact on RPV than expected inflation; asymmetry indicates different responses to positive vs negative unexpected inflation.

### Recent inflation experience and data notes
- Regional inflation trends:
  - WAEMU inflation lower and less volatile recently except during January 1994 CFAF devaluation (50 percent) and the 2008 fuel and food price crisis.
  - Guinea-Bissau experienced instability before CFAF adoption and civil war (1998–99).
- WAEMU GDP and population shares:
  - Côte d’Ivoire accounts for more than 34 percent of WAEMU GDP and 23 percent of its population.
- Summary statistics for regional 12-month simple average inflation rate by five-year periods (means, standard deviations, medians):
  - 1980-84: Mean 9.73; Standard deviation 3.94; Median 10.24.
  - 1985-89: Mean 31.42; Standard deviation 2.23; Median 41.68.
  - 1990-94: Mean 11.42; Standard deviation 9.88; Median 8.76.
  - 1995-99: Mean 28.01; Standard deviation 6.27; Median 18.58.
  - 2000-04: Mean 11.95; Standard deviation 2.03; Median 2.13.
  - 2005-09: Mean 3.93; Standard deviation 3.45; Median 3.33.
- Domestic CPI and HCPI data availability caveats:
  - Domestic CPI generally available since January 1981, with exceptions: Guinea-Bissau since February 1986; Mali since July 1987; Benin since January 1991.
  - HCPI generally available from January 1997; exceptions: Niger and Togo since December 1997; Senegal since January 1998; Guinea-Bissau since July 2002.

### Policy implications and recommendations
- Overarching policy aim: keep inflation low, stable, and predictable.
- Monetary policy:
  - A common monetary policy is sensible given correlated inflation shocks across countries; however, second-round output effects differ across countries.
  - BCEAO goals: maintain price stability (inflation at or below 2 percent) and maintain adequate pooled foreign reserves.
  - French treasury holds at least 65 percent of WAEMU reserves and guarantees CFAF convertibility into Euros under borrowing limits.
- Complementary measures to homogenize transmission:
  - Enhance macroeconomic convergence.
  - Pursue closer regional integration.
  - Increase domestic policy coordination and capacity building.
  - Make adequate use of national monetary policy instruments (e.g., reserve requirements, standing refinance facilities, money market operations).
- Communication and data transparency:
  - Improve central bank information and communication policy.
  - Publish exchange rate, regional inflation, trading partner inflation, and projections of important import/export prices (food and fuel).
  - Consider compiling a regionally comparable core inflation index excluding food and energy as a reference.
- Regulatory and structural recommendations:
  - Align national laws on administered prices, customs exemptions, and tax rates to reduce heterogeneous firm price-setting behavior.
  - Gear national reserve requirements toward countering cyclical fluctuations in individual countries.
- Recognition of supply shocks:
  - Food and fuel prices are significant supply-side drivers of inflation uncertainty and RPV; food share in CPI varies between 32 and 60 percent across countries.

### Conclusions and suggested research avenues
- Documented channels:
  - Inflation increases inflation uncertainty.
  - Expected and unexpected inflation increase RPV.
- Sample: the eight WAEMU members from January 1994 through December 2009.
- Heterogeneity complicates a one-size-fits-all monetary policy; policy takeaway is to keep inflation low, stable, and predictable while promoting convergence and coordination.
- Suggested future research:
  - Distinguish short- from long-term inflation uncertainty.
  - Use micro data on firms’ pricing behavior, remittances, and monetary instruments to understand RPV.
  - Employ threshold models or endogenous break-point tests to identify critical inflation thresholds.
  - Study real output and welfare consequences of inflation-induced increases in inflation uncertainty and RPV.

### Appendix — robustness and diagnostics (selected results)
- Stationarity and heteroscedasticity:
  - Monthly inflation appears stationary: ADF test rejects a unit root at the 1 percent level for all countries; ADF test statistics include: Benin -12.14; Burkina Faso -8.70; Côte d’Ivoire -6.60; Guinea-Bissau -12.46; Mali -9.58; Niger -8.83; Senegal -9.60; Togo -10.30 (critical value at 1 percent: -3.47).
  - LBQ2 rejects no serial correlation in squared OLS residuals for all countries except Niger, indicating ARCH present in OLS residuals for all but Niger.
- Robustness to core inflation:
  - Excluding food and non-alcoholic beverages yields core-inflation coefficients slightly higher than headline results; qualitative evidence on price rigidities largely robust.

*Source: _wp1159 - Bibliography (PDF).*

### Bibliography ...........................................................................................................

### _wp1159 - Bibliography ................................................................................................................

### Introduction and scope
- Focus: extend empirical literature on the channels through which inflation affects the real economy for the eight member countries of the WAEMU area.
- WAEMU member countries analyzed: Benin, Burkina Faso, Côte d’Ivoire, Guinea-Bissau, Mali, Niger, Senegal, and Togo.
- Dataset: a unique consistently defined dataset covering the establishment of the WAEMU in January 1994 through December 2009.
- Key objectives:
  - Measure inflation uncertainty using a GARCH model of inflation (accounting also for lagged, seasonal, regional, and global effects).
  - Study the nexus between inflation and inflation uncertainty in a bi-variate VAR context.
  - Construct a measure of relative price variability (RPV) from the harmonized consumer price index (HCPI) and examine the nexus between inflation and RPV in various empirical models.

### Empirical approach and measures
- Inflation uncertainty:
  - Derived from a GARCH model of inflation that accounts for lagged, seasonal, regional, and global effects.
  - Studied jointly with inflation in a bi-variate VAR framework.
- Relative price variability (RPV):
  - Constructed from the harmonized consumer price index (HCPI).
  - Examined in linear, symmetric/asymmetric, and unexpected-inflation specifications (see referenced Tables 9–11 for model variants).
- Temporal coverage: January 1994 through December 2009.
- Regional focus: WAEMU (West African Economic and Monetary Union) member countries listed above.

### Main empirical findings
- Transmission channels:
  - Inflation raises inflation uncertainty.
  - Inflation raises relative price variability (RPV).
- Heterogeneity:
  - The pattern, magnitude, and timing of both transmission channels vary substantially by country.
  - Heterogeneities are plausible given persistent and sizeable structural differences in the economies, insufficient macroeconomic convergence, and uneven (bilateral, regional, and global) integration.
- Cross-country correlation:
  - Inflation shocks are highly correlated across WAEMU countries.
- Methodological robustness:
  - Results are obtained using a common methodology across countries and account for lagged, seasonal, regional, and global effects in the GARCH specification.
  - The paper reports results from variance decomposition, correlation of inflation shocks, GARCH estimations, Granger causality tests, impulse response functions, and monthly rates analyses (see referenced Figures and Tables).

### Policy implications and recommendations
- Primary policy aim:
  - Keep inflation low, stable, and predictable.
- Monetary policy:
  - Because inflation shocks are highly correlated across countries, a common monetary policy is a sensible instrument to keep inflation under control in the whole region.
  - However, second-round effects of inflation on output are likely to differ across countries.
- Complementary measures to homogenize transmission mechanisms:
  - Enhance macroeconomic convergence.
  - Pursue closer integration.
  - Increase domestic policy coordination.
  - Make adequate use of national monetary policy instruments.
- Central bank practices:
  - Improve the central bank’s information and communication policy.

### Structure of the paper (as presented)
- Section II: background information on the WAEMU.
- Section III: analysis of recent regional inflation.
- Section IV: description of the empirical analysis and presentation of results.
- Section V: policy implications deduced from results.
- Section VI: concluding remarks.

*Source: _wp1159 - Bibliography (PDF).*

### conclusions.

### conclusions.

### II. BACKGROUND — The West African Economic and Monetary Union (WAEMU)
- WAEMU was created as a customs and monetary union in January 1994 by Benin (BEN), Burkina Faso (BFA), Côte d’Ivoire (CIV), Mali (MLI), Niger (NER), Senegal (SEN), and Togo (TGO).
- Guinea-Bissau (GNB) joined in May 1997.
- The union was established in response to the sharp devaluation of their common currency, the CFA Franc (CFAF).
- Côte d’Ivoire accounts for more than 34 percent of WAEMU GDP and 23 percent of its population.
- Senegal is second in GDP; Burkina Faso is second in population; Guinea-Bissau is the smallest member.
- Economic structure heterogeneity:
  - Semi-industrialized with high manufacturing/industry value-added: Côte d’Ivoire and Senegal.
  - High share of agriculture value-added: Guinea-Bissau, Togo, and Niger.
- Trade openness ranges across members from 35 percent of GDP in Burkina Faso to 89 percent in Togo.

### II.B — The Central Bank (BCEAO)
- The common central bank is Banque Centrale des Etats de l'Afrique de l'Ouest (BCEAO).
- Monetary policy goals:
  - Maintain price stability (defined as inflation at or below 2 percent).
  - Maintain an appropriate level of foreign reserves (the pooled foreign exchange reserves of the member states).
- The French treasury holds at least 65 percent of WAEMU reserves and guarantees convertibility of the CFAF into Euros, subject to rules limiting CFA government borrowing.
- Monetary policy in WAEMU is more constrained by rules limiting domestic credit creation than by the exchange rate peg.
- Capital controls provide room for autonomous monetary policy; the BCEAO responds to changes in prices or output regionally.
- Policy challenge: substantial macroeconomic differences among members mean no single monetary policy may be suitable for all members.
- BCEAO instruments at the regional level include:
  - Money market operations: tenders for repo operations.
  - Standing refinance facilities: discount operations for liquidity provision up to 360 days; repo operations for liquidity provision up to 30 days.
  - Different reserve requirements can be set for each member.

### III. DATA — Sources and HCPI composition
- Domestic CPI data: monthly consumer price index (CPI) from the IMF’s International Financial Statistics (IFS).
  - CPI generally available since January 1981.
  - Availability caveats:
    - Guinea-Bissau: only since February 1986.
    - Mali: only since July 1987.
    - Benin: only since January 1991.
- HCPI (harmonized consumer price index) for RPV analysis:
  - Monthly price data and weights for cross-country comparable HCPI provided by member country authorities.
  - HCPIs generally available from January 1997, with exceptions:
    - Niger: since December 1997.
    - Togo: since December 1997.
    - Senegal: since January 1998.
    - Guinea-Bissau: since July 2002.
  - Recorded prices cover 12 product groups with different weights (percent as of 2009).
  - Food and non-alcoholic beverages share (selected examples):
    - WAEMU average: 41.4
    - Benin: 38.2
    - Burkina Faso: 32.1
    - Côte d'Ivoire: 32.2
    - Guinea-Bissau: 59.7
    - Mali: 40.8
    - Niger: 37.5
    - Senegal: 48.3
    - Togo: 40.3
  - Other HCPI subcategory weights (selected figures):
    - Housing, water, electricity, gas and other fuels — WAEMU average: 12.8
    - Transport — WAEMU average: 10.1
    - Restaurants and hotels — WAEMU average: 6.7

### III.B — Recent Inflation Experience — Regional and Domestic
- Regional inflation characteristics:
  - Regional WAEMU inflation has recently become lower and less volatile except during:
    - The 1994 CFAF devaluation (50 percent).
    - The fuel and food price crisis in 2008.
  - Guinea-Bissau had difficulty controlling monetary developments until adopting the CFAF in May 1997; a burst of inflation occurred during the 1998-99 civil war.
- Summary statistics for the WAEMU regional 12-month simple average inflation rate (by five-year periods):
  - 1980-84: Mean 9.73; Standard deviation 3.94; Median 10.24
  - 1985-89: Mean 31.42; Standard deviation 2.23; Median 41.68
  - 1990-94: Mean 11.42; Standard deviation 9.88; Median 8.76
  - 1995-99: Mean 28.01; Standard deviation 6.27; Median 18.58
  - 2000-04: Mean 11.95; Standard deviation 2.03; Median 2.13
  - 2005-09: Mean 3.93; Standard deviation 3.45; Median 3.33
- Domestic inflation:
  - Domestic inflation rates have become quite synchronized since WAEMU began in 1994, including Guinea-Bissau since 1997 accession.
  - There is increasing convergence of WAEMU countries’ mean and median inflation and reduced volatility (lower standard deviations) in recent years.
- Selected descriptive statistics of domestic year-on-year inflation rates (in percent; period columns reflect multiple five-year frames; sample start dates noted in table):
  - WAEMU (regional weighted average): Mean progression across periods: 9.39, 3.36, 6.38, 4.81, 2.05, 3.65; Standard deviation progression: 3.22, 2.21, 12.00, 4.85, 1.66, 2.95; Median progression: 9.15, 3.58, 0.93, 3.99, 2.12, 2.96
  - Country-specific examples (means across periods shown in table, with sample starts):
    - Benin (sample starts 1991:12): Mean progression shown in table.
    - Guinea-Bissau (sample starts 1987:02): Mean progression includes very high values in early periods (e.g., first period Mean 81.15).
    - Mali (sample starts 1988:07): Mean progression shown in table.
  - (Full country-by-period figures are reported in Table 4 of the source.)

### III.C — Interdependence of Inflation Rates — Method and Findings
- Motivation:
  - Fixed exchange rates and a common currency area imply price shocks in one country can transmit to others.
  - Trade linkages, reinforced by the WAEMU free trade area, also transmit price shocks.
- Objective:
  - Assess how much of a country’s domestic inflation dynamics are attributable to foreign-originating inflation.
- Shock classification:
  - Global shocks (ug): affect economies inside and outside the region (e.g., oil shocks).
  - Regional shocks (ur): common to economies within the region (e.g., 1994 CFAF devaluation).
  - Domestic shocks (ud): unique to a single economy (e.g., fiscal policy–induced aggregate demand disturbances).
- Empirical approach:
  - Three-variable structural VAR with global, regional and local price levels: pg, pr, pd.
  - Monthly percentage changes in global, regional and domestic CPIs used.
  - Global inflation proxied by US inflation (and alternatively euro zone inflation to account for CFAF peg to the euro).
  - Regional inflation for each country derived from weighted average (using 2009 GDPs) of monthly inflation rates in WAEMU countries excluding the country in question.
  - Identification via standard structural VAR restrictions; seasonality controlled; VAR(12) specification used.

### III.C — Variance Decomposition Results (Sample 1994:01–2009:12, VAR(12), 24-month horizon)
- Key result summary:
  - Global shocks are surprisingly unimportant in explaining domestic inflation variation in WAEMU countries.
  - Regional shocks matter about as much as domestic shocks in most countries.
  - Typical contribution of regional shocks to total variation in domestic inflation: about 50 to 60 percent.
  - Regional shocks explain less than 40 percent in Burkina Faso and Mali.
  - Regional shocks explain less than 10 percent in Guinea-Bissau.
  - Using US versus euro zone inflation (Specification I versus II) yields relatively equal magnitudes for the global shock contribution, suggesting global shocks are not being absorbed predominantly into the regional shock term because of the CFAF peg to the euro.
- Selected variance decomposition figures (reported for Specification I with global = US; Specification II with global = euro zone; reported are percentages at 24-month horizon):
  - Benin: Global (US) 5.75; Regional 2.74; Domestic 1.79; Specification II columns: Global (Euro zone) 9.54; Regional 7.74; Domestic 2.8
  - Burkina Faso: Global (US) 7.63; Regional 6.25; Domestic 6.21; Specification II: Global 3.83; Regional 2.05; Domestic 4.1
  - Côte d'Ivoire: Global (US) 4.44; Regional 8.44; Domestic 7.29; Specification II: Global 9.94; Regional 3.34; Domestic 6.8
  - Guinea-Bissau: Global (US) 9.1; Regional 6.88; Domestic 4.29; Specification II: Global 9.3; Regional 5.88; Domestic 4.8
  - Mali: Global (US) 9.23; Regional 8.45; Domestic 2.41; Specification II: Global 1.33; Regional 6.45; Domestic 2.3
  - Niger: Global (US) 8.15; Regional 7.43; Domestic 4.51; Specification II: Global 11.65; Regional 1.53; Domestic 6.9
  - Senegal: Global (US) 4.94; Regional 9.94; Domestic 5.27; Specification II: Global 7.25; Regional 0.54; Domestic 2.3
  - Togo: Global (US) 5.45; Regional 7.13; Domestic 7.41; Specification II: Global 2.35; Regional 1.03; Domestic 6.7
- Additional contextual point:
  - The weight of price-volatile HCPI subcategories (notably food) is much higher in WAEMU countries than in the US or the euro zone; for example, the share of food items is on average 41.4 percent in WAEMU HCPIs (with country-specific shares noted above).

*Italic: Source: _wp1159 - conclusions.*

### 41.4 percent in the WAEMU but only about 15 percent in the US and the euro zone. That is how

### _wp1159 - 41.4 percent in the WAEMU but only about 15 percent in the US and the euro zone. That is how

### Correlation of domestic inflation shocks across WAEMU countries
- Correlation coefficients of domestic inflation shocks are all positive except for Guinea-Bissau.
- Correlations range widely, with specific pair examples:
  - 0.07 in the pair Niger-Senegal.
  - 0.76 in the pair Burkina Faso-Mali.
  - 0.76 in the pair Côte d’Ivoire-Benin.
- Pattern of correlations closely resembles established trade pairings.
- Guinea-Bissau’s correlations increase substantially when excluding times before its civil war (June 1998 to May 1999) and its WAEMU accession (May 1997).

### Table 6 (sample 1994:01–2009:12) — selected correlation entries (as presented)
- BEN–BFA: 0.66
- BEN–CIV: 0.76
- BEN–GNB: -0.28
- BEN–MLI: 0.46
- BEN–NER: 0.15
- BEN–SEN: 0.49
- BEN–TGO: 0.71
- BFA–CIV: 0.67
- BFA–TGO: 0.59
- CIV–TGO: 0.76
- GNB (row) shows 1/0.59 (note: 1/ indicates exclusion period as footnoted)
- MLI–TGO: 0.35
- NER–TGO: 0.18
- SEN–TGO: 0.38
- Footnote: "1/ Excluding times before the Guinea-Bissauan civil war (June 1998 to May 1999) and WAEMU accession (May 1997)."

### Analysis framework: inflation → inflation uncertainty → RPV (country-by-country)
- Two-step approach:
  - Assess impact of inflation on inflation uncertainty.
  - Assess impact of inflation uncertainty on RPV.

### Competing hypotheses on inflation and inflation uncertainty
- Friedman-Ball hypothesis: inflation has a positive impact on inflation uncertainty (Ball 1992 builds on Friedman 1977).
- Cukierman alternative: causality runs from inflation uncertainty to inflation (Barro-Gordon/time-inconsistency argument).
- Empirical strategy:
  - Derive variance of unpredictable innovations in inflation as measure of inflation uncertainty using GARCH models for each country.
  - Use Granger tests to identify direction of causality.
  - Use impulse reaction functions to quantify effects.

### Measure of inflation uncertainty — GARCH specification and variables
- GARCH model used to obtain time-varying conditional variance of the error term as measure of inflation uncertainty.
- Mean equation (conceptual form, full model in text): INF_t depends on lags of INF, regional inflation REG_t (weighted average regional inflation using 2009 GDPs, excluding the country itself), depreciation DEP_t (nominal exchange rate domestic currency per US dollar, from IFS), seasonal lags at 6, 9, 12, and two dummy variables for sharp currency depreciations at WAEMU start (January 1994 and February 1994; for Guinea-Bissau: May 1997 and June 1997).
- Error term u_t ~ N(0, σ_t^2); conditional variance specified as GARCH(p,q) with sum of p and q reported.
- All variables are stationary (Appendix A.1).

### GARCH model performance and diagnostics
- GARCH(p,q) models fit well the mean and variance processes of inflation in all WAEMU countries (see Table 7).
- Ljung-Box Q^2-statistics (LBQ^2) confirm that including GARCH parameters removes heteroscedasticity in residuals.
- Individual z-statistics indicate at least one coefficient in a country’s variance equation is highly significant (except Côte d’Ivoire where only the constant is significant).
- Sum of p and q coefficients is very close to one for Benin, Guinea-Bissau, Mali, Senegal, and Togo, indicating especially persistent volatility shocks.
- Using a measure of core inflation (HPCI excluding "food and non-alcoholic beverages") significantly reduced explanatory power (adjusted R^2 below 10 percent), implying inflation uncertainty is mainly driven by food price volatility.

### Estimated determinants across countries (summary from Table 7 narrative)
- Lagged and seasonal domestic inflation significant in all countries, with differing chronology and signs.
- Significant regional inflation impact present throughout; except for Senegal, there is a significant contemporaneous regional inflation effect in countries.
- Benin, Côte d’Ivoire, Mali, Niger, and Senegal also display significant regional inflation lags.
- Contemporaneous depreciation significant only in Côte d’Ivoire.
- Lagged depreciation effects occur in Benin, Burkina Faso, Mali, and Niger.
- One or another dummy (WAEMU start or Guinea-Bissau accession/depreciation dummy) is always significant except for Burkina Faso, Mali, and Niger.

### Dynamics of inflation uncertainty (Figure 3 summary)
- Time dynamics of the error term conditional variance show inflation uncertainty was mildly cyclical at quite a low level across all countries.
- Main exceptions correspond to:
  - Establishment of the WAEMU in January 1994.
  - The food and fuel and food price crisis in 2008.
  - The civil war in Guinea-Bissau from June 1998 to May 1999.

### GARCH result highlights (selected numerical entries from Table 7)
- Benin (GARCH(1,2)) — Mean equation:
  - Constant: 0.0033 (z-Stat. 3.85)
  - INF_t-1: -0.161 (z-Stat. -2.25)
  - REG_t: 0.542 (z-Stat. 4.18)
  - DUM-Jan94: 0.189 (z-Stat. 5.10)
- Benin — Variance equation:
  - u_t-1^2 coefficient: 0.940 (z-Stat. 18.36)
  - σ_{t-1}^2 coefficient: 0.413 (z-Stat. 2.92)
  - Adj. R-squared: 0.55
  - Akaike criterion: -5.81
  - Schwarz criterion: -5.42
  - LBQ^2 [1]: 0.02; [3]: 2.58; [6]: 3.80
- Burkina Faso (GARCH(1,1)) — Variance:
  - u_t-1^2: 0.571 (z-Stat. 1.62)
  - σ_{t-1}^2: 0.100 (z-Stat. 0.84)
  - Adj. R-squared: 0.41
- Côte d’Ivoire (GARCH(0,1)) — Mean equation:
  - REG_t: 0.939 (z-Stat. 5.83)
  - DUM-Jan94: 0.099 (z-Stat. 3.92)
  - Variance constant: 0.0005 (z-Stat. 5.10)
- Guinea-Bissau (GARCH(1,2)) — Mean equation:
  - REG_t: 0.153 (z-Stat. 2.01)
  - DUM-Feb94 (or accession dummy): -0.150 (z-Stat. -5.39)
  - Variance u_t-1^2: 0.817 (z-Stat. 12.41)
- Mali, Niger, Senegal, Togo entries reported with model selection (GARCH orders) and coefficients in Table 7; see table for specific coefficients and diagnostic statistics.
- Note: Bold indicates significance at the 5 and 10 percent level (Bollerslev-Wooldridge robust QM standard errors). Critical values for LBQ^2 at lags 1, 3, 6 at 5 percent: 3.84, 7.81, 12.59 (at 10 percent: 2.71, 6.25, 10.64).

### Granger causality testing approach
- Two-step Granger test regressions:
  - Equation (3.2): UNC_t on lags of UNC and lags of INF.
  - Equation (3.3): INF_t on lags of INF and lags of UNC.
- Tests assess whether lagged INF coefficients in (3.2) and lagged UNC coefficients in (3.3) are statistically significant as a group.
- Recognizes that choice of lag length may affect results (further details and results follow in the source text beyond the excerpt).

*Italic: Source — IMF working paper content (excerpt)._

### conclusions, we report test results for different lag lengths.

### _wp1159 - conclusions, we report test results for different lag lengths.

### Granger causality between inflation and inflation uncertainty
- Granger test results support the Friedman-Ball hypothesis for all WAEMU countries: average inflation has a positive and statistically significant impact on inflation uncertainty (see Table 8).
- The null hypothesis that inflation does not Granger-cause inflation uncertainty can be rejected at conventional levels of significance in all countries, though at different lag lengths.
- The sum of the coefficients on lagged inflation is positive in the reported tests, confirming the overall positive effect of inflation on inflation uncertainty.
- There is evidence for the Cuckierman hypothesis in some countries: the null that inflation uncertainty does not Granger-cause inflation can be rejected for Benin, Senegal, and Togo at a few lags.
- For Benin, Senegal, and Togo, Granger causality runs two ways at some lag lengths, indicating a feedback process between inflation and inflation uncertainty (both Friedman-Ball and Cuckierman hypotheses hold simultaneously in these cases).

- Selected exact entries from Table 8 (H0: Inflation does not Granger cause inflation uncertainty / H0: Inflation uncertainty does not Granger cause inflation):
  - Benin: 3 lags 6.25***(+) / 1.89 ; 6 lags 5.57***(+) / 2.16**(+) ; 9 lags 8.86***(+) / 1.63(+) ; 12 lags 3.73***(+) / 1.27(+)
  - Burkina Faso: 3 lags 2.14*(+) / 1.27 ; 6 lags 1.92*(+) / 1.61(-) ; 9 lags 1.50(+) / 1.11(+) ; 12 lags 1.60*(+) / 0.88(-)
  - Côte d’Ivoire: 3 lags 1.50(+) / 1.04 ; 6 lags 1.10(+) / 0.32 ; 9 lags 0.65(+) / 0.66 ; 12 lags 2.74***(+) / 0.63
  - Guinea-Bissau: 3 lags 1.17 / 0.15 ; 6 lags 2.60**(+) / 0.91(+) ; 9 lags 3.39***(+) / 0.93(+) ; 12 lags 2.97***(+) / 1.33(+)
  - Mali: 3 lags 0.51 / 1.79 ; 6 lags 2.90***(+) / 0.80 ; 9 lags 2.41**(+) / 0.78(-) ; 12 lags 2.55***(+) / 1.22(-)
  - Niger: 3 lags 2.91**(+) / 2.06(-) ; 6 lags 2.67**(+) / 0.65(-) ; 9 lags 4.75***(+) / 0.51(-) ; 12 lags 3.11***(+) / 0.74(-)
  - Senegal: 3 lags 13.86***(+) / 0.54 ; 6 lags 14.29***(+) / 2.73**(-) ; 9 lags 6.17***(+) / 3.73***(+) ; 12 lags 2.89**(+) / 1.48(-)
  - Togo: 3 lags 20.02***(+) / 2.92**(+) ; 6 lags 11.21***(+) / 1.67 ; 9 lags 27.14***(+) / 1.36(-) ; 12 lags 1.49(+) / 0.82(+)

- Note on notation in Table 8: ***, ** and * indicate significance at the 1, 5 and 10 percent level. (+) and (-) indicate the sign of the sum of the coefficients of lagged inflation in equation (3.2) (resp. lagged uncertainty in equation (3.3)) if at least one lagged coefficient is significant at the 10 percent level.

### VAR(12) impulse-response analysis of inflation shocks on inflation uncertainty
- The study employs VAR(12) regressions (the 12-lag Granger causality tests in equation (3.2)) and uses a Cholesky decomposition to calculate impulse-response functions of inflation uncertainty to inflation shocks (see Figure 4).
- Impulse-response pattern commonalities:
  - Following an inflation shock (from sample mean to one standard deviation above), inflation uncertainty:
    - Quickly rises from its mean,
    - Erratically fluctuates at high levels,
    - Then steadily falls back to the mean typically after periods 10 to 15.
- Cross-country differences:
  - Magnitude, volatility, timing and persistence of responses differ widely across WAEMU countries.
  - In Guinea-Bissau the effect does not die down even after 50 periods.
- Robustness: Employing core instead of headline inflation does not change the results. The same holds for using seasonally adjusted monthly domestic inflation rates.

### Inflation and Relative Price Variability (RPV): measurement and visual evidence
- The RPV measure follows Parks (1978): RPV_t = sum_{i=1}^n w_i (π_{it} - π_t)^2, where w_i is the expenditure weight of subcategory i, π_{it} is the monthly inflation rate for subcategory i, and π_t = sum_{i=1}^n w_i π_{it} is the monthly average rate.
- For WAEMU countries with HCPI of 12 subcategories, n=12 produces headline inflation and RPV.
- Visual inspection (Figure 5) shows joint fluctuation of average HCPI inflation and RPV in all WAEMU countries, though to different extents.
- Note: RPV_t monotonically increases in the difference between individual price movements; its lower bound is zero when all prices change proportionally.

### Empirical specifications tested for RPV
- The paper tests several models to examine how RPV varies with inflation and to disentangle expected (average) vs. unexpected inflation:
  - (OLS-I) Linear specification: RPV_t = α_1 + β_1 π_t + ε_t
    - Text summary: The simple linear relation between inflation and RPV is not supported for most WAEMU countries except Benin, Guinea-Bissau, and Niger, where β_1 takes a value of around 0.01 at high confidence levels.
  - (OLS-II) Nonlinear (relevance of inflation): sqrt(RPV_t) = α_2 + β_2 |π_t| + ε_t
    - Results: Nonlinear specification (OLS-II) fits the data better than linear for all WAEMU countries. β_2 is highly significant, ranging from 0.55 in Guinea-Bissau to 1.03 in Togo.
  - (OLS-III) Asymmetric impact of inflation and deflation:
    - sqrt(RPV_t) = α_3 + β_3 |π_t| + γ_3 DUM_t^- * |π_t| + ε_t, where DUM_t^- = 1 if π_t < 0, 0 otherwise.
    - The asymmetric specification tests whether deflationary periods have a different slope on RPV (downward price rigidity concerns).
    - The estimation corroborates asymmetric impact in half of the WAEMU countries: γ_3 is significantly different from zero in Burkina Faso, Côte d’Ivoire, Mali, and Senegal.

- Selected exact summary statistics and coefficients (Tables 9 and 10; robust t-statistics in italics; ***, **, * denote significance at 1, 5 and 10 percent):
  - OLS-I (linear; Table 9): the text reports that in Benin, Guinea-Bissau, and Niger the coefficient β_1 is around 0.01 at high confidence. (Table 9 entries list R^2 and DW and other formatting; the text emphasizes the small β_1 ≈ 0.01 where significant.)
  - OLS-II and OLS-III (Table 10): β_2 (OLS-II) ranges from 0.55 in Guinea-Bissau to 1.03 in Togo.
    - Examples from Table 10 (α_2, β_2, α_3, β_3, γ_3 with R^2 and DW columns):
      - Benin: OLS-II α_2 0.01*** β_2 0.85*** R^2 0.41 DW 1.96 ; OLS-III α_3 0.01*** β_3 0.85*** γ_3 0.00 R^2 0.41 DW 1.96
      - Guinea-Bissau: OLS-II α_2 0.01*** β_2 0.55*** R^2 0.38 DW 2.22 ; OLS-III α_3 0.01*** β_3 0.56*** γ_3 -0.01 R^2 0.38 DW 2.22
      - Niger: OLS-II α_2 0.01*** β_2 0.71*** R^2 0.69 DW 1.70 ; OLS-III α_3 0.01*** β_3 0.70*** γ_3 0.03 R^2 0.69 DW 1.71
      - Togo: OLS-II α_2 0.01*** β_2 1.03*** R^2 0.50 DW 1.63 ; OLS-III α_3 0.01*** β_3 0.97*** γ_3 0.15 R^2 0.50 DW 1.62
    - The coefficient of absolute aggregate inflation (β_3) remains highly significant in all WAEMU countries (values reported above).
    - γ_3 (deflation slope difference) is significantly different from zero in Burkina Faso, Côte d’Ivoire, Mali and Senegal (exact γ_3 values reported in Table 10).

### Interpretation and implications from the results
- Inflation increases inflation uncertainty across all WAEMU countries (Friedman-Ball supported).
- In some countries (Benin, Senegal, Togo) uncertainty also increases inflation (Cuckierman mechanism), implying bi-directional feedback.
- Inflation shocks produce sizable, persistent increases in inflation uncertainty; the persistence varies greatly across countries (e.g., Guinea-Bissau >50 periods).
- RPV is better explained by nonlinear specifications that use absolute inflation (|π_t|); absolute inflation coefficients are large and significant (β_2 between 0.55 and 1.03).
- There is asymmetric behavior: deflationary periods have distinct effects on RPV in several countries (Burkina Faso, Côte d’Ivoire, Mali, Senegal), consistent with downward price rigidity or sectoral asymmetries.

*Source: _wp1159 - conclusions, we report test results for different lag lengths.*

### 0.12 to 0.24).

### _wp1159 - 0.12 to 0.24).

### Relevance of unexpected inflation
- Regression specification tested (OLS-IV): RPV = α4 + β4|Πe| + μ4|Π - Πe| + εt, where |Πe| is absolute expected headline inflation and |Π - Πe| is absolute unexpected headline inflation (proxy: absolute residuals from GARCH regressions in Section III.A).
- Key estimation findings (Table 11, OLS-IV):
  - Coefficient of unexpected inflation, μ4, generally about 0.51 to 0.74 across WAEMU countries.
  - Togo: μ4 = 1.09.
  - Coefficient of expected inflation, β4, is significant in half the countries and lies between 0.28 (Côte d’Ivoire) and 0.64 (Niger), with Mali and Senegal in between.
  - Except for Niger, β4 is always lower than μ4.
- Robust t-statistics reported using White heteroscedasticity-consistent standard errors and covariance. Significance markers: ***, **, and * indicate significance at the 1, 5 and 10 percent level respectively.

### Asymmetric impact of unexpected inflation and deflation
- Asymmetric specification (OLS-V): RPV = α5 + β5|Πe| + η5 D+|Π - Πe| + ψ5 D-|Π - Πe| + εt, where D+ = 1 if Π - Πe > 0 and D- = 1 if Π - Πe < 0.
- Main results (Table 11, OLS-V):
  - Evidence supports asymmetry in most WAEMU countries.
  - Wald test (W) rejects null of equality between positive and negative unexpected inflation coefficients at the 10 percent level in all WAEMU countries except Mali, Niger and Senegal.
  - Estimated coefficient of unexpected deflation, ψ5, varies between 0.36 (Benin) and 1.09 (Togo).
  - Coefficients on unexpected inflation, η5, vary between 0.53 (Senegal) and 1.09 (Togo).
  - Coefficients for expected inflation, β5, remain almost unchanged compared to OLS-IV; β5 becomes significant for Guinea-Bissau.
- Interpretation:
  - Findings align with signal-extraction models: unexpected inflation has a stronger impact on RPV than expected inflation in most countries.
  - Asymmetry indicates responses differ when unexpected inflation is positive versus negative.

### Policy implications
- Overarching implication: benefits of keeping inflation low, stable, and predictable.
- Specific policy recommendations:
  - A common monetary policy is appropriate given interdependent inflation shocks across WAEMU countries.
  - Foster macroeconomic convergence and regional integration to reduce heterogeneity in transmission.
  - Implement swift policy responses to inflation developments to reduce inflation uncertainty and, where Cukierman-type predictions apply, reduce persistence.
  - Publish information on all major drivers of domestic inflation to rationalize and align inflation and RPV expectations; suggested information includes:
    - the exchange rate,
    - the regional inflation rate and that of the most important regional trading partners,
    - projections of important import and export prices (above all, for food and fuel products).
  - BCEAO could consider adding core to headline inflation as a reference measure (despite the exchange rate peg), and would first need to design and compile a regionally comparable index of domestic core inflation that excludes volatile energy and food prices.
  - Better explain current inflation developments and forecasts to the public to anchor expectations, increase transparency and accountability.
  - Enhance coordination of national fiscal policies and capacity building to react to idiosyncratic shocks; gear national reserve requirements toward countering cyclical fluctuations in individual WAEMU countries.
  - Eliminate national differences in laws relating to administered prices, customs exemptions, and tax rates to align firms’ price-setting behavior across the region.
- Additional policy notes:
  - Food and fuel prices act as supply-side factors driving inflation uncertainty and RPV irrespective of monetary policy.
  - Food subcategory weights in the CPI vary between 32 and 60 percent across countries and contribute substantially to RPV.

### Conclusions
- The paper documents two channels through which higher inflation can have real effects:
  - Inflation increases inflation uncertainty.
  - Expected and unexpected inflation increase relative price variability (RPV).
- Sample: all eight WAEMU members from January 1994 through December 2009.
- Heterogeneity: pattern, magnitude, and timing of both channels are quite heterogeneous across the region, complicating common monetary policy implementation.
- Policy takeaway: keep inflation low, stable, and predictable; pursue macroeconomic convergence, closer integration, domestic policy coordination, and harmonized use of national monetary policy instruments to homogenize transmission and mitigate heterogeneous real effects.
- Suggested avenues for future research:
  - Distinguish short- from long-term inflation uncertainty.
  - Use micro data on firms’ pricing behavior, remittances, and monetary instruments to better understand RPV.
  - Employ threshold models or endogenous break-point tests to identify critical inflation thresholds.
  - Study real output and welfare consequences of inflation-induced increases in inflation uncertainty and RPV.

### Appendix — Selected robustness and diagnostic results
- Stationarity and heteroscedasticity:
  - Monthly inflation rate appears stationary; augmented Dickey-Fuller (ADF) test rejects a unit root at the 1 percent level for all countries.
  - Residual variance is not constant in most countries: Ljung-Box Q2-statistic (LBQ2) at lags 1, 3, or 6 rejects null of no serial correlation in squared OLS residuals for all countries except Niger, indicating presence of ARCH in OLS residuals for all countries other than Niger.
  - Table A.1 reported ADF test statistics and LBQ2 statistics; ADF test values include: Benin -12.14, Burkina Faso -8.70, Côte d’Ivoire -6.60, Guinea-Bissau -12.46, Mali -9.58, Niger -8.83, Senegal -9.60, Togo -10.30. Note: critical value at 1 percent level is -3.47.
- Robustness to core inflation (Appendix A.2):
  - Re-estimation excluding CPI subcategory (i) “food and non-alcoholic beverages” (core inflation) shows results robust: coefficients on core inflation are slightly higher than on headline inflation.
  - Evidence on price rigidities largely robust to use of core inflation and core RPV; for example, support for nonfood price rigidities found in Benin only, while food prices appear downward sticky in Burkina Faso, Côte d’Ivoire, Mali and Senegal.
  - Table A.2 reports core-inflation regression statistics and robust t-statistics; selected coefficient patterns remain consistent with headline-inflation results.

*Source: _wp1159 - 0.12 to 0.24).*

### BIBLIOGRAPHY

### BIBLIOGRAPHY

### References
- Aarstol, M., 1999, ―Inflation, Inflation Uncertainty and Relative Price Variability,‖ Southern Economic Journal, Vol. 66, pp. 414–23.
- Adam, K., and R.M. Billi, 2006, ―Optimal Monetary Policy Under Commitment with a Zero Bound on Nominal Interest Rates,‖ Journal of Money, Credit, and Banking, Vol. 38, pp. 1877–905.
- Apergis, N., 2005, ―Inflation Uncertainty and Growth: Evidence from Panel Data,‖ Australian Economic Papers, Vol. 44, pp. 186–97.
- Ball, L., 1992, ―Why Does High Inflation Rise Inflation Uncertainty?‖ Journal of Monetary Economics, Vol. 29, pp. 371–88.
- Balderas, J.U., and R. Garcia-Contreras, 2009, ―The Effects of Remittances and Relative Price Variability on Inflation: Evidence from 32 Mexican States,‖ Indian Journal of Economics and Business, Vol. 8, pp. 269–78.
- Bamba, L., 2004, ―Analyse du Processus de Convergence Dans la Zone UEMOA,‖ UNU WIDER Research Paper 2004/18.
- Barro, R., 1976, ―Rational Expectations and the Role of Monetary Policy,‖ Journal of Monetary Economics, Vol. 2, pp. 1–32.
- Baye, M. R., J. Morgan, and P. Scholten, 2006, ―Information, Search, and Price Dispersion,‖ in T. Hendershott (ed.), Handbook of Economics and Information Systems (Amsterdam: Elsevier).
- Becker, S., and D. Nautz, 2009, ―Inflation and Relative Price Variability: New Evidence for the United States,‖ Southern Economic Journal, Vol. 76, pp. 146–64.
- Benati, L., and P. Surico, 2008, ―Evolving US Monetary Policy and the Decline of Inflation Predictability,‖ Journal of the European Economic Association, Vol. 6, pp. 634–46.
- Berument, H., and E. Yuksel, 2007, ―Effects of Adopting Inflation Targeting Regimes on Inflation Variability,‖ Physica A, Vol. 375, pp. 265–73.
- Berument, H., Z. Kilinc, and U. Ozlale, 2005, ―The Missing Link between Inflation Uncertainty and Interest Rates,‖ Scottish Journal of Political Economy, Vol. 52, pp. 222–41.
- Berument, H. and N.N. Dincer, 2005, ―Inflation and Inflation Uncertainty in the G7 Countries,‖ Physica A, Vol. 348, pp. 371–79.
- Bhar, R., and S. Hamori, 2004, ―The Link Between Inflation and Inflation Uncertainty: Evidence from G7 Countries,‖ Empirical Economics, Vol. 29, pp. 825–53.
- Bick, A., and D. Nautz, 2008, ―Inflation Thresholds and Relative Price Variability: Evidence from U.S. Cities,‖ International Journal of Central Banking, Vol. 4, pp. 61–76.
- Binette, A., and S. Martel, 2005, ―Inflation and Relative Price Dispersion in Canada: An Empirical Assessment,‖ Bank of Canada Working Paper 2005-28.
- Blejer, M., and L. Leiderman, 1980, ―On the Real Effects of Inflation and Relative Price Variability: Some Empirical Evidence,‖ Review of Economics and Statistics, Vol. 62, pp. 539–44.
- Bordo, M., 1993, ―The Gold Standard, Bretton Woods and Other Monetary Regimes: A Historical Appraisal,‖ Review of Federal Reserve Bank of St. Louis, March-April, pp. 175–78.
- Cabellero, R., 1991, ―On the Sign of the Investment-Uncertainty Relationship,‖ American Economic Review, Vol. 81, pp. 279–88.
- Caglayan, M., and A. Filiztekin, 2003, ―Nonlinear Impact of Inflation on Relative Price Variability,‖ Economics Letters, Vol. 79, pp. 213–18.
- Caporale, G.M., L. Onorante, and P. Paesani, 2009, ―Inflation and Inflation Uncertainty in the Euro Area,‖ CESifo Working Paper No. 2720.
- Caporale, G.M., and A.Kontonikas, 2009, ―The Euro and Inflation Uncertainty in the European Monetary Union,‖ Journal of International Money and Finance, Vol. 28, pp. 954–71.
- Caraballo, M.A., C. Dabus, and C. Usabiaga, 2006, ―Relative Prices and Inflation: New Evidence from Different Inflationary Contexts,‖ Taylor and Francis Journals, Vol. 28, pp. 1931–44.
- Çatik, A.N., C. Martin, and A.Ö. Önder, 2008, ―Relative Price Variability and the Philips Curve: Evidence from Turkey,‖ Ege University Working Paper 0807.
- Chang, K.-L., and C.-W. He, 2010, ―Does the Magnitude of the Effect of Inflation Uncertainty on Output Growth Depend on the Level of Inflation?,‖ Manchester School, Vol. 78, pp. 126–48.
- Chang, E., and J. Cheng, 2002, ―Further Evidence on the Variability of Inflation and Relative Price Variability,‖ Economic Letters, Vol. 66, pp. 71–77.
- Choi, C.-Y., 2010, ―Reconsidering the Relationship between Inflation and Relative Price Variability,‖ Journal of Money, Credit and Banking, Vol. 52, pp. 769–98.
- Cogley, T., G.E. Primiceri, and T.J. Sargent, 2010, ―Inflation-Gap Persistence in the US,‖ American Economic Journal: Macroeconomics, Vol. 2, pp. 43–69.
- Conrad, C., and M. Karanasos, 2008, ―Modeling Volatility Spillovers Between the Variabilities of US Inflation and Output: The UECCC GARCH Model,‖ University of Heidelberg Working Papers 0475.
- _____, 2005, ―On the Inflation Uncertainty Hypothesis in the USA, Japan, and the UK: A Dual Long Memory Approach,‖ Japan and the World Economy, Vol. 17, pp. 327–43.
- Cukierman, A., 1992, Central Bank Strategy: Credibility and Independence (Cambridge, MA: MIT Press).
- _____, 1984, Inflation, Stagflation, Relative Prices, and Imperfect Information (Cambridge, MA: Cambridge University Press).
- _____, 1983, ―Relative Price Variability and Inflation: A Survey and Further Results,‖ in K. Brunner and A. Meltzer (eds.), Variability in Employment, Prices and Money, pp. 103–57 (Amsterdam: Elsevier Science Publishers).
- Daal, E., A. Naka, and B. Sanchez, 2005, ―Re-Examining Inflation and Inflation Uncertainty in Developed and Emerging Countries,‖ Economics Letters, Vol. 89, pp. 180–86.
- Davis, G.K., and B.E. Kanago, 2000, ―The Level and Uncertainty of Inflation: Results from OECD Forecasts,‖ Economic Enquiry, Vol. 1, pp. 58–72.
- Entezarkheir, M., 2006, ―The Relation Between Inflation and Inflation Uncertainty in Iran,‖ Iranian Economic Review, Vol. 10, pp. 1–20.
- Evans, M., 1991,―Discovering the Link Between Inflation Rates and Inflation Uncertainty,‖ Journal of Money, Credit, and Banking, Vol. 23, pp. 169–84.
- Fielding, D., and P. Mizen, 2008, ―Evidence on the Functional Relationship Between Relative Price Variability and Inflation with Implications for Monetary Policy,‖ Economica, Vol. 75, pp. 683–99.
- Fielding, D., K. Lee, and K. Shields, 2004, ―The Characteristics of Macroeconomic Shocks in the CFA Franc Zone,‖ Journal of African Economies, Vol. 13, pp. 488–517.
- Fielding, D., and K. Shields, 2005, ―The Impact of Monetary Union on Macroeconomic Integration: Evidence from West Africa,‖ Economica, Vol. 72, pp. 683–704.
- Fielding, D., and P. Mizen, 2000, ―Relative Price Variability and Inflation in Europe,‖ Economica, Vol. 67, pp. 57–78.
- Fountas, A., and M. Karanasos, 2007, ―Inflation, Output Growth, and Nominal and Real Uncertainty: Empirical Evidence for the G7,‖ Journal of International Money and Finance, Vol. 26, pp. 229–50.
- Fountas, A., A. Ioannidis, and M. Karanasos, 2004, ―Inflation, Inflation Uncertainty, and a Common European Monetary Policy,‖ Manchester School, Vol. 72, pp. 221–42.
- Friedman, M., 1977, ―Inflation and Unemployment (Nobel Lecture),‖ Journal of Political Economy, Vol. 85, pp. 451–72.
- Golob, J., 1993, ―Inflation, Inflation Uncertainty, and Relative Price Variability: A Survey,‖ Federal Reserve Bank of Kansas City Research Working Paper No. 93-115.
- Gomes, C., 2007, ―Política Monetária e Variabilidade dos Preços Relativos: Uma Análise do Caso Brasileiro,‖ ANPEC Working Paper.
- Goretti, M., and H. Weisfeld, 2008, ―Trade in the WAEMU: Developments and Reform Opportunities,‖ IMF Working Paper No. 08/68.
- Golosov, M., and R.E. Lucas, 2007, ―Menu Costs and Phillips Curves,‖ Journal of Political Economy, Vol. 115, pp. 171–99.
- Grier, K., and R. Grier, 2006, ―On the Real Effects of Inflation and Inflation Uncertainty in Mexico,‖ Journal of Development Economics, Vol.80, pp. 478–500.
- Grier, K., O. Henry, N. Olekalns, and K. Shields, 2004, ―The Asymmetric Effects of Uncertainty on Inflation and Output Growth,‖ Journal of Applied Econometrics, Vol. 5, pp. 551–65.
- Hercowitz, Z., 1981, ―Money and Dispersion of Relative Prices,‖ Journal of Political Economy, Vol. 89, pp. 328–56.
- Jaramillo, C., 1999, ―Inflation and Relative Price Variability: Reinstating Parks’ Results,‖ Journal of Money, Credit and Banking, Vol. 31, pp. 375–85.
- Karanasos, M., and J. Kim, 2005, ―On the Existence or Absence of a Variance Relationship: A Study of Macroeconomic Uncertainty,‖ WSEAS Transactions on Computers, Vol. 4, pp. 1475–82.
- Keskek, S., and M. Orhan, 2008, ―Inflation and Inflation Uncertainty in Turkey,‖ Applied Economics, Vol. 99, pp. 1–11.
- Klenow, P.J., and O. Kryvtsov, 2008, ―State-Dependent or Time-Dependent Pricing: Does It Matter for Recent U.S. Inflation?‖ Quarterly Journal of Economics, Vol. 123, pp. 863–904.
- Konieczny, J., and A. Skrzypacz, 2005, ―Inflation and Price Setting in a Natural Experiment,‖ Journal of Monetary Economics, Vol. 52, pp. 621–32.
- Lastrapes, W. D., 2006, ―Inflation and the Distribution of Relative Prices: The Role of Productivity and Money Supply Shocks,‖ Journal of Money, Credit, and Banking, Vol. 38, pp. 2159–98.
- Lucas, R., 1973, ―Some International Evidence on Output-Inflation Tradeoffs,‖ American Economic Review, Vol. 63, pp. 326–34.
- Mishkin, F.S., and N.J. Westelius, 2008, ―Inflation Band Targeting and Optimal Inflation Contracts,‖ Journal of Money, Credit and Banking, Vol. 40, 557–82.
- Nath, H.K., 2004, ―Inflation and Relative Price Variability: Short-Run vs. Long-Run,‖ Economics Letters, Vol. 82, pp. 363–69.
- Nautz, D., and J. Scharff, 2007, ―Inflation and Relative Price Variability in the Euro Area: Evidence from a Panel Threshold Model,‖ University of Frankfurt Discussion Paper.
- ___________, 2005, ―Inflation and Relative Price Variability in a Low Inflation Country: Empirical Evidence for Germany,‖ German Economic Review, Vol. 6, pp. 507–23.
- Parks, R.W., 1978, ―Inflation and Relative Price Variability,‖ Journal of Political Economy, Vol. 86, pp. 79–95.
- Parsley, D., and H. Popper, 2002, ―Inflation and Relative Price Dispersion in Equity Markets and in Goods and Services Markets,‖ Vanderbilt University Discussion Paper.
- Payne, J.E., 2008, ―Inflation and Inflation Uncertainty: Evidence from the Caribbean Region,‖ Journal of Economic Studies, Vol. 35, pp. 501–11.
- Rizvi, S., K. Abbas, and B. Naqvi, 2008, ―Asymmetric Behavior of Inflation Uncertainty and Friedman-Ball Hypothesis: Evidence from Pakistan,‖ University of Munich, manuscript.
- Rogoff, K., 2007, ―The Impact of Globalization on Monetary Policy,‖ in The new Economic Geography: Effects and Policy Implications (Federal Reserve Bank of Kansas City).
- Rotemberg, J., 1983, ―Aggregate Consequences of Fixed Costs of Price Adjustment,‖ American Economic Review, Vol. 73, pp. 433–36.
- Sahin, A., B. Saracoglu, and M.H. Berument, 2009, ―The Choice of Monetary Policy Tool(s) and Relative Price Variability: Evidence from Turkey,‖ Journal of Applied Sciences, Vol. 9, pp. 2238–46.
- Samimi, A., and B. Shahryar, 2009, ―Inflation Uncertainty and Economic Growth in Iran,‖ Australian Journal of Basic and Applied Sciences, Vol. 3, pp. 2919–25.
- Sauer, C., and A. Bohara, 1995, ―Monetary Policy and Inflation Uncertainty in the United States and Germany,‖ Southern Economic Journal, Vol. 62, pp. 139–63.
- Schwert, G., 1989, ―Test for Unit Roots: A Monte Carlo Investigation,‖ Journal of Business and Economic Statistics, Vol. 7, pp. 147–59.
- Sheshinsky, E., and Y. Weiss, 1977, ―Inflation and Costs of Price Adjustment,‖ Review of Economic Studies, Vol. 44, pp. 287–303.
- Shortland, A., and D. Stasavage, 2004a, ―What Determines Monetary Policy in the Franc Zone? Estimating a Reaction Function for the BCEAO,‖ Journal of African Economies, Vol. 13, pp. 518–35.
- ____________, 2004b, ―Monetary Policy in the Franc Zone Estimating Interest Rate Rules for the BCEAO,‖ UNU Wider Research Paper No. 2004/20.
- Silver, M., and C. Ioannidis, 2001, ―Intercountry Differences in the Relationship Between Relative Price Variability and Average Prices,‖ Journal of Political Economy, Vol. 109, pp. 355–74.
- Soderstrom, U., 2002, ―Monetary Policy with Uncertain Parameters,‖ Scandinavian Journal of Economics, Vol. 104, pp. 125–45.
- Thornton, J., 2007a, ―The Relationship Between Inflation and Inflation Uncertainty in Emerging Market Economies,‖ Southern Economic Journal, Vol. 73, pp. 858–70.
- _____, 2007b, ―Inflation and Inflation Uncertainty in Argentina,‖ Economics Letters, Vol. 95, pp. 140–45.
- _____, 2006, ―High and Variable Inflation: Further Evidence on the Friedman Hypothesis,‖ South African Journal of Economics, Vol. 74, pp. 167–71.
- Veyrune, R., 2007, ―Fixed Exchange Rates and the Autonomy of Monetary Policy: The Franc Zone Case,‖ IMF Working Paper No. 07/34.
- William, M., and C.-P.C. Vijverberg, 2009, ―Changing Inflation Dynamics and Uncertainty in the United States,‖ Southern Economic Journal, Vol. 75, pp. 736–49.
- Wilson, B., 2006, ―The Links between Inflation, Inflation Uncertainty, and Output Growth: New Time Series Evidence from Japan,‖ Journal of Macroeconomics, Vol. 28, pp. 609–20.
- Woodford, M., 2003, Interest and Prices: Foundations of a Theory of Monetary Policy (Princeton, NJ: Princeton University Press).
- Wozniak, P., 1998, ―Relative Prices and Inflation in Poland 1989–1997: The Special Role of Administered Price Increases,‖ World Bank Policy Research Working Paper No. 1879.

*Source: _wp1159 - BIBLIOGRAPHY*

---


_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2011/_wp1159.pdf_