## _wp05237

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### I. Introduction — scope and contributions
- Fiscal outcomes in developing countries are more volatile and highly procyclical than in industrial economies.
- Identified causes:
  - Limited and volatile access to international financial markets.
  - Frequent discretionary public spending changes tied to political cycles.
  - High dependence on cyclically sensitive revenue sources (indirect taxes, state-owned natural resource transfers).
- Venezuela-specific structural facts (real terms, 1991–2002):
  - Oil sector contributes about 25 percent of total GDP.
  - Oil sector provides about 50 percent of public sector revenues.
  - Oil sector accounts for about 80 percent of exports.
  - Correlation between the oil price (Venezuelan basket) and the oil sector nominal GDP: 0.80.
  - Correlation between the oil price (Venezuelan basket) and total nominal GDP: 0.60.
- Exchange-rate regime and controls:
  - February 2003: FX controls introduced and shift from floating to fixed exchange rate.
  - Official exchange rate: initially Bs 1,600 per US$; devalued to Bs 1,920 in February 2004 and to Bs 2,150 in March 2005.
  - Parallel market exchange rate noted as 2,750 Bs per US$.
- Paper’s three contributions:
  1. Single-country time series business-cycle analysis for an oil-producing emerging market, disaggregating oil and non-oil cyclical components.
  2. Comparison of Venezuela’s fiscal behavior with selected emerging market economies; measurement of discretionary and cyclical indicators across public revenue and spending components; recommendation of the non-oil primary balance as a more reliable indicator of fiscal stance.
  3. Assessment of fiscal policy over the business cycle using four econometric filtering approaches to estimate the output gap: Hodrick-Prescott (HP) filter; ARIMA Model-Based (AMB) approach; Modified HP (Kaiser and Maravall (2004)); Band-pass filter (Ouliaris and others (2002)).

### II. Stylized facts about volatility of revenues and expenditures (cross-country)
- Relation between growth and volatility (26-country sample, 1960–2000):
  - OLS: y = 3.81 - 0.39x
  - R2 = 0.165.
  - Quantification: a percentage point increase in output volatility decreases output growth by 0.4 percent.
- Revenue–expenditure volatility correlation (33-country sample, 1990–2002):
  - Regression: y = 0.61 + 0.47x
  - R2 = 0.52.
  - Quantification: a percentage point increase in revenue volatility increases expenditure volatility by almost 1.03 (reported coefficient).
- Cross-country benchmarks and medians:
  - Median sample (without outliers Nigeria, Turkey, Ukraine): expenditure volatility = 1.9 percent of GDP; revenue volatility = 2.9 percent of GDP.
  - Average revenue and expenditure volatility benchmarks cited: 2 percent and 3 percent of GDP, respectively.
- Oil and commodity exporters (examples: Brazil, Costa Rica, Nigeria, Venezuela) show highest volatility in both revenues and expenditures.
- Venezuela recorded an annual average standard deviation of around 4 percent of GDP for public sector revenue and expenditure volatility during 1990–2002.

### III. Causes and dynamics of oil-related fiscal volatility
- Oil price volatility drivers: time-varying excess capacity, discrete technological jumps, uncertain demand, changing geopolitical reasons.
- Absence of hedging implies oil price volatility directly affects oil revenues.
- Other drivers of fiscal volatility and procyclicality:
  - Instability of private consumption and reliance on indirect taxation.
  - Discretionary policies that deepen recessions.
  - Political incentives and "voracity effects" leading to excessive spending.
  - International credit constraints preventing borrowing in recessions.
- Reverse causality: procyclical fiscal policy can amplify economic fluctuations and increase risk of prolonged recessions.

### IV. Policy recommendations to reduce oil-related revenue volatility
- Adjust the non-oil balance of the public sector in line with oil price movements to guarantee a sustainable level of non-oil primary deficit.
- Accumulate substantial financial assets during periods of high oil production.
- Formulate accurate oil price assumptions.
- Operational guidance referenced (preserved): Barnett and Ossowski (2003) and Daniel (2001).

### V. Data, detrending methods and identification strategy (Venezuela dataset)
- Dataset:
  - Quarterly data for 13 time series during 1990–2003 (total GDP, non-oil GDP, oil GDP, oil price by WEO and Venezuelan basket, fiscal variables including total revenues, oil and non-oil revenues, seigniorage, total expenditures, non-oil expenditures, non-oil primary expenditures, non-oil primary balance).
  - Fiscal data available 1995–end-2003; GDP quarterly data available from 1991 to end-2004.
  - All series expressed in real terms using a GDP deflator.
- Four detrending/filtering approaches applied:
  - Hodrick-Prescott (HP) filter: λ =1600 for quarterly data.
  - ARIMA Model-Based (AMB) approach (Kaiser and Maravall (2000)); optimal ARIMA specifications estimated with TRAMO-SEATS:
    - Total GDP: ARIMA (2,2,0) on second differences.
    - Oil GDP: IMA (0,1,1).
    - Non-oil GDP: IMA (0,1,3).
  - Frequency-domain / band-pass filter (Corbae, Ouliaris, Phillips 2002): chosen frequency band between 0.31 and 1.05 (time duration between 6 and 20 quarters).
  - Modified HP (MHP, Kaiser and Maravall 2004): combines AMB and HP to target business cycle frequencies between two and eight years (8 to 32 quarters).

### VI. Business-cycle properties of GDP and fiscal variables (filter-comparison results)
- Volatility (key magnitudes and comparisons):
  - GDP cyclical component is more volatile than its trend by a factor of 1.2 (cycle-to-trend normalization).
  - Non-oil GDP cyclical volatility is two times that of oil GDP cyclical volatility (across filters).
  - Trend component of oil GDP is more than twice volatile as its cycle.
  - Oil revenue (HP cyclical component) standard deviation = 6.3 billions of Bolívares (real terms); oil revenue is more volatile than its HP trend by a factor of 3.
  - Under AMB, oil revenue identified as an MA(2) process (impacted only by events up to two quarters).
  - Non-oil primary expenditures can be up to twice as volatile as non-oil revenues.
  - Standard HP filter yields larger volatility statistics: on average HP is more volatile by a factor of 2 compared with MHP and by a factor of 3 with AMB.
- Persistence (first-order autocorrelation, MHP benchmark):
  - GDP: 0.92
  - Total expenditure: 0.93
  - Non-oil primary balance: 0.90
  - Oil GDP: 0.88
  - Non-oil GDP: 0.73
  - Non-oil revenues: 0.92
  - Non-oil primary expenditures: 0.88
  - Trends are more persistent than cycles; band-pass/MHP yield higher persistence than HP or AMB.
- Cyclicality (contemporaneous correlations with GDP):
  - Non-oil primary expenditures: correlation ratios between 0.41 and 0.52 (strongly procyclical).
  - Non-oil expenditures: mild positive correlation with real GDP.
  - Total expenditures: weakly procyclical with positive ratios between 0.21 and 0.44.
  - Non-oil primary balance: negatively correlated with GDP with correlation ratios of -0.37 (HP cyclical) and -0.39 (MHP cyclical); interpreted operationally as a worsening of the non-oil primary balance with each percentage point increase in real GDP (source text cuts off exact operational magnitude).
  - Total revenues appear independent of GDP cycle (correlation ratios close to zero or weakly negative); disaggregated:
    - Non-oil revenues: coefficient between 0.40 and 0.50 (procyclical).
    - Oil revenues: correlation ratio = -0.18 (weakly negatively related to GDP cycle).

### VII. Fiscal policy behavior, discretionary vs cyclical components (1995–2003)
- Preferred fiscal indicator: non-oil primary balance.
- Two decomposition approaches used: ARIMA-based AMB and structural fiscal balance using potential output (MHP trend).
- Volatility (Table 4 context, Billions of Bolívares):
  - Non-oil primary balance unadjusted volatility = 6.91; discretionary component volatility = 3.04.
  - Non-oil primary expenditures unadjusted volatility = 6.43; discretionary = 2.54.
  - Oil revenues volatility = 6.89.
  - Non-oil revenues volatility = 2.65.
- Correlations with oil revenues:
  - Non-oil primary balance unadjusted correlation with oil revenues = -0.06.
  - Discretionary component of non-oil primary balance correlation with oil revenues = 0.24 (positive).
  - Non-oil primary expenditures unadjusted correlation with oil revenues = -0.04.
  - Discretionary component of non-oil primary expenditures correlation with oil revenues = -0.27 (negative).
- Interpretation:
  - Extracting discretionary components reduces volatility of non-oil primary balances and expenditures by more than half, indicating a large cyclical contribution to volatility.
  - Positive correlation of discretionary non-oil primary balance with oil revenues (0.24) and negative correlation of discretionary non-oil primary expenditure with oil revenues (-0.27) suggest deviations from an optimal path during 1995–2003.
- Fiscal impulse and structural balance definitions:
  - Structural balance: Gap_str B = B - η × Gap_Y, with η = 0.39 (elasticity proxy).
  - Output gap (Gap_Y): cyclical component in percent of potential output; potential output = trend from Modified HP using AMB.

### VIII. Fiscal-stanza dynamics and numeric examples (1995–2003, Table 5 highlights)
- Non-oil primary balance (Unadjusted, Billions of Bolívares):
  - 1995 = -12.6; 1996 = -9.6; 1997 = -35.3; 1998 = -19.7; 1999 = -30.2; 2000 = -40.4; 2001 = -65.4; 2002 = -45.6; 2003 = -41.2.
  - 1996–2001 average = -30.5; st. dev. = 17.8.
  - 2002–03 average = -43.4; st. dev. = 2.2.
- Discretionary component examples:
  - 1995 = -13.3; 1996 = -17.8; 1997 = -22.5; 1998 = -27.1; 1999 = -32.2; 2000 = -38.0; 2001 = -43.0; 2002 = -46.1; 2003 = -48.0.
  - 1996–2001 average = -27.7; st. dev. = 10.0.
  - 2002–03 average = -47.0; st. dev. = 0.9.
- Cyclical component examples:
  - 1995 = -2.1; 1996 = 6.7; 1997 = -8.9; 1998 = -0.4; 1999 = 16.9; 2000 = -2.2; 2001 = -17.2; 2002 = 0.0; 2003 = 7.2.
  - 1996–2001 average = -1.0; st. dev. = 10.0.
  - 2002–03 average = 3.6; st. dev. = 3.6.
- Fiscal impulse (examples, series values):
  - 1995 = 4.5; 1996 = 4.7; 1997 = 4.7; 1998 = 5.1; 1999 = 5.8; 2000 = 5.0; 2001 = 3.1; 2002 = 1.8; 2003 = 5.0.
  - 1996–2001 average = 0.4; st. dev. = 2.5.
  - 2002–03 average = 0.6.
- Output gaps (percent of trend) examples:
  - 1995 = -1.8; 1996 = 7.2; 1997 = -6.6; 1998 = -1.7; 1999 = 8.0; 2000 = 6.9; 2001 = -8.5; 2002 = -9.7; 2003 = 2.0.
  - 1996–2001 average = 5.6; st. dev. = -9.1; 2002–03 average = 0.6.
- Medium-run cumulative changes:
  - 1995–1998 cumulative discretionary non-oil primary expenditure growth = 29 percent (real terms).
  - 1995–1998 cumulative non-oil revenues growth = 8 percent (real terms).
  - 2001–2003 recession: non-oil revenue fell by about 15 percent (real terms, cumulative); about three times more than non-oil primary expenditures on a cumulative basis.

### IX. Synthesis and methodological implications
- Filter choice materially affects measured volatility, persistence, and cyclicality:
  - Standard HP and AMB cycles are more erratic (HP and AMB cross zero line ~16 and ~28 times respectively in referenced figures) than band-pass/MHP (~7 zero crossings).
  - Band-pass (selected 6–20 quarter band) and MHP reduce high-frequency noise and yield higher persistence and lower volatility estimates for business cycles.
- Oil-sector fluctuations in Venezuela appear driven more by trend (low-frequency, possibly permanent) shocks than by high-frequency cyclical movements.
- Fiscal policy during 1995–2003 behaved procyclically, particularly via non-oil primary expenditures, implying expansionary behavior in “good” times and contractionary behavior in “bad” times.
- Non-oil primary balance is recommended as the preferred indicator for fiscal stance and sustainability in oil-producing economies.

### X. Key numerical highlights (selected exact figures)
- Oil sector: about 25 percent of GDP; about 50 percent of public revenues; about 80 percent of exports.
- Correlations: oil price–oil GDP = 0.80; oil price–total GDP = 0.60.
- Volatility benchmarks and estimates:
  - Venezuela public revenue/expenditure annual average standard deviation ≈ 4 percent of GDP (1990–2002).
  - Non-oil primary balance volatility (unadjusted) = 6.91 (Billions of Bolívares); discretionary = 3.04.
  - Oil revenues volatility = 6.89 (Billions of Bolívares).
  - Non-oil revenues volatility = 2.65 (Billions of Bolívares).
- Persistence (Modified HP cyclical-component benchmark):
  - GDP = 0.92; Total expenditure = 0.93; Non-oil primary balance = 0.90.
- Elasticity used in structural balance: η = 0.39.

*Source: _wp05237 (IMF working paper content provided in the input PDF chapter/section).*

### References..............................................................................................................

### _wp05237 - References..............................................................................................................

### I. INTRODUCTION
- Fiscal outcomes in developing countries tend to be both volatile—much more volatile than in the industrial economies—and highly procyclical (expansionary in “good” times and contractionary in “bad” times).
- Causes identified:
  - Limited and volatile access to international financial markets, especially during recessionary periods.
  - Frequent changes in the discretionary component of public spending responding to political cycles.
  - High dependence on cyclically sensitive revenue sources such as indirect taxes and transfers from state-owned natural resource industries.
- Venezuela-specific facts (real terms, period 1991–2002):
  - Oil sector contributes about 25 percent of total GDP.
  - Oil sector provides about 50 percent of public sector revenues.
  - Oil sector accounts for about 80 percent of exports.
  - Correlation between the oil price (Venezuelan basket) and the oil sector nominal GDP: 0.80.
  - Correlation between the oil price (Venezuelan basket) and total nominal GDP: 0.60.
- Structural context:
  - February 2003: introduction of wide-ranging foreign exchange (FX) controls and shift from floating to fixed exchange rate regime to halt reserve depletion.
  - Official exchange rate initially set at Bolívares (Bs) 1,600 per US$; devalued to Bs 1,920 in February 2004 and to Bs 2,150 in March 2005.
  - Parallel market exchange rate noted as 2,750 Bs per US$.
- Paper’s three main contributions:
  1. Single-country time series business-cycle analysis for an oil-producing emerging market (Venezuela), disaggregating oil and non-oil cyclical components.
  2. Comparison of Venezuela’s fiscal behavior with selected emerging market economies and measurement of discretionary and cyclical indicators across public revenue and spending components; selection of the non-oil primary balance as a more reliable indicator of fiscal stance.
  3. Assessment of fiscal policy over the business cycle using four econometric filtering approaches to estimate the output gap:
     - Hodrick-Prescott (HP) filter (time-domain).
     - Two-step ARIMA identification followed by frequency-domain cyclical estimation (AMB approach).
     - Modified HP filter (Kaiser and Maravall (2004) variant).
     - Band-pass filter (frequency-domain, Ouliaris and others (2002) construction).

- Paper structure:
  - Section II: stylized facts on volatility of public revenue and expenditure (cross-section of selected emerging markets).
  - Section III: overview of macro and fiscal trends in Venezuela, 1991–2003.
  - Section IV: methodology for measuring unobserved components (trends and cycles) and Venezuela’s output gap.
  - Section V: application of methodologies to fiscal policy conduct in Venezuela, 1995–2003.
  - Section VI: estimation of fiscal stance using fiscal impulse and output gap analysis.
  - Concluding remarks in final section.

### II. STYLIZED FACTS ABOUT THE VOLATILITY OF REVENUES AND EXPENDITURES
- Empirical background:
  - Recent empirical and theoretical studies indicate that growth and business-cycle volatility might be related; countries with more volatile fluctuations display lower long-term output growth rates.
  - Extensive evidence shows fiscal outcomes are more volatile and procyclical in developing economies than in industrial countries.
- Cross-country illustrations (figures referenced in source):
  - Figure 2: simple correlations between output growth and its volatility (1960–2000) for selected middle-income and emerging economies.
    - Output growth measured by average annual growth rate of real per capita GDP.
    - Volatility measured by standard deviation of real per capita GDP growth.
  - Figure 3: correlations between volatilities of public revenues and expenditures (percent of GDP) between 1990 and 2002 for the same group.
    - Volatility of fiscal outcomes measured by annual standard deviation of total public expenditures and revenues over 1990–2002.
- Cross-country regression results:
  - OLS regression of annual mean growth of GDP per capita (∆yi) on its standard deviation, σ(∆y)i, for the 26-country sample (1960–2000):
    (2.17)
    (4.60)
    3.81    0.39   ()
    ii
    yyσ
    −
    ∆=−  ∆          (1)
    (R2 = 0.165, t-statistics in parentheses).
    - Interpretation: volatility is harmful for growth; negative and statistically significant relationship between growth and volatility.
    - Quantification: a percentage point increase in output volatility decreases output growth by 0.4 percent.
    - Note: low R-squared implies volatility explains only up to 16 percent of variation in negative growth.
    - Note: Nigeria and Venezuela experienced average negative output per capita growth during 1960–2000; Nigeria’s output growth volatility was two times that of Venezuela.
  - OLS regression of annual average public expenditure volatility (σi(g)) on annual average public revenue volatility (σi(τ)) for the 33-country sample (1990–2002):
    (1.92)(5.79)
    (   )    1.03   1.09(  )
    ii
    gσστ=+         (2)
    (R2 = 0.52, t-statistics in parentheses).
    - Interpretation: strongly positive correlation between revenue and expenditure volatility.
    - Quantification: a percentage point increase in revenue volatility increases expenditure volatility by almost 1.03 (as reported in the regression coefficient).

### Key methodological notes (as used in the paper)
- Four detrending/filtering approaches are applied to estimate the output gap and the cyclical components of GDP and subcomponents:
  - Hodrick-Prescott (HP) filter.
  - ARIMA Model-Based (AMB) approach (Kaiser and Maravall (2000)).
  - Modified HP filter (Kaiser and Maravall (2004)).
  - Band-pass filter constructed by Ouliaris and others (2002).
- Output gap analysis is used to assess whether variations in actual growth are cyclical or more permanent and to gauge inflationary pressures.

*Source: _wp05237 - References..............................................................................................................*

### 1.1 percent.

### _wp05237 - 1.1 percent.

### Key empirical findings on fiscal volatility
- Revenue volatility alone explains up to 0.52 (R-squared coefficient) of the variation in expenditure volatility.
- Oil countries and countries with main export commodities (examples: Brazil, Costa Rica, Nigeria, Venezuela) tend to exhibit the highest volatility in both revenues and expenditures.
- Venezuela recorded an annual average standard deviation of around 4 percent of GDP for public sector revenue and expenditure volatility during 1990–2002.
- Removing outliers (Nigeria, Turkey, Ukraine) yields median values for the sample: expenditure volatility = 1.9 percent of GDP; revenue volatility = 2.9 percent of GDP.
- For the sample, average revenue and expenditure volatility benchmarks cited: 2 percent and 3 percent of GDP, respectively. Several countries remained above these averages in the 1990s; others lowered volatility below them.
- Some countries (Bulgaria, Lebanon, Côte d’Ivoire, Turkey, Ukraine) experienced high revenue volatility but kept expenditure volatility in line with or below the sample average.
- Revenue and expenditure volatility can be strongly procyclical in developing or emerging market economies.

### Causes and dynamics of revenue and expenditure volatility
- Oil price volatility drivers noted: time-varying excess capacity, discrete jumps in technological innovation, uncertain demand, changing geopolitical reasons.
- In absence of hedging strategies, oil price volatility directly affects oil revenues in oil-producing countries (historically a large share of total revenues for countries like Venezuela).
- Other factors increasing fiscal volatility and procyclicality:
  - Greater instability and volatility of private consumption in developing countries, amplified by reliance on indirect taxation due to weak tax administration.
  - Misguided discretionary policies that deepen recessions.
  - Political incentives prompting discretionary spending increases during revenue windfalls.
  - "Voracity effects" from multiple power blocs competing for fiscal shares, leading to spending increases more than proportional to revenue increases.
  - International credit constraints that prevent borrowing during recessions, forcing spending cuts in bad times.
- Reverse causality: procyclical fiscal policy can exacerbate amplitude and persistence of economic fluctuations, increasing macroeconomic instability and risk of prolonged recessions.

### Policy recommendations to reduce oil-related revenue volatility
- Adjust the non-oil balance of the public sector in line with oil price movements to guarantee a sustainable level of non-oil primary deficit.
- Accumulate substantial financial assets during periods of high oil production.
- Formulate accurate oil price assumptions.
- References to operational guidance: Barnett and Ossowski (2003) and Daniel (2001) discussed operational aspects and hedging assessments, respectively (citations preserved as referenced in source).

### Venezuela: fiscal trends 1990–2003 (selected statistics and patterns)
- Venezuela’s average external public-debt-to-GDP ratio was above 40 percent (within a sample range from more than 120 percent in Côte d’Ivoire to about 3 percent in South Africa).
- Primary balances in the sample ranged from an average surplus of 5 percent of GDP in Malaysia to an average deficit of more than 6 percent of GDP in Lebanon.
- Venezuela had an average primary surplus around 1 percent of GDP, in line with the sample average.
- Revenue trends for Venezuela, 1996–2003:
  - Non-oil revenues: clear downward trend, reduction of more than 20 percent in real terms.
  - Oil revenues: slightly decreasing in real terms despite the rise in oil prices.
  - Total revenues: declined for about 15 percent in real terms since 1996.
- Implicit revenues and seigniorage:
  - Venezuelan authorities benefited from seigniorage and the impact of foreign exchange controls as important sources of implicit revenues to finance growing deficits and reduce debt service payments.
  - Average annual revenue from seigniorage during 1995–2003 was 1.5 percent of GDP.
  - Implementation of capital controls since February 2003 caused domestic interest rates to decline, reducing government domestic debt service and increasing money holdings, thereby increasing the seigniorage revenue base.
- Expenditure structure:
  - Mandatory spending accounts for more than 60 percent of total expenditure.
  - No-interest expenditure trend was rather stationary; interest expenditure decreased until 1996 and then rebounded.
- Overall assessment: fiscal stance in Venezuela described as fragile, relying on volatile oil revenues and implicit taxes (seigniorage, financial repression). Need to distinguish cyclical versus discretionary components of fiscal outcomes.

### Methodology for detrending and business-cycle analysis (Venezuela dataset)
- Dataset: quarterly data for 13 time series during 1990–2003 (total GDP, non-oil GDP, oil GDP, oil price by WEO and Venezuelan basket, fiscal variables including total revenues, oil and non-oil revenues, seigniorage, total expenditures, non-oil expenditures, non-oil primary expenditures, non-oil primary balance). Fiscal data available 1995–end-2003; GDP quarterly data available from 1991 to end-2004. All series expressed in real terms using a GDP deflator.
- Four detrending/filtering methods applied to extract different business-cycle information:
  - Hodrick-Prescott (HP) filter:
    - HP parameter λ =1600 for quarterly data.
    - HP cycles may include noncyclical high-frequency variations; HP cyclical component = series − HP trend.
    - HP decomposition shows non-oil sector GDP cycle amplitude more than twice that of oil-sector GDP (non-oil sector more volatile).
  - ARIMA Model-Based Approach (AMB or Beveridge-Nelson):
    - AMB includes more short-term variation; trend is a trend-cycle component.
    - Optimal models estimated (program TRAMO-SEATS):
      - Total GDP: ARIMA (2,2,0) applied to second differences.
      - Oil GDP: IMA (0,1,1) (moving average on first-difference).
      - Non-oil GDP: IMA (0,1,3) (moving average of third order on first-difference).
  - Frequency-domain / spectral analysis and band-pass filter:
    - Squared gain functions: oil GDP area underlying trend-cycle gain smaller than non-oil GDP; oil gain drops to zero around frequency 1.57 (approx. 4 quarters).
    - Non-oil GDP squared gain exhibits a cyclic peak around frequency 1.77 (duration ~3.6 quarters), suggesting short-term mean reversion.
    - Band-pass filter (Corbae, Ouliaris, Phillips 2002) chosen frequency band: between 0.31 and 1.05 (corresponds to time duration between 6 and 20 quarters), narrower than standard business cycle 8 to 32 quarters, to reflect short-term characteristics in Venezuela.
    - Observed stochastic non-oil GDP behavior linked to rapid alternance of weak and strong growth episodes (examples: banking crisis of 1995, growth in 2001–02, political stalemate early 2002, oil-sector work stoppage end-2002).
  - Modified Hodrick-Prescott (MHP) filter (Kaiser and Maravall 2004):
    - Combines AMB and HP: first extract trend-cycle via AMB, then de-trend that trend-cycle with HP.
    - MHP targets business cycle frequencies between two and eight years (8 to 32 quarters) and reduces erraticism observed in standard HP and AMB cyclical components.
- Comparative insight:
  - Standard HP and AMB cycles tend to be more volatile and less persistent than the band-pass estimates.
  - Band-pass and MHP approaches help target specific frequency ranges and reduce spurious high-frequency or low-frequency contributions to the estimated business cycle.

*Source: _wp05237 - 1.1 percent.*

### 2002. Figures 9 and 10 show that the GDP business cycle estimated with respectively the

### V. BUSINESS CYCLE PROPERTIES OF FISCAL VARIABLES IN VENEZUELA

### Data and methods
- Quarterly data for the period 1990–2003.
- 13 economic time series: total GDP; non-oil GDP; oil GDP; oil price (WEO and Venezuelan basket); total revenues; oil revenues; non-oil revenues; total expenditures; non-oil expenditures; non-oil primary balance; seigniorage.
- Filters applied to each series: standard HP filter, modified HP (MHP) filter (a band-pass variant), AMB model-based filter, and a frequency-domain filter. (Results under the frequency-domain filter are omitted in the source because they are very similar to the Modified HP filter.)
- Three sets of moments analyzed:
  - Volatility: quarterly average standard deviation.
  - Persistence: first-order autocorrelation.
  - Cyclicality: contemporaneous correlations with GDP.

### Volatility — key findings
- By normalizing the volatility of the cyclical component of real GDP to its corresponding trend, the cycle is more volatile than the trend by a factor of 1.2.
- The cyclical component of non-oil GDP exhibits volatility two times as high as the oil GDP component, independently of which filter is applied.
- The trend component of oil GDP is more than twice volatile as its cycle.
  - Implication: shocks to the trend appear to be the primary source of fluctuations in oil GDP rather than cyclical fluctuations.
- Fiscal variables:
  - Oil revenue (standard HP cyclical component) exhibits a standard deviation of 6.3 billions of Bolívares (in real terms).
  - Oil revenue is more volatile than its HP trend by a factor of 3.
  - Under the AMB approach, oil revenue is identified as an MA(2) process, implying real oil revenues are influenced only by events up to two quarters.
  - Non-oil primary expenditures are up to twice as volatile as non-oil revenues.
- Filter comparisons:
  - The standard HP filter produces larger volatility statistics than the other filters.
  - On average the standard HP is more volatile by a factor of 2 compared with the modified HP filter and by a factor of 3 with the AMB filter.
  - Explanation: the standard HP (and to a lesser extent AMB) approximate high-pass filters and retain high-frequency volatility removed by the Modified HP band-pass filter.

### Persistence — key findings
- Trends are more persistent than their cyclical components.
- Substantial persistence (around 0.9) implies business cycle predictability.
- Modified HP cyclical-component persistence (benchmark):
  - GDP: 0.92
  - Total expenditure: 0.93
  - Non-oil primary balance: 0.90
  - Oil GDP: 0.88
  - Non-oil GDP: 0.73
  - Non-oil revenues: 0.92
  - Non-oil primary expenditures: 0.88
  - Note: total expenditures are more persistent than total revenues.
  - Comment: interest payments likely increase persistence of revenues/expenditures; interest expenditure of the non-oil sector is negatively correlated with real GDP with a correlation ratio of -0.26 (reported elsewhere in the source).
- Filter differences:
  - Band-pass filters (modified HP) yield the most persistent cyclical components because they include low-frequency components removed by the standard HP cycle and AMB approach.
  - On average, the difference in persistence between the modified HP filter and the standard HP filter is a factor of 1.6 (except seigniorage, where the difference is much larger).
  - The AMB cycle exhibits negative persistence for all 13 variables except non-oil revenues and expenditures (and therefore non-oil primary balance) due to emphasis on high-frequency components.

### Cyclicality of fiscal policy — key findings
- Correlations computed between GDP and trend/cyclical components of fiscal variables to assess procyclicality.
- Interpretation note: a cross-correlation ratio ranges between -1 and +1; around zero = acyclical; close to +1 = strongly procyclical; close to -1 = strongly countercyclical.
- Main results (cyclical components):
  - Non-oil primary expenditures: strong procyclicality with correlation ratios ranging between 0.41 and 0.52.
  - Non-oil expenditures: mild but positive correlation with real GDP.
  - Non-oil primary expenditures are more procyclical than non-oil expenditures because the interest component tends to be counter-cyclical.
  - Total expenditures: weakly procyclical with positive ratios ranging between 0.21 and 0.44.
    - Rationale: oil-related expenditures have a simple correlation with real GDP close to zero and account for about 20 percent of total expenditure in the sample.
  - Non-oil primary balance: negatively correlated with GDP with correlation ratios of 0.37 and 0.39 when using, respectively, the HP and the modified HP cyclical component.
    - Operational interpretation (from source): this implies that, for each percentage point increase in real GDP, there is actually a worsening in the non-oil primary balance of about (text cuts off in source).

### Synthesis of implications
- Use of band-pass filters (MHP and similar frequency-domain filters) substantially reduces apparent business-cycle erraticism relative to standard HP and AMB short-term cycles (MHP and band-pass cycles cross the zero line only seven times vs. about 16 times for the standard HP and 28 times for the AMB short-term cycle in the referenced figures).
- Oil-sector fluctuations in Venezuela appear driven more by trend (permanent or low-frequency) shocks than by high-frequency cyclical movements.
- Fiscal policy in the sample period displays procyclical behavior, particularly via non-oil primary expenditures, suggesting fiscal outcomes tend to be expansionary in “good” times and contractionary in “bad” times.
- Filter choice materially affects measured volatility, persistence, and cyclicality; band-pass (modified HP) filters produce lower volatility and higher persistence estimates than standard HP or AMB filters.

*Source: _wp05237 - 2002. Figures 9 and 10 show that the GDP business cycle estimated with respectively the*

### 0.4 percentage points. On the revenue side, total revenues appear to be independent on GDP

### _wp05237 - 0.4 percentage points. On the revenue side, total revenues appear to be independent on GDP

### Key empirical findings on revenues and expenditures
- Total revenues appear to be independent on GDP business cycle with correlation ratios close to zero or weakly negative.
- Disaggregated revenues:
  - Non-oil revenues: coefficient between 0.40 and 0.50 (stronger procyclicality).
  - Oil revenues: correlation ratio of -0.18 (weakly negatively related to the GDP cycle).
- Non-oil primary expenditures account for about 80 percent of total public expenditures and were procyclical, with correlation ratios with total GDP up to 0.50.
- Interest expenditures were countercyclical (as expected for an emerging market economy).

### Fiscal policy behavior: discretionary versus cyclical components (1995–2003)
- Main fiscal indicator used: non-oil primary balance (preferred over overall primary balance due to oil revenue volatility).
- Two decomposition approaches applied:
  - ARIMA based model estimation (AMB).
  - Structural fiscal balance based on potential output.
- Trend and potential output estimated using the Modified HP filter (chosen for lower erraticism and more balanced volatility).

### Volatility and correlations (1995–2003)
- Volatility (standard deviations reported in Table 4 context):
  - Non-oil primary balance: at almost 7 (more than twice as volatile as non-oil revenues).
  - Non-oil primary expenditures: 6.4 (almost as volatile as oil revenues).
  - Oil revenues: 6.9.
- Effect of extracting discretionary components:
  - Volatility of non-oil primary balances and primary expenditures reduced by more than half when considering discretionary components, indicating a large cyclical contribution to volatility.
- Correlation with oil revenues:
  - Unadjusted non-oil primary balance: correlation with oil revenues close to zero (suggesting an optimal path).
  - Discretionary component of non-oil primary balance: correlation with oil revenues = 0.24 (positive).
  - Discretionary component of non-oil primary expenditure: correlation with oil revenues = -0.27 (negative), indicating discretionary non-oil primary expenditures increased despite declining oil revenues.
- Interpretation: relatively high volatility of non-oil primary expenditures and balances and a mild correlation with oil revenue indicate fiscal policy deviated from an optimal path during 1995–2003.

### Structural fiscal balance, fiscal stance and fiscal impulse
- Structural balance definition used:
  - Gap_str B = B - η × Gap_Y  (represented in source as Gap_str B B Y η = − ×)
  - Where η (non-oil primary balance elasticity to output) = 0.39 (proxy value equal to the correlation ratio between non-oil primary balance and real GDP).
  - Output gap (Gap_Y) defined as output cyclical component in percent of potential output; potential output = trend from Modified HP filter using AMB.
- Main fiscal stance / impulse results (Table 5 context):
  - Discretionary component of the non-oil primary balance shows progressive worsening: by end-2003 the non-oil primary deficit is almost four times as large as at the end of 1995.
  - 1996–2001: fiscal impulse positive, reflecting strong expenditure increase with annual average growth of about 5 percent change in real terms; non-oil revenues mainly stationary.
  - 2002–2003:
    - Primary public expenditure growth becomes negative, falling by about 1–2 percentage points each year.
    - Non-oil revenues fall by about 4 percentage points each year.
    - Fiscal impulse remains positive at about 3 and 2 percentage points in real terms, respectively, in 2002 and 2003 (largely due to the sharp fall in revenues rather than increased spending).
  - Real GDP of Venezuela fell by 9 percent each year in 2002 and 2003.
    - Output gap: 9 percent below potential in 2002; 10 percent below potential in 2003.
  - 1996–2001: annual average output gap = a positive 2 percent above trend.
- Volatility comparison 1996–2001:
  - Discretionary component of fiscal stance is as volatile as the cyclical component.
  - Volatility of non-oil primary expenditures is four times as volatile as non-oil revenues.

### Medium-run and cumulative changes in fiscal aggregates
- 1995–1998 (cumulative):
  - Discretionary component of non-oil primary expenditure grew by 29 percent in real terms.
  - Growth rate of non-oil revenues on a cumulative basis: 8 percent in real terms.
- 2001–2003 (recessionary period, output gap up to 10 percent below potential):
  - Non-oil revenue fell by about 15 percent in real terms (cumulative), or three times more than non-oil primary expenditures on a cumulative basis.

### Business cycle characteristics and cross-country context
- Business cycle characteristics (1991–2003):
  - Strong dominance of short-term cyclical components with average duration of about 2–3 years for both oil and non-oil economies.
  - Volatility (amplitude) of non-oil sector GDP business cycle is more than two times that of oil sector GDP cycle.
  - For oil GDP, trend is more volatile than cycle, suggesting primary source of oil GDP fluctuations originates from shocks to the trend.
- Cross-section evidence:
  - Positive correlation between public revenue and expenditure volatility among developing countries.
  - Oil-exporting and commodity-exporting countries (e.g., Brazil, Costa Rica, Nigeria, Venezuela) tend to exhibit highest volatility in both revenues and expenditures.
  - Venezuela: high public sector revenue and expenditure volatility, with an annual average standard deviation of around 4 percent of GDP during the period 1990–2002.
- Revenue composition:
  - Non-oil revenues exhibit far less erratic behavior than oil revenues (oil revenues affected by short-term oil price volatility).
  - Decomposition shows discretionary-component-induced volatility is as large as cycle-induced volatility; non-oil primary expenditures much more volatile than non-oil revenues.

### Detrending and decomposition methods used (appendix summary)
- Hodrick and Prescott (HP) Filter:
  - HP filter minimizes a criterion that penalizes deviations from trend and trend acceleration; business cycle measured as deviation from trend.
- ARIMA–based filter (AMB) following Kaiser and Maravall (2000):
  - Identify ARIMA model for observed quarterly series x_t; decompose into trend (p_t), seasonal (s_t) and irregular (u_t) components.
  - AMB produces MMSE (optimal) estimators for trend, seasonal and irregular components.
- Frequency-Domain Filter:
  - Uses spectral representation to decompose series into periodic components across frequencies.
  - Spectrum interpreted as variance of cyclical components by frequency; with quarterly data, frequency corresponding to T = 4 quarters equals π/2 approximately 1.57.

*Source: IMF working paper content provided in the input PDF chapter/section.*

### REFERENCES

### _wp05237 - REFERENCES

### Major bibliographic sources cited
- Aguiar, Mark, and Gita Gopinath, 2004, “Emerging Market Business Cycle: The Cycle is the Trend,” NBER Working Paper 10734.
- Alesina, Alberto, Ricardo Hausmann, Rudolf Hommes, and Ernesto Stein, 1996, “Budget Institutions and Fiscal Performance in Latin America,” NBER Working Paper 5586.
- Baxter, Marianne, and Robert King, 1999, “Measuring Business Cycles: Approximate Band-Pass Filters for Economic Time Series,” The Review of Economics and Statistics, Vol. 81 (November), pp. 575–93.
- Barnett, Steven, and Rolando Ossowski, 2003, “Operational Aspects of Fiscal Policy in Oil-Producing Countries,” in Fiscal Policy Formulation and Implementation in Oil-Producing Countries, ed. by Jeffrey M. Davis, Rolando Ossowski, and Annalisa Fedelino.
- Barnett, Steven, and Alvaro Vivanco, 2003, “Statistical Properties of Oil Prices: Implications for Calculating Government Wealth,” in Fiscal Policy Formulation and Implementation in Oil-Producing Countries, ed. by Jeffrey M. Davis, Rolando Ossowski, and Annalisa Fedelino.
- Blanchard, Olivier, and Roberto Perotti, 2002, "An Empirical Characterization of the Dynamic Effects of Changes in Government Spending and Taxes on Output”, The Quarterly Journal of Economics, Vol. 117, No.4, pp. 1329–68.
- Blejer, Mario, and Adrienne Cheasty, 1993, How to Measure the Fiscal Deficit: Analytical and Methodological Issues (Washington: International Monetary Fund).
- Canova, Fabio, 1998, “Detrending and Business Cycle Facts,” Journal of Monetary Economics, Vol. 41, pp. 475–512.
- Corbae, Dean, Sam Ouliaris, and Peter Phillips, 2002, “Band Spectral Regression with Trending Data,” Econometrica, Vol. 70, pp. 1067–109.
- Daniel, James, 2001, “Hedging Government Oil Price Risk”, IMF Working Paper No. 01/185.
- Devlin, Julia, and Michael Lewin, 2004, “Managing Oil Booms and Busts in Developing Countries,” Managing Volatility and Crises: A Practitioner Guide (unpublished; Washington: World Bank).
- Fischer, Stanley, 1982, “Seigniorage and the Case for a National Money,” Journal of Political Economy, (April) 90, pp. 295–313.
- Franken, Helmut, Guillermo Le Fort, and Eric Parrado, 2004, “Business Cycles responses and the Resilience of the Chilean Economy During the Last Fifty years,” prepared for the Eight Annual Conference of the Central Bank of Chile, Santiago, August 10–11, 2004.
- Gavin, Michael, 1997, “A Decade of Reform in Latin America: Has It Delivered Lower Volatility?” OCE Working Paper 349.
- Gavin, Michael, Ricardo Hausmann, Roberto Perotti, and Ernesto Talvi, 1996, “Managing Fiscal Policy in Latin America,” OCE Working Paper.
- Gavin, Michael, and Roberto Perotti, 1997, “Fiscal Policy in Latin America,” in NBER Macroeconomics Annual, pp. 11–61.
- Giovannini, Alberto, and Martha de Mello, 1993, “Government Revenue from Financial Repression,” The American Economic Review, Vol. 83, No. 4, pp. 953–63.
- Hamilton, James, 1994, Time Series Analysis (Princeton University Press).
- Hausmann, Ricardo, and Roberto Rigobon, 2002, “An Alternative Interpretation of the ‘Resource Curse’: Theory and Policy Implications,” NBER Working Paper 9424.
- Hodrick, Robert J., and Prescott, Edward C., 1997, "Postwar U.S. Business Cycles: An Empirical Investigation," Journal of Money, Credit and Banking, Vol. 29, No. 1, pp. 1–16.
- International Monetary Fund, World Economic Outlook, September, 2003 (Washington).
- Kaiser, Regina, and Agustín Maravall, 2000, “Notes on Time Series Analysis, ARIMA Models and Signal Extraction,” Working Paper No. 0012 (Bank of Spain).
- Kaiser, Regina, and Agustín Maravall, 2004, “Combining Filter Design With Model-Based Filtering (With an Application to Business-Cycle Estimation),” Working Paper No. 0417 (Bank of Spain).
- Kaminsky, Graciela, Carmen Reinhart, and Carlos Vegh, 2004, “When it Rains, It Pours: Procyclical Capital Flows and Macroeconomic Policies,” NBER working paper 10780.
- Kose, Ayhan, Eswar Prasad, and Marco Terrones, 2005, “How Do Trade and Financial Integration Affect the Relationship between Growth and Volatility?” IMF Working Paper No. 05/19, January.
- Kydland, Finn, and Carlos Zarazaga, 1997, “Is the Business Cycle of Argentina ‘Different’?” Federal Reserve Bank of Dallas, Economic Review, pp. 21–36.
- Lane, Phillip, 2003, “Business Cycles and Macroeconomic Policy in Emerging Market Economies,” (unpublished; Trinity College).
- Lane, Phillip, and Aaron Tornell, 1999, “The Voracity Effect,” American Economic Review, No. 89, pp. 22–46.
- Marcet, Albert, and Morten O. Ravn, 2001, “The HP-Filter in Cross-Country Comparisons,” University of Pompeu Fabra Working Paper 588.
- Nelson, Charles R., and Charles I. Plosser, 1982, “Trends and Random Walks in Macroeconomic Time Series”, Journal of Monetary Economics, Vol. 10, pp. 139–62.
- Ramey, Garey, and Valerie A. Ramey, 1995, “Cross-Country Evidence on the Link between Volatility and Growth,” American Economic Review, Vol. 85, No. 5, pp. 1138–51.
- Ravn, Morten O. and Harald Uhlig, 2001, “On Adjusting the HP-Filter for the Frequency of Observations,” CEPR Discussion Papers 2858.
- Restrepo, Jorge Enrique, and José Daniel Reyes, 2002, “Los Ciclos Económicos en Colombia, Evidencia Empírica (1977–1998).”
- Rigobon, Roberto, 1999, “Caracteristicas del Proceso Stocastico del Petroleo,” Mimeo.
- Sáez, Francisco, 2004, “Patrones Cíclicos de la Economía Venezolana,” Working Paper No. 60 (Banco Central de Venezuela).
- Spilimbergo, Antonio, 2005, “Measuring the Performance of Fiscal Policy in Russia,” Selected Issues on Russian Federation, Country Report No. 05/378.
- Stock, James H., and Mark W. Watson, 1988, “Business Cycle Fluctuations in U.S. Macroeconomic Time Series,” NBER Working Paper 6528.
- Talvi, Ernesto, and Carlos A. Vegh, 2000, “Tax Base Variability and Procyclical Fiscal Policy,” NBER Working Paper 7499.
- Torres, Alberto, 1999, “Empirical Evidence from the Mexican Business Cycle,” (unpublished; New York University).
- Watson, Mark, 2001, “Time Series: Cycles,” International Encyclopedia of the Social and Behavioral Sciences.

### Figures and key annotations (captions and quantitative highlights)
- Figure 1. Venezuela Oil Price and Oil Sector GDP Fluctuations, 1991–2002
  - Correlation ratio = 0.80
  - Series shown: Oil Price (Venezuelan basket, in current US$) and Venezuela GDP (Oil sector, in Bs billion)
  - Source: Banco Central de Venezuela (BCV), Venezuelan Ministry of Finance (MOF), and IMF’ staff calculations.
- Figure 2. Output Growth and Output Volatility in Selected Emerging Market Countries, 1960–2000 (In percent of GDP)
  - Regression: y = 3.81 - 0.39x
  - R2 = 0.165
  - Volatility measured by standard deviation.
  - Source: Kose, Prasad, and Terrones (2004), and author’s estimation.
- Figure 3. Volatility of Public Revenue and Expenditure in Selected Emerging Market Countries, 1990–2002 (In percent of GDP)
  - Regression: y = 0.61 + 0.47x
  - R2 = 0.52
  - Volatility measured by standard deviation.
  - Source: IMF(2003), and author’s estimation.
- Figure 4. External Public Debt in Selected Emerging Market Countries, 1990–2002 (In percent of GDP)
  - External Public Debt to GDP Ratios plotted for named countries.
  - Source: IMF (2003).
- Figure 5. Primary Balance in Selected Emerging Market Countries, 1990–2002 (In percent of GDP)
  - Primary Balance values plotted across countries; axis range shown from -8.0 to 6.0.
  - Source: IMF (2003).
- Figures 6–10. Venezuela: Business Cycles (1991–2002) using multiple filtering procedures
  - Figure 6: HP Filtering Procedure, GDP, Non-Oil GDP, and Oil GDP (In billions of Bolívares, at 1984 prices).
  - Figure 7: ARIMA-Model Filtering Procedure, GDP, Non-Oil GDP, and Oil GDP (In billions of Bolívares, at 1984 prices).
  - Figure 8a: Venezuela: Real Non-Oil GDP—Squared Gain of Trend-Cycle Filter.
  - Figure 8b: Venezuela: Real Oil GDP—Squared Gain of Trend-Cycle Filter.
  - Figure 9: Band-Pass Filtering Procedure, GDP, Non-Oil GDP, and Oil GDP (In billions of Bolívares, at 1984 prices).
  - Figure 10: Modified Hodrick-Prescott Filtering Procedure, GDP, Non-Oil GDP, and Oil GDP (In billions of Bolívares, at 1984 prices).
  - Sources for the figures: BCV, MOF, IMF (2003) or IMF (2004), and author’s estimations as indicated per figure.

### Tables and principal quantitative results (titles and selected exact figures)
- Table 1. Effects of Filtering on Moments: Volatility, 1991–2002 (In billions of Bolívares, at 1984 prices)
  - Panel A. Standard deviations (proxy for volatility)
    - real GDP (Bn BS), 1991:1–2002:4: Unadjusted series = 7.7; Hodrick-Prescott Trend (HP-X11 SA) = 4.5; Modified Hodrick-Prescott Trend = 7.2; ARIMA Trend-cycle (TSW) = 6.9.
    - HP-X11SA Cyclical component = 5.3; Modified HP Cyclical component = 4.7; ARIMA(TSW) short-term cyclical component = 4.2.
    - real oil GDP, 1991:1–2002:4: Unadjusted series = 4.7; HP trend = 4.0; Modified HP trend = 4.2; ARIMA trend = 4.5.
    - HP cyclical = 2.3; Modified HP cyclical = 1.2; ARIMA short-term cyclical = 1.8.
    - real non-oil GDP, 1991:1–2002:4: Unadjusted series = 5.1; HP trend = 0.7; Modified HP trend = 1.3; ARIMA trend = 3.8.
    - HP cyclical = 3.7; Modified HP cyclical = 3.3; ARIMA short-term cyclical = 3.6.
    - Seigniorage (in percent of money base), 1996:4–003:3: Unadjusted = 3.6%; HP trend = 1.5%; Modified HP trend = 0.4%; ARIMA trend = 1.1%; HP cyclical = 1.2%; Modified HP cyclical = 0.8%; ARIMA short-term cyclical = 0.2%.
    - Oil Price (WEO quarterly data (1984:1–2003:3)): Unadjusted = 5.2; HP trend = 3.7; Modified HP trend = 3.8; ARIMA trend = 5.2; HP cyclical = 3.4; Modified HP cyclical = 3.4; ARIMA short-term cyclical = 0.6.
    - Oil Price (VEN quarterly data (1994:1–2003:4)): Unadjusted = 5.2; HP trend = 3.8; Modified HP trend = 3.7; ARIMA trend = 5.7; HP cyclical = 3.5; Modified HP cyclical = 3.5; ARIMA short-term cyclical = 0.7.
    - Real fiscal variables (nonfinancial public sector), 1995:1–2003:4: Expenditure Unadjusted = 7.7; HP trend = 0.4; Modified HP trend = 0.8; ARIMA trend = 0.7; HP cyclical = 7.1; Modified HP cyclical = 0.1; ARIMA short-term cyclical = 1.9.
    - Revenues Unadjusted = 6.7; HP trend = 2.6; Modified HP trend = 2.4; ARIMA trend = 5.3; HP cyclical = 5.7; Modified HP cyclical = 4.8; ARIMA short-term cyclical = 1.8.
    - Non-oil revenues Unadjusted = 2.6; HP trend = 2.6; Modified HP trend = 1.1; ARIMA trend = 1.6; HP cyclical = 1.9; Modified HP cyclical = 0.8; ARIMA short-term cyclical = 1.5.
    - Oil revenues Unadjusted = 6.9; HP trend = 1.8; (Noted: estimated as an MA(2) process; no decomposition for Modified HP and TSW).
    - Non-oil expenditures Unadjusted = 6.3; HP trend = 2.2; Modified HP trend = 1.2; ARIMA trend = 1.8; HP cyclical = 3.6; Modified HP cyclical = 1.5; ARIMA short-term cyclical = 3.3.
    - Non-oil primary expenditures Unadjusted = 6.5; HP trend = 2.6; Modified HP trend = 2.5; ARIMA trend = 3.9; HP cyclical = 3.8; Modified HP cyclical = 2.7; ARIMA short-term cyclical = 4.8.
    - Non-oil primary balance Unadjusted = 7.1; HP trend = 3.7; Modified HP trend = 3.2; ARIMA trend = 3.9; HP cyclical = 3.9; Modified HP cyclical = 2.1; ARIMA short-term cyclical = 2.7.
  - Notes: 1/ Computed as the difference between the series and its respective trend. 2/ 2003:4 is staff projection. 3/ Oil revenue time series estimated as MA(2); no decomposition performed for Modified HP and TSW filters. 4/ Frequency-domain filter estimates omitted as similar to Modified HP.
- Table 2. Effects of Filtering on Moments: Persistence, 1991–2002 (In billions of Bolívares, at 1984 prices)
  - Panel B. First-Order Correlation (proxy for persistence)
    - real GDP (1991:1–2002:4): Unadjusted series = 0.47; Hodrick-Prescott Trend = 0.94; Modified HP = 0.87; ARIMA Trend-cycle = 0.80; HP-X11SA Cyclical component = 0.49; Modified HP Cyclical component = 0.92; ARIMA(TSW) short-term cyclical component = -0.24.
    - real oil GDP: Unadjusted = 0.84; HP trend = 0.95; Modified HP = 0.95; ARIMA = 0.97; HP cyclical = 0.19; Modified HP cyclical = 0.88; ARIMA short-term cyclical = -0.06.
    - real non-oil GDP: Unadjusted = 0.23; HP trend = 0.84; Modified HP = 0.89; ARIMA = 0.71; HP cyclical = 0.71; Modified HP cyclical = 0.73; ARIMA short-term cyclical = -0.38.
    - Seigniorage (1996:4–2003:3): Unadjusted = -0.30%; HP trend = 0.87%; Modified HP = 0.86%; ARIMA = 0.76%; HP cyclical = 0.03%; Modified HP cyclical = 0.70%; ARIMA short-term cyclical = -0.64%.
    - Oil Price (WEO quarterly data (1984:1–2003:4)): Unadjusted = 0.80; HP trend = 0.93; Modified HP = 0.95; ARIMA = 0.89; HP cyclical = 0.76; Modified HP cyclical = 0.83; ARIMA short-term cyclical = -0.13.
    - Oil Price (VEN quarterly data (1994:1–2003:4)): Unadjusted = 0.87; HP trend = 0.94; Modified HP = 0.94; ARIMA = 0.89; HP cyclical = 0.81; Modified HP cyclical = 0.83; ARIMA short-term cyclical = -0.14.
    - Real fiscal variables (1995:1–2003:4): Expenditure Unadjusted = -0.08; HP trend = 0.85; Modified HP = 0.92; ARIMA = 0.91; HP cyclical = 0.55; Modified HP cyclical = 0.93; ARIMA short-term cyclical = -0.44.
    - Revenues Unadjusted = 0.66; HP = 0.91; Modified HP = 0.92; ARIMA = 0.85; HP cyclical = 0.68; Modified HP cyclical = 0.80; ARIMA short-term cyclical = -0.23.
    - Non-oil revenues Unadjusted = 0.69; HP = 0.90; Modified HP = 0.91; ARIMA = 0.92; HP cyclical = 0.46; Modified HP cyclical = 0.92; ARIMA short-term cyclical = 0.20.
    - Oil revenues Unadjusted = 0.73; HP = 0.91; (MA(2) noted for Modified HP and TSW).
    - Non-oil expenditures Unadjusted = 0.09; HP = 0.92; Modified HP = 0.89; ARIMA = 0.95; HP cyclical = 0.46; Modified HP cyclical = 0.66; ARIMA short-term cyclical = 0.36.
    - Non-oil primary expenditures Unadjusted = 0.30; HP = 0.92; Modified HP = 0.92; ARIMA = 0.92; HP cyclical = 0.58; Modified HP cyclical = 0.88; ARIMA short-term cyclical = -0.21.
    - Non-oil primary balance Unadjusted = 0.29; HP = 0.92; Modified HP = 0.93; ARIMA = 0.95; HP cyclical = 0.48; Modified HP cyclical = 0.90; ARIMA short-term cyclical = 0.10.
  - Notes: 1/ Computed as the difference between the series and its respective trend. 2/ 2003:4 is staff projection. 3/ Oil revenue MA(2) note. 4/ Frequency-domain filter omitted as similar to Modified HP.
- Table 3. Effects of Filtering on Moments: Cyclicality, 1991–2002 (In billions of Bolívares, at 1984 prices)
  - Panel C. Contemporaneous Correlations with GDP (proxy for cyclicality)
    - real GDP: correlations = 1.00 across trend and cycle decompositions.
    - real oil GDP: Unadjusted = 0.77; HP trend = 0.99; Modified HP trend = 0.58; ARIMA trend = 0.63; HP cyclical = 0.85; Modified HP cyclical = 0.33; ARIMA short-term cyclical = 0.44.
    - real non-oil GDP: Unadjusted = 0.81; HP trend = 0.71; Modified HP trend = 0.23; ARIMA trend = 0.57; HP cyclical = 0.93; Modified HP cyclical = 0.56; ARIMA short-term cyclical = 0.55.
    - Seigniorage (1996:4–2003:3): Unadjusted = 0.27%; HP trend = -0.15%; Modified HP trend = -0.05%; ARIMA trend = -0.32%; HP cyclical = 0.26%; Modified HP cyclical = -0.41%; ARIMA short-term cyclical = -0.05%.
    - Oil Price (WEO quarterly): Unadjusted = 0.13; HP trend = 0.26; Modified HP trend = -0.10; ARIMA trend = 0.13; HP cyclical = -0.05; Modified HP cyclical = -0.11; ARIMA short-term cyclical = -0.30.
    - Oil Price (VEN quarterly): Unadjusted = -0.17; HP trend = -0.10; Modified HP trend = -0.10; ARIMA trend = -0.18; HP cyclical = -0.22; Modified HP cyclical = -0.16; ARIMA short-term cyclical = -0.30.
    - Real fiscal variables (1995:1–2003:4): Expenditure Unadjusted = 0.61; HP trend = 0.13; Modified HP trend = 0.03; ARIMA trend = 0.05; HP cyclical = 0.44; Modified HP cyclical = 0.21; ARIMA short-term cyclical = 0.25.
    - Revenues Unadjusted = 0.09; HP = 0.11; Modified HP = 0.11; ARIMA = 0.0; HP cyclical = 0.09; Modified HP cyclical = 0.03; ARIMA short-term cyclical = -0.18.
    - Non-oil revenues Unadjusted = 0.45; HP = 0.33; Modified HP = 0.33; ARIMA = 0.49; HP cyclical = 0.45; Modified HP cyclical = 0.48; ARIMA short-term cyclical = 0.38.
    - Oil revenues Unadjusted = -0.07; HP = -0.06; (MA(2) note for Modified HP and TSW).
    - Non-oil expenditures Unadjusted = 0.41; HP trend = -0.55; Modified HP trend = -0.31; ARIMA trend = -0.04; HP cyclical = 0.33; Modified HP cyclical = 0.47; ARIMA short-term cyclical = 0.11.
    - Non-oil primary expenditures Unadjusted = 0.49; HP trend = -0.1; Modified HP trend = -0.1; ARIMA trend = 0.32; HP cyclical = 0.46; Modified HP cyclical = 0.52; ARIMA short-term cyclical = 0.41.
    - Non-oil primary balance (minus implies procyclicality): Unadjusted = -0.43; HP trend = 0.33; Modified HP trend = 0.31; ARIMA trend = -0.18; HP cyclical = -0.37; Modified HP cyclical = -0.39; ARIMA short-term cyclical = -0.14.
  - Notes: Sources and methodological notes as in Table 1.
- Table 4. Performance of Fiscal Policy in Venezuela, 1995–2003 (quarterly data, Billions of Bolívares at 1984 prices)
  - Volatility (Measured by the standard deviation) and Correlation with Oil Revenues (with unadjusted oil revenue series)
    - Oil-revenues: Volatility = 6.89; Correlation with Oil Revenues (Unadjusted) = 1.00.
    - Non-oil revenues: Volatility = 2.65; Correlation with Oil Revenues (Unadjusted) = -0.24.
    - Non-oil primary expenditures: Volatility Unadjusted = 6.43; Discretionary = 2.54; Correlation Unadjusted = -0.04; Correlation Discretionary = -0.27.
    - Non-oil primary balance: Volatility Unadjusted = 6.91; Discretionary = 3.04; Correlation Unadjusted = -0.06; Correlation Discretionary = 0.24.
  - Note: Discretionary component computed as the extracted Modified HP trend after applying the ARIMA based model (AMB) estimation (Kaiser and Maravall, 2004).
- Table 5. Venezuela Fiscal Stance, 1995–2003 (In billions of Bolívares, at 1984 prices)
  - Non-oil primary balance (Unadjusted series, 1995–2003 examples)
    - 1995 = -12.6; 1996 = -9.6; 1997 = -35.3; 1998 = -19.7; 1999 = -30.2; 2000 = -40.4; 2001 = -65.4; 2002 = -45.6; 2003 = -41.2.
    - 1996–2001 average = -30.5; st. dev. = 17.8; 2002–03 average = -43.4; st. dev. = 2.2.
  - Adjusted series (ARIMA estimation) examples:
    - 1995 = -15.4; 1996 = -11.1; 1997 = -31.4; 1998 = -27.5; 1999 = -15.3; 2000 = -40.1; 2001 = -60.2; 2002 = -46.1; 2003 = -40.7.
    - 1996–2001 average = -28.7; st. dev. = 16.0; 2002–03 average = -43.4; st. dev. = 2.7.
  - Discretionary component (examples):
    - 1995 = -13.3; 1996 = -17.8; 1997 = -22.5; 1998 = -27.1; 1999 = -32.2; 2000 = -38.0; 2001 = -43.0; 2002 = -46.1; 2003 = -48.0.
    - 1996–2001 average = -27.7; st. dev. = 10.0; 2002–03 average = -47.0; st. dev. = 0.9.
  - Cyclical component (examples):
    - 1995 = -2.1; 1996 = 6.7; 1997 = -8.9; 1998 = -0.4; 1999 = 16.9; 2000 = -2.2; 2001 = -17.2; 2002 = 0.0; 2003 = 7.2.
    - 1996–2001 average = -1.0; st. dev. = 10.0; 2002–03 average = 3.6; st. dev. = 3.6.
  - Non-oil revenue (Unadjusted series examples):
    - 1995 = 72.7; 1996 = 72.4; 1997 = 81.8; 1998 = 93.3; 1999 = 80.9; 2000 = 74.2; 2001 = 73.4; 2002 = 70.3; 2003 = 61.7.
    - 1996–2001 average = 78.4; st. dev. = 7.1; 2002–03 average = 66.0; st. dev. = 4.3.
  - Non-oil primary expenditure (Unadjusted series examples):
    - 1995 = 85.4; 1996 = 81.9; 1997 = 117.1; 1998 = 113.0; 1999 = 111.2; 2000 = 114.6; 2001 = 138.8; 2002 = 116.0; 2003 = 102.8.
    - 1996–2001 average = 108.9; st. dev. = 18.1; 2002–03 average = 109.4; st. dev. = 6.6.
  - Fiscal impulse (examples):
    - Series: 1995 = 4.5; 1996 = 4.7; 1997 = 4.7; 1998 = 5.1; 1999 = 5.8; 2000 = 5.0; 2001 = 3.1; 2002 = 1.8; 2003 = 5.0.
    - 1996–2001 average = 0.4; st. dev. = 2.5; 2002–03 average = 0.6.
    - of which: non-oil revenues (discretionary component) series examples: -2.8, -2.3, -1.0, 0.8, 2.3, 3.2, 3.8, 4.0, 0.0; averages and st. dev. reported as 2.3, 3.9, 0.1.
    - of which: non-oil primary expenditures (discretionary component) series examples: 7.3, 7.0, 5.7, 4.3, 3.5, 1.8, -0.7, -2.2, 4.9; averages and st. dev. reported as 2.0, -1.4, 0.8.
  - Output gaps (in percent of trend) examples:
    - 1995 = -1.8; 1996 = 7.2; 1997 = -6.6; 1998 = -1.7; 1999 = 8.0; 2000 = 6.9; 2001 = -8.5; 2002 = -9.7; 2003 = 2.0.
    - 1996–2001 average = 5.6; st. dev. = -9.1; 2002–03 average = 0.6. (Values reported as in table.)
  - Structural balance (b_str = b - e*output gap) examples:
    - 1995 = -8.9; 1996 = -38.1; 1997 = -17.1; 1998 = -29.6; 1999 = -43.5; 2000 = -68.1; 2001 = -42.3; 2002 = -37.4; 2003 = -34.2.
    - 1996–2001 average = 19.2; st. dev. = -39.8; 2002–03 average = 2.5. (Values as reported in table.)
  - Non-oil primary expenditures, real growth (discretionary component) examples:
    - 1995 = 8.43; 1996 = 7.46; 1997 = 5.60; 1998 = 4.00; 1999 = 3.11; 2000 = 1.55; 2001 = -0.56; 2002 = -1.87; 2003 = 5.0.
    - 1996–2001 average = 2.4; st. dev. = -1.2; 2002–03 average = 0.7.
  - Non-oil revenues, real growth (discretionary component) examples:
    - 1995 = 3.86; 1996 = 3.03; 1997 = 1.27; 1998 = -0.99; 1999 = -2.91; 2000 = -4.23; 2001 = -5.15; 2002 = -5.76; 2003 = 0.0.
    - 1996–2001 average = 3.0; st. dev. = -5.5; 2002–03 average = 0.3.
  - Notes: 1/ In oil-producing economies, non-oil primary balance is a better indicator to analyze the fiscal stance and sustainability. 2/ Adjusted by ARIMA based model (AMB) estimation (Kaiser and Maravall, 2004). 3/ Discretionary measure removes cyclic or 'automatic' components. 4/ First-difference of discretionary component of non-oil primary balance. 5/ Discretionary change in non-oil primary balance; positive implies revenue reduction or spending increase. 6/ Output gap defined as cycle-to-trend output ratio; Real GDP trend estimated using ARIMA univariate detrending methods and Kaiser-Maravall methodology. 7/ Structural balance defined as non-oil primary balance adjusted by the cyclical component; estimated elasticity of the fiscal balance set at 0.39.

*Source: _wp05237 - REFERENCES (PDF), BCV, MOF, IMF (2003/2004) and author’s calculations as cited in the document.*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2005/_wp05237.pdf_