Norway
Selected Issues

This Selected Issues paper analyzes core and idiosyncratic inflation in Norway. The paper provides estimates of underlying inflation, using a statistical technique to decompose inflation and a measure of core inflation into “common” and “idiosyncratic” components. It finds that overall inflation is not far from its underlying value, as estimated by the common component, while core inflation is below its underlying value. The paper also considers medium-term and long-term fiscal policy in light of high oil prices and the prospect of substantial increase in pension outlays.

Abstract

This Selected Issues paper analyzes core and idiosyncratic inflation in Norway. The paper provides estimates of underlying inflation, using a statistical technique to decompose inflation and a measure of core inflation into “common” and “idiosyncratic” components. It finds that overall inflation is not far from its underlying value, as estimated by the common component, while core inflation is below its underlying value. The paper also considers medium-term and long-term fiscal policy in light of high oil prices and the prospect of substantial increase in pension outlays.

I. Core and Idiosyncratic Inflation in Norway1

A. Introduction

1. In the past four years, inflation in Norway has been below the average in other countries (Figure 1), although a surge in energy prices has recently pushed it up. However, indicators of underlying (core) inflation continue to show a benign outlook (Figure 2), as both domestic and import prices have risen only modestly. Indeed, core inflation remains below the inflation target of 2½ percent established in 2001.

Figure 1.
Figure 1.

Inflation Performance, 2000-06

(Year-on-year percent change, harmonized index, seasonally adjusted)

Citation: IMF Staff Country Reports 2007, 197; 10.5089/9781451829815.002.A001

Figure 2.
Figure 2.

Inflation Performance, 2000-06

(Year-on-year percent change, seasonally adjusted)

Citation: IMF Staff Country Reports 2007, 197; 10.5089/9781451829815.002.A001

2. Although immigrant workers will likely continue to ease labor-market tightness, and, hence, cost pressures, there are signs that the risks of overheating have intensified. In fact, the November 2006 Inflation Report notes that “high capacity utilization, rising wage growth and somewhat slower productivity growth are expected to lead to higher inflation, particularly from the second half of 2007 and into 2008.”

3. The challenge ahead is therefore one of cautiously managing an increase in inflation toward its target while avoiding overshooting and a consequent rise in the policy interest rate. To this end, Norges Bank (NB) would need to gauge underlying inflation pressures.

4. This chapter focuses on underlying inflation. It looks at inflation and tries to distinguish between shocks that drive the underlying inflation process and are common (correlated) across countries or sectors—although their impact depends on their individual “load” and differences in economic structures and policies—and shocks that impact a single country, which, by definition, are uncorrelated with common factors. Among these idiosyncratic determinants of inflation one could think of specific features of the labor market, degree of competition in product markets, and, of course, specific policy actions such as direct and indirect taxation.

5. The chapter is organized as follows. Section B briefly presents the data and the generalized dynamic factor model methodology. Section C reviews the results. Section D concludes.

B. Methodology and Data

6. The analysis in this chapter is based on an application of the generalized dynamic factor model (GDFM) proposed by Forni and others (2000 and 2001). This is a statistical approach that extends principal component analysis and Stock and Watson’s (1989) coincident and leading indicator approach. Factor analysis assumes that covariation among time series can be explained by a few unobserved shocks (factors). In factors models, therefore, a large number of covarying series are transformed into a smaller number of unobserved orthogonal series (common components) so as each additional factor (component) explains as much as possible of the remaining variation in the observed series. The basic framework is that of a dynamic factor model in which the assumption of mutually orthogonal idiosyncratic components is relaxed to allow for some mild cross correlation. Each observed series is then represented as the sum of a common component and of a disturbance term (idiosyncratic component), which is uncorrelated with the common component. For each country and each sector, underlying inflation is proxied by the common component, which, although driven by the same factors, can differ across countries and sectors depending on their structure—that is, the impact on inflation depends on the “load” for each factor.

7. The dataset comprises a panel of 19 countries, and 214 monthly series of CPI indices and their components over the period 1999–06.2 Factor models can accommodate large panels and overcome the problem inherent in multivariate analysis when the time dimension is smaller than the cross-country dimension. The data set contains seasonally adjusted monthly inflation from January 1999 through October 2006, both for headline CPI inflation and for its components, with over 17000 data points.3 The sources are the Harmonized Index of Consumer Prices (HICP) and national statistics.

8. Each of these 214 series, spanning both countries and sectors, is decomposed into a part that is explained by a set of common factors and a residual part that reflects ideosyncratic influences. The first step in the analysis is to determine the number of common factors. A principal component analysis of the spectral density matrices of the data (Figure 3) shows the share of the cumulative variance (cumulative eigenvalues) of the series that is explained by each successive principal components (eigenvector). Different thresholds can be set to identify the number of common factors (components). Here, this is chosen by stopping at the factor (eigenvalue) that improves upon the explained cumulative data variability by less than 10 percent at all frequencies. This yields three dynamic common components, which explain about 80 percent of the total data variability. From an economic point of view, a possible rationalization of this choice would be to look at the inflation process as generated by three underlying forces: demand, supply, and structural variables (although these forces cannot be identified with specific components).

Figure 3.
Figure 3.

Cumulative Data Variability Explained by the First Ten Common Factors

(Percent)

Citation: IMF Staff Country Reports 2007, 197; 10.5089/9781451829815.002.A001

Source: IMF staff estimates.

9. The next step is to determine the number of static factors. The relation among static and common factors, and lags is given by: number of static factors=number of common factors * (1+number of lags).

With 3 common factors and 12 as the number of lags (in light of the monthly frequency), the number of static factors is set at 39.

C. Developments in Underlying Inflation

10. Figure 4 plots headline CPI explained by the three common factors and the static factors (henceforth, underlying inflation) and actual inflation for Norway. It suggests an increased importance of idiosyncratic factors in explaining the pickup in inflation in 2006, in contrast to what occurred the previous year. A closer look at the components of the CPI index indicates that food, housing, utilities and other fuels, and hotels and restaurants were the sectors that contributed the most to the rise in inflation (Figure 5).

Figure 4.
Figure 4.

Norway: Common and Actual Headline Inflation, 2000-06

(Year-on-year percent change, seasonally adjusted)

Citation: IMF Staff Country Reports 2007, 197; 10.5089/9781451829815.002.A001

Sources: Eurostat; National authorities; and IMF staff estimates.
Figure 5.
Figure 5.
Figure 5.

Norway: Common and Actual Inflation by Sector, 2000-06

(Year-on-year percent change, seasonally adjusted)

Citation: IMF Staff Country Reports 2007, 197; 10.5089/9781451829815.002.A001

Sources: National authorities; and IMF staff estimates._common components.....................actual

11. But which sectors are the most likely to experience price pressures looking ahead? (Table 1) reports the difference and ratio between underlying and actual inflation in Norway. While there are no clear signs that headline inflation should dramatically increase (the ratio of underlying to actual inflation is close to one and its average over the sample period), inflation may pick up in those sectors—such as alcoholic beverages and tobacco, clothing and footwear,4 recreation and communication services, and the miscellaneous category—in which inflationary pressures have mounted as indicated by a ratio above its average and/or above its current level.5 This would mean that idiosyncratic influences, which have contributed to lower inflation in these sectors, would dissipate, consistent with the view that they affect inflation only in the shorter term.

Table 1.

Norway: Difference Between Underlying and Actual Inflation, 2000-06

article image
Source: National authorities; Eurostat; and IMF staff calculations.

12. Relatedly, one can look at measures of core inflation to provide additional insights into the inflation outlook. (Figure 6) decomposes various measures of core inflation into underlying core inflation and, as a residual, idiosyncratic core inflation. Although some of the items that are excluded from these measures of core inflation were responsible for the pickup in inflation in 2006, the results of this exercise suggest that underlying inflation is stronger than indicated by actual core inflation, and not as far below NB’s target. As mentioned above, some items that are included in the various indices of core inflation that NB uses show mounting inflationary pressures.

Figure 6.
Figure 6.

Norway: Core Inflation, 2000-06

(Year-on-year percent change, seasonally adjusted)

Citation: IMF Staff Country Reports 2007, 197; 10.5089/9781451829815.002.A001

Sources: National authorities; and IMF staff estimates.

13. A cross-country analysis reveals that underlying inflation in Norway explains somewhat less than the average 65 percent of the variability of actual inflation for the whole panel (Table 2). Variation across countries indicates that idiosyncratic shocks can have substantial impact on local inflation developments. Figure 7 shows that, compared to Norway, EU15 countries as a group appear to face stronger underlying inflationary pressures. Finally, Figure 8 plots openness—proxied by the ratio of trade in goods and services to GDP—against the share of total variability accounted for by the underlying component of inflation, providing some evidence that the explanatory power of underlying inflation would increase with country’s openness.

Table 2

Inflation Variance

article image
Sources: Eurostat; National authorities; and IMF staff calculations.
Figure 7.
Figure 7.

Common and Actual Headline Inflation, 2000-06

(Year-on-year percent change, harmonized index, seasonally adjusted)

Citation: IMF Staff Country Reports 2007, 197; 10.5089/9781451829815.002.A001

Sources: National authorities; and IMF staff estimates.
Figure 8.
Figure 8.

Explanatory Power of Common Inflation and Openness

Citation: IMF Staff Country Reports 2007, 197; 10.5089/9781451829815.002.A001

Share of total variability

D. Conclusion

14. “With the substantial number of businesses now facing capacity constraints, we can expect inflation to pick up. It is uncertain whether inflation will then rise quickly or only gradually near target.”6 The analysis of common and idiosyncratic components of inflation in this chapter confirms that inflationary pressures in Norway mounted in 2006. In fact, a comparison among underlying inflation, as defined in this chapter, and various measures of core inflation (CPI-ATE, CPI-AE, CPI-AEL) suggests that the rise in inflation may gain momentum, pointing to the need for additional caution in conducting monetary policy.

15. The common component of inflation derived in this chapter is one possible measure of underlying inflation. A comparison with other measures of underlying inflation such as core inflation, the truncated mean, and the median, particularly with regard to their predictive power, would offer additional insights into the potential developments of inflationary pressures in Norway.

References

  • Bai, J. and S. Ng, 2000, “Determining the Number of Factors in Approximate Factor Models,” mimeo.

  • Forni, M., M. Hallin, M. Lippi, and L. Reichlin, 2000, “The Generalized Factor Model: Identification and Estimation,” Review of Economics and Statistics, Vol. 82, No. 4, pp. 54054.

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  • Forni, M., M. Hallin, M. Lippi, and L. Reichlin, 2003, “The Generalized Dynamic Factor Model One-sided Estimation and Forecasting,” mimeo.

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1

Prepared by Marco Rossi.

2

In addition to Norway, the sample comprises EU15 countries, Canada, Japan, and the U.S.

3

For Norway, different price indices were included in the data set in addition to the harmonized CPI index: the CPI-ATE, the CPI-AE, the CPI-AEL, and the all-items CPI (1998=100). All originally nonseasonally adjusted series were adjusted using additive Census X12.

4

The average ratio for this item of the CPI index would be 0.98 if 2001 were dropped.

5

The impact on headline inflation will, of course, depend on the specific weight each item has in the CPI index.

6

From the address by Governor Gjedrem at the meeting of the Supervisory Council of Norges Bank on Thursday, 15 February, 2007.

References

  • 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 Davis, Rolando Ossowski, and Annalise Fedelino (Washington: International Monetary Fund), pp. 4581.

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  • Bellone, Benoit and Alexandra Bibbee, 2006 ,“The Ageing Challenge in Norway: Ensuring a Sustainable Pension and Welfare System,” OECD Economics Department Working Paper No. 480.

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  • Carcillo, Stephane, Daniel Leigh, and Mauricio Villafuerte, 2007, “Natural-Resource Depletion, Habit Formation, Income Convergence, and Sustainable Fiscal Policy: the Case of Congo,” forthcoming IMF Working Paper.

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    • Export Citation
  • Fredriksen, Dennis and Nils Martin Stølen, 2005, “Effects of Demographic Development, Labour Supply and Pension Reforms On the Future Pension Burden,” Statistics Norway Discussion Papers No. 418, April 2005.

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  • Heide, Kim Massey, Erling Holmøy, Ingeborg Foldøy Solli and Birger Strøm, 2006, “A Welfare State Funded by Nature and OPEC,” Statistics Norway Discussion Papers No. 464, July 2006.

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  • International Monetary Fund, 2005, IMF Staff Country Report No. 05/196, of May 13, 2005 (Washington: International Monetary Fund).

  • Jafarov, Etibar and Kenji Moriyama, 2005, “The Norwegian Government Petroleum Fund and the Dutch Disease,” in IMF Staff Country Report No. 05/197 (Norway—Selected Issues), of May 13, 2005, Chapter III (Washington: International Monetary Fund).

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  • Kumhof, Michael, and Douglas Laxton, 2006, “A Party Without a Hangover? On the Effects of U.S. Government Deficits,” (unpublished, International Monetary Fund).

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  • Leigh, Daniel, and Jan-Peter Olters, 2006 ,“Natural Resource Depletion, Habit Formation, and Sustainable Fiscal Policy: Lessons from Gabon,” IMF Working Paper No. 06/193 (Washington: International Monetary Fund).

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  • OECD, 2003, “Policies For An Ageing Society: Recent Measures And Areas For Further Reform,” Economics Department Working Papers No.369 (ECO/WKP(2003)23).

    • Search Google Scholar
    • Export Citation
  • OECD, 2006, Economic Survey of Norway 2007.

  • United Nations, 2007, Population Projections, http://esa.un.org/unpp/index.asp?panel=2

  • Tersman, Gunnar, 1991 ,“Oil. National Wealth, and Current and Future Consumption Possibilities,” IMF Working Paper No. 91/60 (Washington: International Monetary Fund).

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7

Prepared by Etibar Jafarov (EUR) and Daniel Leigh (FAD).

8

Unless otherwise specified, GDP in this paper refers to mainland GDP, which is all domestic production except from exploration of crude oil and natural gas, services activities incidental to oil and gas, and transport via pipelines; and ocean transport.

9

Norway has been one of the first oil-producing countries measuring its fiscal policy stance based on non-oil budget balances. See Barnett and Ossowski (2003) on why this approach is more appropriate for countries with exhaustible resources.

10

Hereafter, oil and gas revenues/production will be called oil revenues/production.

11

The state receives revenues from oil enterprises through taxes (ordinary corporate income tax at 28 percent; special tax rate for oil producers at 50 percent of income; and the green gas emission (CO2) tax), royalties, fees, its direct financial interest in the petroleum sector (SDFI), and dividends from state shares of Statoil and Norsk Hydro (see IMF 2001).

12

The alternative rules are not necessarily meant to be welfare optimizing. This paper does not analyze inter-generational equity impact of these alternative rules. Heide and others (2006) argue that higher pre-funding of future spending favors future generations, who would be better off even without such redistribution because of economic growth.

13

For the derivation of Equation (1), and its application to a number of oil producing countries, see, for example, Barnett and Ossowski (2003), Leigh and Olters (2006), and Carcillo, Leigh, and Villafuerte (2007). Tersman (1991) applies a similar framework for Norway.

14

See IMF (2006) for recommendations to Gabon made in the context of the 2006 Article IV consultations.

15

In particular, monetary policy follows a forward-looking reaction function that targets the one-year ahead forecast of domestic inflation, and contains an interest rate inertia component in line with the monetary policy literature.

16

Note, however, that households without access to financial markets do not increase consumption in response to the future expected reduction in the tax burden.

Norway: Selected Issues
Author: International Monetary Fund
  • View in gallery

    Inflation Performance, 2000-06

    (Year-on-year percent change, harmonized index, seasonally adjusted)

  • View in gallery

    Inflation Performance, 2000-06

    (Year-on-year percent change, seasonally adjusted)

  • View in gallery

    Cumulative Data Variability Explained by the First Ten Common Factors

    (Percent)

  • View in gallery

    Norway: Common and Actual Headline Inflation, 2000-06

    (Year-on-year percent change, seasonally adjusted)

  • View in gallery View in gallery

    Norway: Common and Actual Inflation by Sector, 2000-06

    (Year-on-year percent change, seasonally adjusted)

  • View in gallery

    Norway: Core Inflation, 2000-06

    (Year-on-year percent change, seasonally adjusted)

  • View in gallery

    Common and Actual Headline Inflation, 2000-06

    (Year-on-year percent change, harmonized index, seasonally adjusted)

  • View in gallery

    Explanatory Power of Common Inflation and Openness