United Kingdom: Recent Economic Developments

This paper reviews economic developments in the United Kingdom during 1991–95. Following a brisk expansion in 1994, when the economy achieved a rare combination of above-trend output growth and historically low inflation, the pace of economic activity in the United Kingdom has moderated in the course of 1995. Real GDP growth picked up to an annual rate of 4 percent in the first half of 1994, moderating to 3½ percent in the second half. Real GDP growth slowed further to just more than 2 percent in the first half of 1995.

Abstract

This paper reviews economic developments in the United Kingdom during 1991–95. Following a brisk expansion in 1994, when the economy achieved a rare combination of above-trend output growth and historically low inflation, the pace of economic activity in the United Kingdom has moderated in the course of 1995. Real GDP growth picked up to an annual rate of 4 percent in the first half of 1994, moderating to 3½ percent in the second half. Real GDP growth slowed further to just more than 2 percent in the first half of 1995.

III. Inflation Targeting in the United Kingdom: Information Content of Financial Variables 4/

Soon after sterling’s departure from the Exchange Rate Mechanism (ERM) of the European Monetary System in September 1992, the United Kingdom adopted an explicit inflation target as the framework for conducting monetary policy. The targeted measure of inflation is the 12-month change in the retail price index excluding mortgage interest payments (RPIX). The target range for RPIX inflation established in October 1992 was 1 to 4 percent, with the additional objective of being in the lower half of that range by the end of the current Parliament (by the spring of 1997). In June 1995, the Chancellor of the Exchequer announced that he would continue to aim at achieving inflation of 2 1/2 percent or less, and that setting interest rates according to this objective should keep actual inflation between 1 and 4 percent most of the time. 5/

Inflation targeting has come into prominence in the 1990s. With its new framework, the United Kingdom joined New Zealand, which adopted an explicit inflation target in 1990, and Canada, which adopted it in 1991. Others such as Sweden and Finland have also joined the group of countries in which monetary policy is conditioned by inflation targets.

While inflation targeting in the United Kingdom arose initially as the immediate response to the aftermath of sterling’s exit from the ERM, it can be perceived in broader terms as an answer to the inadequacies of earlier intermediate targeting strategies. The United Kingdom experimented with a number of intermediate targets for conducting monetary policy in the 1970s and 1980s. Following the abandonment of the fixed exchange rate system in the early 1970s and a subsequent period in which there was no formal anchor for monetary policy, the United Kingdom adopted formal monetary targets in the early 1980s. Targeting monetary aggregates proved to be of limited success, as rapid financial innovation, just as in many other countries during the 1980s, rendered the relationship between monetary aggregates and various measures of activity tenuous. 1/ From the latter part of the 1980s onwards, the exchange rate assumed greater importance as an indicator of monetary conditions. Before the U.K. formally joined the ERM in October 1990, exchange rate targeting was implicitly undertaken with the policy of “shadowing” the deutsche mark. The period of exchange rate targeting came to an end with the suspension of sterling’s membership of the ERM in September 1992.

The main objectives of this paper are two fold. The first is to discuss some of the conceptual issues pertaining to inflation targeting in the United Kingdom in a wider context. The second is empirical. Estimates are provided of the information that can be gleaned on future inflation and activity from a variety of financial variables. 2/ The econometric methodology adopted is that of non-structural vector autoregressions. The main conclusions of this paper are that narrow money (M0) has a strong leading indicator property for inflation in the United Kingdom. Broad money, in contrast, does not appear to have much predictive content for inflation. The surprising result of this paper is that the nominal exchange rate does not have a leading indicator property for inflation. Beyond that, long yields appear to have some predictive information for the GDP deflator and “headline inflation” (i.e., changes in RPI, the retail price index including mortgage interest), and the short rates for RPIX. The spread between commercial paper and gilts also has some predictive power for some indicators of inflation, but the yield curve does not appear to have strong leading indicator properties for inflation. These results, as discussed later, appear to be somewhat different from the findings on leading indicator properties for the United States, but are largely consistent with other studies for the United Kingdom.

1. Inflation targets: conceptual issues

Should one be targeting inflation at all in the first place? Or, does targeting nominal income, or the price level provide a better framework for conducting monetary policy? The answers to these questions are likely to vary, depending both on the model of the economic process that one uses, as well as the nature of the stochastic shocks to which the economy is subject.

The main conceptual argument for inflation targeting is based on the assessment that while monetary policy can affect real activity in the short run, it cannot do so over the long-run. If this indeed happens to be the case, then monetary policy can achieve better results by targeting the nominal variables that it can influence in the long run. If, in addition, high inflation has costs in the form of high volatility of output, it is obviously best to target low inflation. The implicit assumption behind this type of a conceptual framework is that hysteresis effects are not very important in practice. However, when hysteresis effects or path-dependency turns out to be an important feature of the economy, then there may be a case for monetary policy to target nominal income in order to have greater control over the starting point of the long-run dynamics. 1/

A second criterion for choosing between inflation and nominal income targeting can be based on the nature of the shocks that affect the economy. A demand shock raises both inflation and output, and a monetary policy that targets inflation will not have to deal with adverse tradeoffs. With supply shocks, however, inflation and output are likely to move in opposite directions, and nominal income targeting may prove to be a less costly strategy for monetary policy. The advantage of nominal income targeting over inflation targeting in this case can, of course, be mitigated to the extent that the inflation target makes special provisions for supply shocks, e.g., by allowing deviations from the target path in case of, for instance, terms of trade shocks. 2/ One possible way of choosing between inflation and nominal income targeting is to identify the relative importance in practice of supply and demand shocks. One could, for instance, use statistical techniques 1/ for identifying whether supply or demand shocks have predominated over a given history, and choose between inflation and nominal income targeting based on the past history of shocks. However, this still leaves open issues of whether supply and demand shocks are truly identified, and even if identified correctly, whether past shocks provide information about the future pattern of stochastic shocks.

A related issue in this context is the criterion for deciding between inflation and price level targets. Unlike price level targeting, inflation targeting does not require past stochastic deviations of inflation from given targets to be corrected for. Consequently, the price level under inflation targeting is a random walk, so that devising long-term nominal contracts are made difficult when inflation is being targeted. However, this has to be set against the fact that price level targeting requires considerable volatility in output, as past inflationary shocks need to be offset under this regime. If there are costs associated with adjusting to different levels of economic activity, as one would reasonably expect to be the case, inflation targeting may prove to be a better option than price level targeting. 2/

How broad or narrow should the inflation target be? There is very little that economic theory can offer in the way of precise guidance, other than to state that too wide a range may lack credibility. The desirable range for the inflation target also depends on the way that monetary policy is visualized--whether in the activist framework of being an effective strategy for controlling fluctuations, or in the Friedmanite tradition of avoiding being a cause of fluctuations. 3/ The implicit policy conclusion of the latter perspective is that too narrow a range requires monetary fine-tuning, which has the potential for destabilizing activity. While it is difficult on theoretical grounds to state precisely what a desirable range for the inflation target should be, it is easier to argue on a priori grounds that a zero inflation rate may not be optimal, since negative real interest rates may be desirable under certain circumstances. In addition, factors such as money illusion in the labor market may make the task of implementing a zero inflation target difficult. The United Kingdom has dealt with the problem of choosing a range in a practical way by relating it to forecast errors. The main idea is that if an inflation target of about 2 1/2 percent is aimed for, the likely outcome is for inflation to be in the range of 1 to 4 percent. The range itself is seen as a prerequisite for providing a credibility band.

2. Targeting inflation: the information variable approach

How does one go about the job of targeting inflation? The information variable approach lends itself most naturally as the appropriate framework for conducting monetary policy. The main features of the information variable approach are best understood by contrasting it with the intermediate targeting strategy. 1/ In the information variable approach, the search is for a set of variables that can forecast inflation well, whereas the intermediate targeting strategy calls for the choice of a variable that has a causal relationship with inflation, and which can also be influenced by the instruments at the disposal of the monetary authority. Forecasting power is the most important criterion in the information variable approach, since the instruments under the direct control of the monetary authority impact on inflation only with considerable lags. While it is also necessary for a successful intermediate target to be able to forecast inflation, this is not in itself a sufficient condition, as is the case with the information variable approach. The intermediate targeting strategy is to a large extent conditional on the existence of a stable structural relationship between the intermediate target and the ultimate objective of monetary policy.

The same variable, depending upon the way it is used, can serve either as an intermediate target or as an information variable. The exchange rate, for instance, is an intermediate target under a fixed exchange rate regime, whereas it can serve as an information variable under a floating regime. Again, loosely speaking, monetary aggregates are information variables if the focus is not on the causal relationship between money and activity, but is mainly on whether changes in monetary aggregates predict changes in activity and inflation. To put it in the language of Friedman and Kuttner (1992), as long as movements in money do contain information about future movements in income beyond what is already contained in income itself, monetary policy can exploit that information by responding to observed money growth, regardless of whether the information it contains reflects true causation, reverse causation based on anticipations, or mutual causation caused by some independent but unobserved influence.

The main advantage with the information variable approach is that one can make use of a variety of variables, including non-financial variables, for implementing monetary policy. This is particularly useful when the economy is subject to large structural changes. Variables which cease to predict inflation well can be shed from the information set and, where possible, be replaced by other variables which predict better. The instability in the 1980s between broad monetary aggregates and nominal activity pointed out earlier, can be sorted out in the information variable approach by shifting the focus to a narrower monetary aggregate, if that happens to predict inflation better. It does not matter all that much that broad money should be the more important causal determinant of nominal activity. Similarly, under the information variable approach, one is not committed for credibility reasons to a particular nominal value of the exchange rate, if that rate starts having adverse consequences for the economy.

At a deeper level, the relationship between the information variable approach and the intermediate targeting strategy is closely tied up to questions about rules versus discretion and the related issues of active versus passive orientation in monetary policy. Intermediate targets correspond most closely to the passive orientation in monetary policy, as for instance, is the case with Friedman’s money supply rule. While there are definite feedback rules from indicators on to monetary policy action under the information variable approach, this does not constitute a “rule” in the strict sense of the term. Feedback rules, especially when there are number of indicators, provide sufficient discretion in practice to the monetary authority in implementing monetary policy. The issue, then, is one of whether rules dominate discretion. The answer very much depends on the policy and institutional settings. As pointed out by Fischer (1990), the traditional argument against the superiority of rules was that any rule which stabilizes the economy, can be simulated by the appropriate discretionary policy. The dynamic inconsistency literature, originating with Kydland and Prescott (1977), resurrected the importance of rules by showing that precommitment could improve the behavior of the economy. However, recent extensions to the Kydland-Prescott model, by invoking the role of reputation in dynamic game theoretical settings, has essentially taken an eclectic position on whether rules dominate discretion in theory. 1/

Given the emphasis on forecasting in the information variable approach, time series techniques in the econometric tradition of Granger and Sims are particularly apt tools that can be utilized in implementing monetary policy. 2/ As pointed out earlier, a full understanding of the structural features of the transmission mechanism is not a necessary condition for targeting inflation. Consequently, information about the future direction of inflation can be gleaned from causality tests, variance decompositions, and impulse responses derived from non-structural vector autoregressions defined over a variety of indicators. However, there are effective limits on the extent to which one can rely solely on the results of non-structural vector autoregressions for conducting monetary policy. The first is related to the well known problem of Goodhart’s law i.e., that a good predictor of inflation may cease to continue to be so if it becomes the explicit object of monetary policy actions. The second is related to the possibility of unstable feedbacks in situations where structural information is ignored altogether in implementing the information variable approach. 3/ For example, if the treasury bill predicts inflation well, and the operational intervention rate of the monetary authority is increased every time that higher than average treasury bill rates are observed there is a strong possibility of unstable feedbacks due to the likely existence of a positive relationship between the intervention rate of the monetary authority and treasury bills. The solution under these circumstances is obviously to make use of judgements gathered from “out of model” structural information in conducting monetary policy.

3. Empirical tests

This section implements the empirical tests for deriving the information content of financial variables. The strategy adopted is as follows. Firstly, we start with bivariate Granger causality tests which provide information on the leading indicator properties of the variables. The estimated equations are of the form:

ΔXt=α(L)ΔXt1+β(L)ΔYt1+ε(1)

X is the vector target variables, which for this exercise are defined as real GDP (denoted as NGDPZ_R in the tables), the GDP deflator (PGDPZ), the consumer price index (RPI--denoted as PCPIX in the tables) and the consumer price index, excluding mortgage payments (RPIX--denoted as PCPIX_XM in the tables). While the inflation target is defined only in terms of the RPIX, this study takes a broader perspective, and estimates the predictive content of financial indicators for the different measures of inflation.

Y is a vector of financial indicator variables, which includes narrow money (M0--denoted M0Z in the tables), broad money (M4--denoted M4Z in the tables), M4 lending (M4L), the 25-year commercial paper rate (BI25), 20-year gilts (GB20), 10-year gilts (GB10), the 3-month treasury bill rate (TB91), the 3-month inter-bank rate (IB90), the base rate (LR), the spread between the 25-year commercial paper and 20-year gilts (25_20), the spread between 10-year gilts and 3-month treasury bills (91_10), and the nominal exchange rate (EE). L is the lag operator. Table 3.1 provides a more detailed description of all the variables used.

The following set of data transformations have been carried out. All variables were seasonally adjusted with the exception of the interest rates, the spreads (this refers throughout this paper to the difference between the 25-year commercial paper and 20-year gilts), the yield curve (refers to the difference between 10-year gilts and 3-month treasury bills), and the exchange rate. Except for the interest rate variables, the spread, and the yield curve, all variables are in logs. The sample period is from 1969:2 to 1994:4, except for the 25-year commercial paper which starts in 1970, and the nominal exchange rate and RPIX for which the time series starts only in 1975. All variables have been first differenced to take care of stationarity considerations. Augmented Dicky-Fuller tests, with the appropriate representation of the deterministic trend using the sequential procedure outlined in Holden and Perman (1994) has been used for selecting the order of integration. Since first differences on spreads are difficult to interpret intuitively, we also carry out the tests on levels for the spread variables, and find that the results are largely unaffected by the transformations. 1/

Table 3.1.

Variable Definitions and Transformations

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NOTE: The series are quarterly time series and the sample ranges from 1969:2 to 1994:4. For IB25 and 25_20 the sample starts 1970:1 and for PCPIX_XM and EE the first observations are available 1975:1.

F- tests are first carried out for the null hypothesis of the non-Granger causality of the relevant monetary variable, and Table 3.2 presents the marginal significance levels (p-values) for the bivariate Granger causality tests for lag lengths of 1 to 8. The smaller these values, the stronger is the predictive content of the relevant financial variable.

Table 3.2

Granger Causality Tests for Different Lag Order Information Content of Monterary Indicators for Real Growth and Inflation

Bivariate Prediction Equations with Different Lag Length 1 - 8 Lags Sample 1969:03 + Lags - 1994:04

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All series are in first diffferences. The numbers in the table are marginal significance levels (p-values) of F-tests for the HO of non-Granger causality of a monetary indicator.

The second set of tests involve the forecast error variance decompositions for bivariate vector autoregressions defined on the target variables and the monetary indicators. The VAR is structured such that the monetary indicators are last in order. The results are computed with 6 lags for the bivariate VAR (the results were not significantly different when the calculations were repeated with 4 and 8 lags). The forecast error variance decompositions for different forecast horizons are presented in Table 3.3. Each element in the table indicates the percentage variation in the forecast error of the target variables which are explained by the financial variables. It follows that the higher these numbers the stronger is the predictive content of the relevant financial variable.

Table 3.3.

Forecast Error Variance Decompositions for Bivariate Var Models Variance Explained Through Different Monetary Indicators

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VAR order is 6 lags, monetary variable last in the ordering. All variables are in first differences.

The results of the bivariate Granger causality tests reported in Table 3.2 indicate that M0 predicts both GDP, and all indicators of inflation well. Both M4 and M4 lending do a bad job of predicting all indicators of inflation. All the different long yields--25-year commercial paper, 20-year gilts, and 10-year gilts--have predictive information for the GDP deflator and RPI, but not for RPIX. The predictive power of long yields for GDP is insignificant. The short rates, which include the base rate, the inter-bank rate, and the 3-month treasury bill have some predictive information for RPI, and over longer lags for RPIX. The spread between commercial paper and long gilts predicts RPI over most lag lengths, but not RPIX. The surprising finding of the Granger causality tests is that both the yield curve (given by the spread between 10-year gilts and 3-month treasury bills) and the nominal exchange rate do not have much predictive information for all indicators of inflation. The bivariate Granger causality tests indicate that M0 has by far the greatest predictive information on inflation.

The bivariate variance decompositions reported in Table 3.3 reinforce the finding of the Granger causality tests that M0 explains the forecast variance of all indicators of inflation well. Again, both M4 and M4 lending do not predict inflation well. Long yields now turn out to have a good explanation for the forecast variance of output. As in the bivariate Granger causality tests, long yields predict both the GDP deflator and RPI, but not RPIX. The short rates have very little predictive content for the GDP deflator and RPI, but appear to have predictive information for RPIX, as in the case of the Granger causality tests. The bivariate variance decompositions indicate that the nominal exchange rate does not have much predictive information over inflation, but the yield curve does have some predictive power for RPIX.

In order to test the robustness of these results, Granger causality tests are conducted in a multi-variable set up. These tests involve estimating the following VARs:

ΔXt=α(L)ΔXt1+β(L)ΔZt1+Φ(L)ΔYt+1+ε(2)

Again, X and Y are the vectors of the target and financial indicator variables respectively. Z is a vector of variables which are likely to contain information on the target variables, and is defined as follows. For real GDP it includes the GDP deflator and the real exchange rate. For all price variables, it includes real GDP and the real exchange rate. The reason for carrying out this exercise in a multi-variable set up is to test if the financial variables are capturing information from omitted variables which may have an impact on inflation. This experiment is also repeated for calculating the forecast error variance decompositions.

Finally, the multivariate analysis is extended to “blocks” of the monetary indicators. That is, equation 2 is estimated by taking the financial indicators in “blocks”. The purpose is again to test the robustness of earlier findings. For this particular exercise, M0, M4 and M4 lending are taken as Block 1; the 25-year commercial paper rate, 20-year government gilts and 10-year gilts as Block 2; the inter-bank rate, the base rate and the 3-month treasury bill as Block 3; and the spread between 25 year commercial paper and 20-year gilts and the spread between 10-year gilts and 3-month treasury bills as Block 4.

The results of these exercises reinforce the results of the bivariate exercises given in Tables 3.2 and. 3.3. 1/ For the multi-variable Granger causality test, M0’s predictive content for inflation continues to hold. However, M4 now has some predictive information for the RPI over some lags, but none for either the GDP deflator or RPIX. Long yields again predict the GDP deflator and RPI, but not RPIX. Short yields again do not have much predictive information on inflation. Spreads predict both the GDP deflator and RPI, but not RPIX. The yield curve has no predictive information on output or inflation. The nominal exchange rate now has a strong predictive power for both inflation and output. But that is mainly due to the multicollinearity involved in including the real exchange rate and the nominal exchange rate for the multi-variable Granger causality test. When the real exchange rate is dropped from the multi-variable exercise, the nominal exchange rate’s predictive power disappears. However, the predictive power of the other indicators are unaffected when the real exchange rate is excluded.

The multi-variate variance decompositions replicate the results of the bivariate case to a large extent. M0 continues to be a good predictor of both activity and all indicators of inflation M4 now turns out to have some predictive power for real GDP. Again long yields do a good job of explaining the forecast error variance of both the GDP deflator and RPI, but not for RPIX. Short yields have some predictive power for RPIX as in the bivariate case. The spreads have some predictive information on both the GDP deflator and RPI, and the yield curve for RPIX. The nominal exchange rate has fairly high predictive content for RPIX, but this drops sharply when the real exchange rate is dropped, as in the case of the Granger causality tests.

Granger causality tests on four variable equations with the monetary indicators taken in blocks again largely replicate earlier results. The results of the variance decompositions, taking the monetary indicators in blocks, give slightly mixed results as for as the monetary indicators are concerned, but M0 appears to still predict RPIX well. The results of the forecast error variance decompositions are useful, to the extent to which the results do not change when the order of the variables in the VAR model are changed. The results appear to be fairly stable when the ordering of the monetary variables within the blocks are changed. 1/

All things considered, M0 appears to be best predictor of all indicators of inflation. In contrast both M4 and M4 lending do not have much predictive content for inflation. The interesting conclusion of this study is that the nominal exchange rate does not have much predictive power for inflation. This result is a bit puzzling since most structural models of inflation would posit a relationship between the exchange rate and inflation. A plausible explanation for the absence of a leading indicator property for the nominal exchange rate could be that corrective policy action normally follows exchange rate movements in practice. This may result in statistical tests failing to pick up the links between the exchange rate and inflation. Beyond this, long yields appear to have some predictive content for the GDP deflator and RPI, but not for RPIX. The converse is true for short rates. It is, however necessary as pointed out earlier to be mindful of unstable feedbacks in using both long and short yields as indicators of inflation. As in the case of the long yields, the spread between commercial paper and gilts has some predictive content for both the GDP deflator and RPI, but not for RPIX. The yield curve appears to have very little information about the different measures of inflation.

These results are quite different from the findings for the United States, where both the narrow and broad monetary indicators have very little predictive content for inflation. The influential study by Bemanke and Blinder (1992) for the United States indicated that the Federal funds rate had by far the most predictive content for inflation. Monetary aggregates, both narrow and broad, fared poorly. The study by Friedman and Kuttner (1992) indicated that in addition to the Federal Funds rate, the spreads between 6-month commercial paper and 3-month treasury bills had strong leading indicator properties in the United States. However, the results of this paper that the narrow monetary aggregate has powerful leading indicator properties is consistent with previous studies carried out by the Bank of England using different methodologies. 1/

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4/

Prepared by Ramana Ramaswamy and Josef Baumgartner.

5/

The evolution of the new framework in the United Kingdom up to 1994, including also its transparency features, is described in detail in SM/94/257, Chapter VI.

1/

See Temperton (1991), Artis and Lewis (1991), and Breeden and Fischer (1994) for detailed discussions on the behavior of monetary aggregates in the United Kingdom.

2/

The relationship between the output gap and inflation is examined in Chapter V.

1/

Hysteresis describes a situation where temporary shocks have persistent effects. Well known examples of this are in the labor market and international trade. In the labor market for instance, a temporary adverse shock which increases unemployment can prove to be persistent; the unemployed have declining probabilities of future re-employment due to demotivation and atrophying skills. Similarly, in international trade, a temporary exchange rate shock can lead to a permanent loss of export markets, as competitors make sustained gains through brand recognition and other related factors. Such situations are also said to be characterized by path dependency--the starting point influences the evolution of the long run dynamics (see Krugman (1994) for an interesting discussion of these issues). In these circumstances, accommodative monetary policy can, at times, have an impact on activity in the longer run by influencing the starting point itself.

2/

See Fischer (1995) and Hall and Mankiw (1994) for a comprehensive discussion of these issues.

1/

Such as for instance the one used in Blanchard and Quah (1989)

2/

More detailed discussions of these issues can be found in Hall and Mankiw (1994) and Fischer (1994).

3/

See, in this context, Feldstein and Stock (1994) and Mankiw (1994).

1/

Friedman (1990), Friedman and Kuttner (1992) and Woodford (1994) provide interesting discussions of the information variable approach.

1/

See Blinder (1995) for an interesting discussion of these issues.

2/

A useful methodological discussion of these issues can be found in Pagan (1987).

3/

Woodford (1994) has been a strong proponent of not ignoring structural information in inflation targeting.

1/

The results of these tests are available with the authors and can be provided on request.

1/

Tables giving the results of the Granger causality tests and variance decompositions in a multi-variate set up can be provided by the authors on request.

1/

Detailed tables on these results can be provided by the authors on request.

1/

See Breeden and Fisher (1994) and Astley and Haldane (1995).