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Graduate School of Business and Law
Doctoral Symposium
To Raise or not to Raise?
Exploring the Effects of UK Monetary Policy on Prices and Unemployment
Key words
VAR, monetary policy, unemployment, inflation
Author
Jonathan Rae
Email address
[email protected]
Early Researcher track
Effects of Monetary Policy
To Raise or not to Raise?
Exploring the Effects of UK Monetary Policy on Prices and Unemployment
Abstract
This research has been completed in order to provide empirical grounds for policy makers concerning
the decision to raise interest rates in the United Kingdom. After providing the context for the study, a
short literature review has been completed which identifies the VAR methodology as a suitable means
of modelling the dynamic relationship between multiple macro-economic variables. As well as a
reduced-form VAR, two different structural VARs (SVARs) will be developed to investigate whether the
Band of England (BoE) adopts a forward- or backward-looking stance when deciding on the base rate.
The findings will indicate whether there is a strong argument to suggest that the BoE should consider
raising interest rates, and will shine some light on the relationship between interest rates, prices and
unemployment. The paper is original in the sense that it focuses on recent data in the U.K., and
investigates the effect on house prices separately from the prices of other goods.
I.
I.I.
Introduction
Economic Bubbles
Economic ‘bubbles’ are said to occur when the price of an asset rises beyond that
implied by its intrinsic value for a period of time, and are named as such due to being
unsustainable. Eventually the asset reaches a price at which new investors no longer
consider purchasing the asset viable and the reduction in demand causes prices to
level off. If at this point large numbers of speculative investors recognise the asset is
no longer increasing in value and choose to sell their holdings, the price of the asset
can drop sharply. At this point, the bubble is said to have ‘burst’.
Minsky (n.d.) postulates that these ‘boom’ and ‘bust’ periods are endemic to a capitalist
economy. Minsky’s financial instability hypothesis states that increased confidence in
an economy encourages borrowers and lenders to become progressively more
reckless, which in turn increases the money supply. This excess optimism creates
financial bubbles which later burst. As such, Minsky argues that capitalist economies
are prone to move from periods of stability to instability, which as a form of market
failure requires government intervention to control.
One such way a government may opt to increase or decrease spending in the economy
is with monetary policy. Raising the interest rate at which banks borrow money in turn
increases the cost of borrowing for individuals. In this environment, higher interest
rates simultaneously incentivise saving. Conversely, the government can decrease
interest rates to allow more people access to credit, whilst making saving appear
unappealing.
I.II.
The U.S. Housing Bubble and subsequent Financial Crisis
Between January 2000 and May 2004, former United States Federal Reserve
Chairman Alan Greenspan continually cut interest rates to stimulate economic growth.
During this time, the average house price increased in value by 48.3% (see Fig. 1),
largely due to an abundance of cheap credit. Additionally, complex financial
innovations incentivised mortgage lenders to relax their lending standards, allowing
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Effects of Monetary Policy
virtually anyone to borrow the money to purchase a home. Between May 2004 and
July 2006, house prices rose another 24.5%, despite the Federal Reserve raising
interest rates to 5.25% in this time.
Figure 1: US House Prices vs Federal Funds Rate
Source: S&P Dow Jones Indices, February 2016; Federal Reserve Bank of St Louis, February 2016
At this point, many homeowners began defaulting on their mortgages, presumably as
mortgage payments became too expensive. As defaults increased, there became an
oversupply of houses relative to demand, causing prices to level off before dropping
sharply. At this point, many homeowners who could still afford to pay their mortgages
chose to walk away from their home, as the amounts outstanding on their mortgages
greatly exceeded the market value of the property.
Investors around the world felt the effects of the crash, as many pension funds were
exposed to the U.S. housing market due to the AAA ratings of the bonds involved.
Many economies around the globe saw productivity and output decline, and
unemployment soared.
It could be argued intuitively from economic theory that the rise in interest rates been
made mortgage payments unaffordable for the vast majority of homeowners at this
time, leading to widespread defaults. However whilst the causal relationship can be
analysed qualitatively, it can also be tested quantitatively due to the ease of access to
relevant data. It cannot be known whether home prices would have continued to
increase indefinitely had interest rates not been raised, or alternatively whether the
bubble would have burst of its own accord. The latter scenario raises questions
concerning the efficacy of monetary policy to control inflation.
I.III.
The Return of Low Interest Rates
To stimulate growth the U.S. and other countries affected by the crash have retained
record low interest rates. This includes the United Kingdom, who like many other
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Effects of Monetary Policy
nations saw the economy slip into a recession during the financial crisis. This resulted
in reduced economic output and increased unemployment, as illustrated by Figure 2.
Figure 2: UK Unemployment Rate vs Base Rate vs House Prices
Source: The World Bank, February 2016; Nationwide, February 2016
It could be argued that low interest rates were required to encourage spending and
help the country out of a recession; the recent fall in unemployment provides some
evidence that the measure was justified and at least partially successful. Whilst house
prices on a national scale have remained fairly stable throughout this period however,
there is evidence of high levels of price growth in some localised regions. London is
one such area, with house prices on average around 20% higher than those at the
height of the bubble.
This raises the question as to whether the central bank should exercise its control over
interest rates to slow the growth of house prices to prevent another bubble from
developing. This scope of this piece of research is narrower however, owing to the
scale and resources required to answer the full question. As such, this research will
explore the macro-economic relationships between interest rates, unemployment and
prices. Additionally, prices will be split into two variables: (1) the consumer price index
(CPI), used to measure inflation of prices of everyday goods, and (2) house prices.
The paper will be set out as following: Part II consists of a short literature review which
investigates the relationship between monetary policy and the aforementioned
variables, and identifies the most suitable models to use. In Part III the methodology is
outlined and models are explicitly defined. Part IV presents the expected results and
implications for policy makers.
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Effects of Monetary Policy
II.
Literature Review
A new macroeconomic framework was devised in 1980 when Christopher Sims
developed vector autoregressions (VARs) (Sims, 1980). VARs provide a systematic
way to capture rich dynamics in multiple time series (Stock and Watson, 2001). In a
macroeconomic context, VARs model can be used to understand how multiple
variables interact with each other in a complex system. A common approach, and the
one taken by this paper, is to understand the impact of monetary policy on various
other variables, such as unemployment and prices.
Elbourne (2008) constructs an eight variable structural VAR (SVAR) of the UK
economy, and concludes that a 1% temporary increase in short-term interest rates
results in a fall in inflation and a house price decrease of 0.75%. These findings are in
line with those of Aoki et al. (2002) and Giuliodori (2005), who similarly experience a
decline in house prices following an increase in interest rates. This could be due to the
reduced demand for mortgages as the price of the loan increases.
One thing in common with the aforementioned studies is that the data used preceded
the financial crisis. It may well be the case that a structural break is required when
modelling these extraordinary time periods. Mishkin (2009) conducts a literature to
investigate whether monetary policy is effective during financial crises, and concludes
that, converse to some viewpoints, monetary policy is more potent during financial
crises that under normal conditions. This is largely a qualitative piece however, and
offers very little in terms of empirical support for this conclusion. Nevertheless, if correct
these findings suggest that a shock to interest rates should still have some discernible
effect on our other variables, regardless of whether or not a financial crisis is present.
The empirical evidence then suggests that a rise in interest rates would result in a fall
in both house prices and inflation. This poses a problem to the U.K. government
however. As illustrated by Figure 3, the government may wish to raise interest rates in
order to slow the growth of house prices, particularly in certain localised regions (such
as London). However, low levels of inflation are already present, and further decreases
as a result of raising interest rates could result in the inflation turning negative.
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Figure 3: UK House Prices vs Inflation Rate
Source: The World Bank, February 2016; Nationwide, February 2016
Lastly, it appears that since the introduction of VARs in 1980 they have been a popular
choice for modelling the macro-economy. Borrowing from Stock and Watson (2001),
VARs exist in three varieties, each with different properties. These are: reduced form,
recursive and structural. Reduced form VARs are the most simplistic, and are limited
in their suitability for this piece of research. This is largely due to some degree of
correlation between our variables which will result in our error terms being similarly
correlated. Recursive VARs ensure that the error terms are uncorrelated with the error
in the preceding equations. The ordering of the variables in a recursive VAR however
is important must be chosen carefully, perhaps through the use of Granger Causality
tests.
The final VAR type is a structural VAR (or SVAR), and are most commonly used in
macro-analysis. A SVAR uses economic theory to sort out the contemporaneous links
between the variables (Bernanke, 1986; Blanchard & Watson, 1986; Sims, 1986). As
such, SVARs require “identifying assumptions” that allow correlations to be interpreted
causally (Stock & Watson, 2001). As SVARs have been the most common choice of
model in the empirical studies discussed above, it is most likely going to be the optimal
model for this piece of research.
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Effects of Monetary Policy
III.
Methodology & Data
The literature review has identified vector autoregressive (VAR) models as being the
most suitable for this piece of research, largely owing to the complex nature of
macroeconomic dynamics and the multidirectional causal relationships present.
Additionally, this research will follow in the steps of Stock and Watson (2001) who
provide an excellent overview of modelling the US macro-economy. This research
possesses two significant differences; first, the previous authors did not isolate house
prices, whilst this study divides ‘Prices’ into CPI and house prices. The second
difference is the obvious usage of UK data.
The variables collected include UK real house prices, from which an index has been
created, the unemployment rate, the CPI, and the base rate. Data has been collected
from the World Bank, Nationwide, and the Office of National Statistics (ONS). The data
contains quarterly observations from Q1 1989 to Q3 2014, comprising 103
observations in total.
Initially a reduced-form VAR is estimated, before using economic theory to construct
two related structural VARs. Each SVAR incorporates a different assumption that that
identifies the causal influence of monetary policy on unemployment, inflation and
interest rates. The first ‘backward-looking’ SVAR uses a version of the Taylor rule in
which the Bank of England (BoE) is modelled as setting the base rate based on past
rates on inflation and unemployment. The second SVAR is ‘forward-looking’, and uses
four-quarter ahead forecasts of inflation and unemployment taken from the estimates
of the reduced-form VAR.
Lastly, orthogonalised impulse response functions will be generated to understand how
an unexpected shock to one of the four variables affects each of the other three. This
will provide a graphical representation of the dynamic relationship between each of our
four variables, and can be used to justify or argue against the decision to raise interest
rates.
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Effects of Monetary Policy
IV.
Possible Results and Implications for Policy Makers
This research may indicate that raising interest rates would be a sensible policy
decision. This may be due to the resultant slowdown of house price growth, or may be
found to increase inflation towards the target level of 2%. Similarly, it may be the case
that raising interest rates would reduce unemployment. All these outcomes are
positive, and represent a best-case scenario. Similarly, if it becomes apparent that
raising interest rates would have the opposite effects on each of the three variables,
the analysis could be put forward as an argument against raising interest rates. Lastly,
we must give consideration to the possibility that interest rates have no significant
bearing on output or prices, though this is admittedly unlikely.
This piece of research will offer a significant contribution to knowledge to the subject
area. First, this is the first piece of research (to the author’s knowledge) which places
particular emphasis on analysing the relationship between interest rates and house
prices in the U.K. Second, it will strive to establish whether the financial crisis was
somewhat forecastable.
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References
Aoki, K., Proudman, J. and Vlieghe, G. (2002). House Prices, Consumption, and Monetary
Policy: A Financial Accelerator Approach. SSRN Electronic Journal.
Bernanke, B. (1986). Alternative explanations of the money-income correlation. CarnegieRochester Conference Series on Public Policy, 25, pp.49-99.
Blanchard, O.J. and Watson, M.W., 1986. Are business cycles all alike?. InThe American
business cycle: Continuity and change (pp. 123-180). University of Chicago Press.
Data.worldbank.org, (2016). Data | The World Bank. [online] Available at:
http://data.worldbank.org/ [Accessed 5 Feb. 2016].
Elbourne, A. (2008). The UK housing market and the monetary policy transmission mechanism:
An SVAR approach. Journal of Housing Economics, 17(1), pp.65-87.
Giuliodori, M. (2005). The Role Of House Prices In The Monetary Transmission Mechanism
Across European Countries. Scottish J Political Economy, 52(4), pp.519-543.
Minsky, H. (n.d.). The Financial Instability Hypothesis. SSRN Electronic Journal.
Mishkin, F. (2009). Is Monetary Policy Effective during Financial Crises?. American Economic
Review, 99(2), pp.573-577.
Nationwide.co.uk, (2016). House Price Index Headlines | Nationwide. [online] Available at:
http://www.nationwide.co.uk/about/house-price-index/headlines [Accessed 5 Feb. 2016].
Ons.gov.uk, (2016). Office for National Statistics (ONS) - ONS. [online] Available at:
http://www.ons.gov.uk/ons/index.html [Accessed 5 Feb. 2016].
Research.stlouisfed.org, (2016). Effective Federal Funds Rate (FEDFUNDS). [online] Available
at: https://research.stlouisfed.org/fred2/series/FEDFUNDS/downloaddata [Accessed 5 Feb. 2016].
Sims, C. (1980). Macroeconomics and Reality. Econometrica, 48(1), p.1.
Sims, C.A., 1986. Are forecasting models usable for policy analysis?.Federal Reserve Bank of
Minneapolis Quarterly Review, 10(1), pp.2-16.
Stock, J. and Watson, M. (2001). Vector Autoregressions. Journal of Economic Perspectives,
15(4), pp.101-115.
Us.spindices.com, (2016). S&P/Case-Shiller U.S. National Home Price Index - S&P Dow Jones
Indices. [online] Available at: http://us.spindices.com/indices/real-estate/sp-case-shiller-us-nationalhome-price-index [Accessed 5 Feb. 2016].
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