Predicting UK Business Cycle Regimes

Following on from the work of Birchenhall, Jessen, Osborn & Simpson (JBES, 1999) on predicting US business cycle regimes we apply the same methodology to construct a one period ahead model of classical business cycle regimes in the UK. Birchenhall et al (1999) used regime data implied by the NBER dating of peaks and troughs. In the UK there is no comparable dating committee and our first task is to date the UK peaks and troughs. Application of a simple mechanical rule based on changes in GDP produces a set of acceptable turning points, with one exception that is attributable to the 3-day working week in 1974. Based on data from 1963 to 1999, we date three business cycle peaks at 1973 Q3, 1979 Q2 and 1990 Q2 together with troughs at 1975 Q3, 1981 Q1 and 1992 Q2. Starting with a number of real and financial leading indicators, several parsimonious one-quarter-ahead models are selected largely on the basis of the SIC criterion. A number of interesting results emerge from this investigation. A real M4 variable is consistently found to have predictive content. One model that performs well combines this with UK and German short-term interest rates. The role of the latter variable emphasises the open nature of the UK economy.

Short-term volatility versus long-term growth: evidence in US macroeconomic time series

We test for a change in the volatility of 215 US macroeconomic time series over the period 1960-1996. We find that about 90\\% of these series have experienced a break in volatility during this period. This result is robust to controlling for instability in the mean and business cycle nonlinearities. Real variables have seen a reduction in volatility since the early 1980s, which is accompanied by lower but steadier output growth. Furthermore, nominal variables have seentemporary increases in their volatility around the early 1980s. This suggests the existence of a trade-off between short-term volatility and the long-term pattern of growth.growth;Volatility;Business cycle nonlinearity;Structural change tests

Is volatility good for growth? Evidence from the G7.

We provide empirical support for a DSGE model with nominal wage stickiness where growth is driven by learning-by-doing and money shocks and their variance are allowed to impact on long-run output growth. In our theoretical model the variance of monetary shocks has a negative effect on growth, while output volatility is good for growth as a positive relationship exists. Utilising a bivariate GARCH-M model we test the empirical conditional mean and variance relationships of nominal money and production growth rates in the G7 countries. We corroborate the theoretical model predictions with evidence from Bonferroni multiple tests across the G7.

Explaining movements in UK stock prices:

This paper provides evidence on the causes of movements in monthly UK stock prices, examining the role of macroeconomic and financial variables in a nonlinear framework. We allow for time-varying effects through the use of smooth transition models. We find that past changes in the dividend yield are an important transition variable, with current US stock market price changes providing a second nonlinear influence. This model explains the declines in the UK market since 2000, whereas a competing model excluding current US prices does not. The conclusion is that the principal explanation of recent declines in the UK lies in the nonlinear influence of declines in the US, and not the domestic economic environment.Regime-switching models, smooth transition autoregressive models, linearity tests, model evaluation,

Is Volatility Good for Growth?

We provide empirical support for a DSGE model with nominal wage stickiness where growth is driven by learning-by-doing and money shocks and their variance are allowed to impact on long-run output growth. In our theoretical model the variance of monetary shocks has a negative effect on growth, while output volatility is good for growth as a positive relationship exists. Utilising a bivariate GARCH-M model we test the empirical conditional mean and variance relationships of nominal money and production growth rates in the G7 countries. We corroborate the theoretical model predictions with evidence from Bonferroni multiple tests across the G7.growth uncertainty, learning-by-doing, monetary uncertainty, multivariate GARCH-in-mean, nominal rigidity.

Testing for causality in variance in the presence of breaks

We examine the size properties of tests for causality in variance in thepresence of structural breaks in volatility. Extensive Monte Carlo simulationsdemonstrate that these tests suffer from severe size distortions when suchbreaks are not taken into account. Pre-testing the series for structuralchanges in volatility is shown to largely remedy the problem.structural change;causality tests;volatitilty

Changes in variability of the business cycle in the G7 countries

Volatility breaks are tested and documented for 19 important monthlymacroeconomic time series across the G7 countries. Across all conditional meanspecifications considered, including both linear and nonlinear models with andwithout a structural break, volatility breaks are found to be widespread. Thiscontinues to hold when business cycle nonlinearities are allowed in thevariance. Multiple volatility breaks are also examined, and these are found tobe especially prevalent for short-term interest rates. Volatility breaks inindustrial production and consumer prices are largely synchronous across theG7. The facts established are discussed in the context of some explanationsput forward in the literature to explain volatility breaks previously foundfor US series.Volatility;Growth;Business cycle nonlinearity;Structural change tests

Smooth Transition Regression Models in UK Stock Returns

This paper models UK stock market returns in a smooth transition regression (STR) framework. We employ a variety of financial and macroeconomic series that are assumed to influence UK stock returns, namely GDP, interest rates, inflation, money supply and US stock prices. We estimate STR models where the linearity hypothesis is strongly rejected for at least one transition variable. These non-linear models describe the in-sample movements of the stock returns series better than the corresponding linear model. Moreover, the US stock market appears to play an important role in determining the UK stock market returns regime.