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.

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.

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.

Tracking unemployment in Wales through recession and into recovery

This paper assesses turning points in the economic cycle of Welsh unitary authorities by applying a mathematical algorithm to the claimant count unemployment data. All but one unitary authority has now emerged from recession (Anglesey being the exception). We also date the business cycle for the UK and country-level employment data and Wales has emerged from recession but Scotland is yet to exit recession. We estimate a logistic model which utilises housing sector and survey data to forecast the Welsh employment cycle. The model predicts that employment in Wales will continue to grow into 2011

Tracking Unemployment in the North West Through Recession and Forecasting Recovery

This is a technical paper that tries to assess turning points in the economic cycle of sub-regions by applying a business cycle dating methodology to monthly North West local authority district claimant count data. We date the transition of all districts of the North West into recession beginning in June 2007. By utilising manufacturing and service sector survey information in a modelling exercise, we forecast the continuation of the recession for the North West region’s employment cycle into 2010. A longer term forecast with the Land Registry’s house price index predicts a transition to an expansion phase in the fourth quarter of 2010

Nonlinearity in the Fed's Monetary Policy Rule

This paper investigates the nature of nonlinearities in the monetary policy rule of the US Fed using the flexible approach of Hamilton (2001a). We find that while there is significant evidence of nonlinearity for the period to 1979, there is little such evidence for the subsequent period. Possible asymmetries in the Fed's reactions to inflation deviations from target and the output gap in the 1960s and 70s may tell part of the story, but do not capture the entire nature of the nonlinearity. The inclusion of the interaction between inflation deviations and the output gap, as recently proposed, appears to characterize the nonlinear policy rule more adequately.nonlinearities, monetary policy rule, Phillips curve, interaction