Does Seasonality Change over the Business Cycle? An Investigation using Monthly Industrial Production Series

This paper examines the proposition that the business cycle affects seasonality in industrial production, with output being switched to the traditionally low production summer months when recent (annual) growth has been strong. This is investigated through the use of a restricted threshold autoregressive model for the monthly growth rate in a total of 74 industries from 16 OECD countries. Approximately one third of the series exhibit significant nonlinearity, with this nonlinearity predominantly associated with changes in the seasonal pattern. Estimates show that the summer slowdown in many European countries is substantially reduced when recent growth has been high.

Forecasting UK Industrial Production Over the Business Cycle

This paper examines the information available through leading indicators for modelling and forecasting the UK quarterly index of production (seasonally adjusted). The emphasis is on one-quarter ahead prediction, especially over the 1990s recession. Linear specifications considered are univariate autoregressive models together with dynamic single indicator and multiple indicator models. Both univariate and leading indicator versions of nonlinear Markov switching specifications are also examined. In the latter case, the transition probabilities are modelled as logistic functions of the leading indicators, allowing the lead times to differ for the expansion to expansion and recession to recession probabilities. Despite general evidence that the term structure of interest rates helps regime classification in the Markov switching models, these models perform relatively poorly in forecasting the 1990s production recession. It is suggested that this poor performance may be due to the nature of that recession, which differed from previous major UK postwar recessions in having no single quarter where industrial production declined substantially. However, a three indicator linear specification does well. The leading indicator variables in this latter model are a short-term interest rate, the stock market dividend yield and the optimism balance from the quarterly survey conducted by the Confederation of British Industry.

Seasonal adjustment and the detection of business cycle phases

To date, there has been little investigation of the impact of seasonal adjustment on the detection of business cycle expansion and recession regimes. We study this question both analytically and through Monte Carlo simulations. Analytically, we view the occurrence of a single business cycle regime as a structural break that is later reversed, showing that the effect of the linear symmetric X-11 filter differs with the duration of the regime. Through the use of Markov switching models for regime identification, the simulation analysis shows that seasonal adjustment has desirable properties in clarifying the true regime when this is well underway, but it distorts regime inference around turning points, with this being especially marked after the end of recessions and when the one-sided X-11 filter is employed. JEL Classification: E32, C22, C80business cycles, Markov switching models, Seasonal adjustment

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.

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

On the Expectations Hypothesis in US Term Structure

We extend the vector autoregression (VAR) based expectations hypothesis (EH) test of term structure, considered in Bekaert & Hodrick (2001), B&H thereafter, using recent developments in bootstrap literature. Modifications include the use of wild bootstrap to allow for conditional heteroskedasticity in the VAR residuals without imposing strict parameterization, while keeping their contemporaneous correlation, endogeneous model selection procedure in the bootstrap replications to reflect true uncertainty and the stationarity correction designed to prevent finite- sample bias adjusted VAR parameters from becoming explosive. Since Lagrange Miltiplier, Wald and Distance Metric test statistics used in this study are all asymptotically pivotal we estimate their finite sample distributions using a computer simulation, rather than relying on the approximation provided by the first order asymptotic theory. When the modified B&H methodology is applied to extensive US zero coupon term structure data ranging from 1 month to 10 years we find less rejections for the theory in a sub-sample of Jan 1982- Dec 2003 than in Jan 1952- Dec 1978, and when it is rejected it occurs at the very short and long ends of the maturity spectrum. It is also relieving to note that this inference seems to be robust to both AIC and SIC model selection methods. In terms of the conclusions made about the validity of the EH of term structure, the main difference between this study and its counterpart of Sarno, Thornton & Valente (2006), which uses the original B&H methodology, is that we reject the theory less often than they do. This is probably as one would expect, since we test the EH theory of term structure only as opposed to Sarno et al (2006) who, in effect, are testing a joint null hypothesis of the conditional homoskedasticity in the residuals and exogenous lag length of the VAR along with the EH.expectations hypothesis, term structure of interest rates, vector autoregression