Housing, credit and consumer expenditure

Many factors have contributed to the development of credit markets, easing access of households to credit. This paper considers the implications of easier credit for the influence of higher house prices on consumer expenditure. It argues that with poorly developed credit markets, the effect is likely to be negative, but becomes positive as access to housing collateral increases and down-payments for first time homebuyers fall in relation to values. The implications for differences between countries and changes in consumer behaviour over time are explored. Previous studies are reviewed: the omission of credit liberalization and other controls has often biased estimates of housing 'wealth' effects on consumption. New empirical estimates for the U.K. and U.S. suggest that there was no housing 'wealth effect' before credit market liberalization, but that the housing collateral effect is now significant, larger than the stock market wealth effect, and about twice as large in the U.S. as the U.K.Housing ; Consumer behavior ; Credit

Some Issues in Modeling and Forecasting Inflation in South Africa.

Abstract: Inflation targeting central banks will be hampered without good models to assist them to be forward-looking. Many current inflation models fail to forecast turning points adequately, because they miss key underlying long-run influences. The world is on the cusp of a dramatic turning point in inflation. If inflation falls rapidly, such models can underestimate the speed at which interest rates should fall, damaging growth. Our forecasting models for the new measure of producer price inflation suggest methodological lessons, and build in conflicting pressures on SA inflation from exchange rate depreciation, terms of trade shocks, collapsing oil, food and other commodity prices, and other shocks. Our US and SA forecasting models for consumer price inflation underline the methodological points, and suggest the usefulness of thinking about sectoral trends. Finally, we apply the sectoral approach to understanding the monetary policy implications of introducing a new CPI measure in SA that uses imputed rents rather than interest rates to capture housing costs

Some Issues in Modeling and Forecasting Inflation in South Africa.

This invited overview paper draws on our South African and United States (US) inflation modelling and forecasting experience. The current global crisis highlights the importance for policy-makers of having good models for forecasting inflation. Central banks’ caution about inflation risks (expressed, for example, in the Federal Reserve minutes of 16 September 2008, released on 7 October, and the European Central Bank’s 2 October 2008 statement about the decision to leave interest rates unchanged) may have been understandable, given the inflation shocks of 2008. However, it suggested the major central banks were ‘behind the curve’

Multi-sector inflation forecasting – quarterly models for South Africa.

Inflation is a far from homogeneous phenomenon, a fact often neglected in modeling consumer price inflation. Using a novel methodology grounded in theory, the ten sub-components of the consumer price index (excluding mortgage interest rates), are modeled separately and forecast, four-quartersahead. Equilibrium correction models in a rich multivariate form employ general and sectoral information, and take account of structural breaks and institutional changes. Our methods allow for longer lags than conventionally considered in VARs, but in a parsimonious manner. Sign priors are imposed on long-run effects and automatic model selection is used to select parsimonious models from more general ones. The models throw light on sectoral sources of inflation, useful to monetary policy. Data for 1979 to 2003 are used for model selection, and pseudo out of sample forecasting performance to the end of 2007 is examined. Aggregating the weighted sub-component forecasts indicates gains are made over forecasting the overall index using these methods, and also substantial gains over forecasting using benchmark naïve models. To extend this work, including sectoral information such as an explicit treatment of tax policy, regulatory information and announced administered price rises, should further enhance these forecasting methods