Denise R. Osborn

Public Expenditure and Economic Growth: A Disaggregated Analysis for Developing Countries

In this paper, we examine the growth effects of government expenditure for a panel of 30 developing countries over the 1970s and 1980s, with a particular focus on disaggregated government expenditures. Our methodology improves on previous research on this topic by explicitly recognizing the role of the government budget constraint and the possible biases arising from omitted variables. Our primary results are twofold. First, the share of government capital expenditure in GDP is positively and significantly correlated with economic growth, but current expenditure is insignificant. Second, at the disaggregated level, government investment in education and total expenditures in education are the only outlays that are significantly associated with growth once the budget constraint and omitted variables are taken into consideration.

Business Cycles for G7 and European Countries

This article proposes classical business cycle turning points for the G7 and a number of European countries based on industrial production. This enables the authors to examine the international nature of cyclical movements free from arbitrary assumptions about the trend. In particular, they show that cyclical assymetry is common, with slopes during declines being generally larger in magnitude than during expansions. A binary measure of association for expansion and contradiction regimes indicates a core group of European countries related to each other and apparently linked to the United States and Japan through Germany. Copyright 1997 by University of Chicago Press.

A survey of seasonality in UK macroeconomic variables

Abstract This paper analyses the nature of seasonality in quarterly observations for thirty important UK macroeconomic variables. These variables include real gross domestic product and its major components, employment variables, price/earnings indices, the rate of interest and the exchange rate, together with nominal and real monetary series. Unit root tests are applied to determine whether the seasonal component in each variable exhibits stochastic nonstationarity. Only six variables are found to have a seasonal unit root; this implies over-differencing in many conventional time series models of seasonal economic variables. Turning to quantitative measures of seasonality, nineteen series have at least 30 percent of their non-trend variation mopped up by seasonal dummy variables alone. Only interest rates and the exchange rate exhibit no significant (deterministic or stochastic) seasonality. Overall, deterministic seasonal effects are relatively more important than stochastic ones.

The distribution of Household Property Crimes

Previous studies have established that repeat victimizations occur more frequently than would be expected if households within a particular area were victimized randomly. This implies that characteristics of the household affect the victimization rate. Even controlling for these characteristics, we find that a Poisson model does not capture the distribution of victimizations because repeat victimizations are more concentrated than it would indicate. This leads us to adopt the negative binomial generalization of the Poisson model. Our analysis uses sociodemographic attributes of the household and community-level characteristics to predict victimizations, with the victimization data being the observed number of property crime victimizations from the 1992 British Crime Survey. The negative binomial generalization is found to be highly statistically significant and the crime concentration it implies becomes much more marked as the predicted number of victimizations increases.

The implications of periodically varying coefficients for seasonal time-series processes

Abstract Periodic ARMA processes have seasonally varying parameters superimposed on a conventional ARMA structure. These varying parameters can arise when seasonality is incorporated into the theory of economic decision-making. Analogously to the aggregate of component ARMA processes having a unvariate representation, the periodic process has a corresponding ARMA representation. This unvariate representation may, however, imply high orders and substantial dynamic misspecification. In terms of forecast efficiency, the true periodic dynamic process dominates its unvariate counterpart.

Linear versus neural network forecasts for European industrial production series

Abstract The value of neural network models in forecasting economic time series has been established for North America, but little work has been undertaken for Europe. This paper considers 24 series measuring the annual change in monthly seasonally unadjusted industrial production for important sectors of the German, French and UK economies. Preliminary testing indicates relatively little evidence of nonlinearity in most series. According to root mean-square error (RMSE), linear models generally produce more accurate post-sample forecasts than neural network models at horizons of up to a year. This applies overall and also to the sub-group of series with substantial sample period evidence of nonlinearity. In contrast, the neural network models dominate linear ones in predicting the direction of change. Therefore, the model chosen by users should depend on the type of forecasts they require.

Asymmetric Interest Rate Effects for the UK Real Economy

Recent literature has uncovered asymmetries in the response of real output to monetary policy variables. Nevertheless, it remains unclear whether such asymmetries relate to different responses to monetary policy or to the business cycle. This paper uses nonlinear models to examine the issues in the context of interest rate effects on quarterly UK GDP growth. Strong evidence of nonlinearity is found, with asymmetry relating to the business cycle through lagged GDP regimes and interest rate changes. The results suggest that interest rate effects on GDP are larger when either lagged growth has been high or when interest rates have substantially increased in the past. However, the inclusion of interest rate regimes without taking account of GDP regimes yields an unsatisfactory model.

The Econometric Analysis of Seasonal Time Series: Subject Index

In this book Eric Ghysels and Denise R. Osborn provide a thorough and timely review of the recent developments in the econometric analysis of seasonal economic time series. The authors discuss the asymptotic distribution theory for linear stationary and nonstationary seasonal stochastic processes. They also cover the latest contributions to the theory and practice of seasonal adjustment, together with its implications for estimation and hypothesis testing. A comprehensive analysis of periodic models is provided, including stationary and nonstationary cases. The book concludes with a discussion of some nonlinear seasonal and periodic models. The treatment is designed for an audience of researchers and advanced graduate students.

The Econometric Analysis of Seasonal Time Series: Contents

In this book Eric Ghysels and Denise R. Osborn provide a thorough and timely review of the recent developments in the econometric analysis of seasonal economic time series. The authors discuss the asymptotic distribution theory for linear stationary and nonstationary seasonal stochastic processes. They also cover the latest contributions to the theory and practice of seasonal adjustment, together with its implications for estimation and hypothesis testing. A comprehensive analysis of periodic models is provided, including stationary and nonstationary cases. The book concludes with a discussion of some nonlinear seasonal and periodic models. The treatment is designed for an audience of researchers and advanced graduate students.

Spillovers and Correlations between US and Major European Stock Markets: The Role of the Euro

This article investigates the impact of the introduction of the euro on the interactions across the New York, London, Frankfurt and Paris stock markets. After controlling for possible returns and volatility spillovers, we focus on the correlations of shocks using the framework of Dynamic Conditional Correlations (DCC). Daily pseudo-closing prices (recorded at 16:00 London time) are used to avoid conflating correlation and spillover effects. Statistical break tests confirm that the introduction of the euro significantly affects the cross-market correlations. Although dynamic correlations of shocks between all market pairs increase, the correlation in the post-euro period is highest between Frankfurt and Paris, indicating increased integration of these markets. Other findings include the presence of spillover effects from foreign markets for both returns and volatilities, with asymmetries in volatilities and conditional correlations such that negative shocks have larger effects than positive ones.