David E. A. Giles

Bio: David E. A. Giles is an academic researcher from University of Victoria. The author has contributed to research in topics: Estimator & Mean squared error. The author has an hindex of 33, co-authored 208 publications receiving 4828 citations. Previous affiliations of David E. A. Giles include Monash University & Monash University, Clayton campus.

Handbook of applied economic statistics

Abstract: Economic statistics and social welfare comparisons - a review measurement of inequality poor areas the demand for health services in a developing country - the role of prices, service quality and reporting of illnesses on mobility aggregation and econometric analysis of demand and supply spatial dependence in linear regression models with an introduction to spatial econometrics panel data methods econometric analysis in complex surveys information recovery in simultaneous equations' statistical models diagnostic testing in econometrics - variable addition, RESET and Fourier approximations applications of the bootstrap in econometrics and economic statistics detection of unusual observations in regression and multivariate data union-intersection and sample-split methods in econometrics with applications to MA and SURE models modelling economic relationships with smooth transition regressions modelling seasonality in economic time series nonparametric and semiparametric econometrics of panel data on calibration.

Measuring the Hidden Economy: Implications for Econometric Modelling

Abstract: In this paper I support using econometric techniques to measure the size of the hidden (underground) economy, because such information is important for the construction of certain economic models, and for empirical policy analysis. Generally, detailed information on the output of the hidden economy is unavailable. Even where careful measures of the underground economy have been constructed, usually these data are available only periodically. Important exceptions are the classic results of Tanzi (1983) for the United States, and Bhattacharyya's (1990) series for the United Kingdom. In the case of the New Zealand economy, a time-series of data on the hidden economy has been generated recently (Giles, 1997a). This provides the unusual opportunity to undertake econometric modelling in a way which takes account of such activity formally. Moreover, we can examine the policy implications arising from the linkages between hidden output and various measured economic aggregates. (This abstract was borrowed from another version of this item.)

Seemingly unrelated regression equations models : estimation and inference

Abstract: This book brings together the scattered literature associated with the seemingly unrelated regression equations (SURE) model used by econometricians and others. It focuses on the theoretical statistical results associated with the SURE model.

Taxes and the Canadian underground economy

Abstract: In this volume we report the results of an extensive empirical study into the size of the Canadian underground economy, its development from the mid-1970's to the mid-1990's, and some of the linkages between taxation policy and underground activity in this country. First, we estimate that the Canadian Underground Economy grew from about 3.5% of measured GDP in 1976, to almost 16% in 1995. The latter figure accords well with recent evidence for Canada obtained by Schneider by totally different means - he estimates that it averaged 14.8% in 1994/95 and 16.2% in 1997/98. Second, when the implications of an underground economy of this size are explored in terms of the amount of tax revenue that is lost, we find that the size of this "tax-gap" varied from approximately $2 billion in 1976 to almost $44 billion in 1995, in current-dollar terms. Third, we establish a clear and positive empirical relationship between the aggregate effective tax rate and the (relative) size of the underground economy. We have shown that there is significant statistical evidence of two-way Granger causality, both from the effective tax rate, to the underground economy; and also from the underground economy to the effective tax rate.

Modelling Volatility Spillover Effects Between Developed Stock Markets and Asian Emerging Stock Markets

Abstract: This paper examines the linkages of stock markets across the U.S., Japan and six Asian developing countries: China, India, Indonesia, Malaysia, the Philippines and Thailand over the period January 1, 1993 to December 31, 2012. The volatility spillover is modeled through an asymmetric multivariate GARCH model. We find significant unidirectional shock and volatility spillovers from the U.S. market to both the Japanese and the Asian emerging markets. It is also found that the volatility spillovers between the U.S. market and the Asian markets are stronger and bidirectional during the Asian financial crisis. Further, during the last five years, the linkages between the Japanese market and the Asian emerging markets became more apparent. Our paper contributes to the literature by examining both the long run and the short run periods and focusing on shock and volatility spillovers rather than return spillovers, which have been the primary focus of most other studies.

Forecasting, Structural Time Series Models and the Kalman Filter

Abstract: In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Table Of Integrals Series And Products

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Economic growth and income inequality

Abstract: We investigate whether income inequality affects subsequent growth in a cross-country sample for 1965-90, using the models of Barro (1997), Bleaney and Nishiyama (2002) and Sachs and Warner (1997), with negative results. We then investigate the evolution of income inequality over the same period and its correlation with growth. The dominating feature is inequality convergence across countries. This convergence has been significantly faster amongst developed countries. Growth does not appear to influence the evolution of inequality over time. Outline

A Practitioner’s Guide to Cluster-Robust Inference

Abstract: We consider statistical inference for regression when data are grouped into clus- ters, with regression model errors independent across clusters but correlated within clusters. Examples include data on individuals with clustering on village or region or other category such as industry, and state-year dierences-in-dierences studies with clustering on state. In such settings default standard errors can greatly overstate es- timator precision. Instead, if the number of clusters is large, statistical inference after OLS should be based on cluster-robust standard errors. We outline the basic method as well as many complications that can arise in practice. These include cluster-specic �xed eects, few clusters, multi-way clustering, and estimators other than OLS.