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.

Forecasting, Structural Time Series Models and the Kalman Filter

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

https://www.econstor.eu/bitstream/10419/81822/1/02-28.pdf

Economic growth and income inequality

The Econometrics of Panel DataSpringer Handbook of Science and Technology IndicatorsPanel Data EconometricsThe Econometrics of Panel DataA Practitioner's Guide to Stochastic Frontier Analysis Using StataBenchmarking for Performance EvaluationEssays on Microeconomics and Industrial OrganisationHealth System EfficiencyInternational Journal of Production EconomicsEconometric Analysis of Model Selection and Model TestingInternational Applications of Productivity and Efficiency AnalysisAdvanced Robust and Nonparametric Methods in Efficiency AnalysisEconometrics and the Philosophy of EconomicsThe Measurement of Productive EfficiencyMeasuring Efficiency in Health CareFinancial, Macro and Micro Econometrics Using REconometric Analysis of Cross Section and Panel DataApplied EconometricsProductivity and Efficiency AnalysisEconometric Model SelectionProductivity and Efficiency AnalysisStochastic Frontier AnalysisThe Oxford Handbook of Health EconomicsThe Measurement of Productive Efficiency and Productivity GrowthNew Directions in Productivity Measurement and Efficiency AnalysisA Primer on Efficiency Measurement for Utilities and Transport RegulatorsPanel Data EconometricsProduction and Efficiency Analysis with RApplications of Modern Production TheoryThe Measurement of Productive EfficiencyNonparametric Econometric Methods and ApplicationAn Introduction to Efficiency and Productivity AnalysisHealth, the Medical Profession, and RegulationThe Analysis of Household SurveysData Envelopment AnalysisProgramming Collective IntelligenceEfficiency AnalysisProductivity and Efficiency AnalysisMeasurement of Productivity and EfficiencyProduction Frontiers

The Econometric Approach to Efficiency Analysis

This paper describes the correlations between inequality and the growth rates in cross-country data. Using non-parametric methods, we show that the growth rate is an inverted U-shaped function of net changes in inequality: Changes in inequality (in any direction) are associated with reduced growth in the next period. The estimated relationship is robust to variations in control variables and estimation methods. This inverted U-curve is consistent with a simple political economy model, although, as we point out, efforts to interpret this model causally run into difficult identification problems. We show that this non-linearity is sufficient to explain why previous estimates of the relationship between the level of inequality and growth are so different from one another.

Inequality and Growth: What Can the Data Say?

relationship. All subsequent studies confirmed it (see for example King and Levine, 1993, and the review in: Pagano, 1993). Goldsmith used a sample of 35 countries for the period between 1860 and 1963 to show the relationship between income and financial depth measured by the ratio between bank's assets and GDP. He also showed that in periods of rapid growth, financial depth grows faster than income. More details about measuring financial depth can be found in this paper. FINANCIAL DEVELOPMENT AND ECONOMIC GROWTH Velimir Šonje

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Financial development and economic growth

This paper considers the measurement of firm's specific (in)efficiency while allows for the possible heterogeneous technologies adopted by different firms. A flexible stochastic frontier model with random coefficients is proposed to distinguish technical inefficiency from technological differences across firms. Posterior inference of the model is made possible via the simulation-based approach, namely, Markov chain Monte Carlo method. The model is applied to a real data set which has also been considered in Christensen and Greene (1976), Greene (1990), Tsionas (2002), among others. Empirical results show that the regression coefficients can vary across firms, indicating the adoption of heterogeneous technologies by different firms. More importantly, we find that, without considering this possible heterogeneity, the inefficiency of firms can be over-estimated.

Estimation of Technical Inefficiencies with Heterogeneous Technologies

Smooth-time-varying Okun's coefficients

Inflation and the finance–growth nexus

This paper assesses the long-run effect of growth volatility on income inequality using a comprehensive panel of annual U.S. state-level data during the 1945 to 2004 period. Using the pooled mean group (PMG) estimator, we find overwhelming evidence supporting the hypothesis that larger growth volatility positively and significantly associates with higher income inequality. In addition, our key finding is robust to alternative lag structures, conditioning variables, inequality measures, volatility indicators, and time periods.

/pdf/the-effect-of-growth-volatility-on-income-inequality-1i0l7hcled.pdf

The Effect of Growth Volatility on Income Inequality

This article contributes to the empirical literature of Okun's law in three respects. First, in contrast to the limited data used in the existing studies, we employ two extensive (across countries and across states, i.e. within a country) panel data sets to investigate the validity of Okun's law. Second, the use of the Pooled Mean Group (PMG) estimator permits us not to pre-filter the data as often done in the current literature, and can take into account the possibility of cointegration between unemployment and output. Third, in addition to the short-run relationship or cyclical components between unemployment and output, we also estimate the long-run linkage between these two important variables. Empirical results show that unemployment and output are long-run cointegrated, irrespective of using country- or state-level data. Moreover, the unemployment-output linkages are found to be negative and highly significant both in the short- and long-run. Our results not only confirm the validity of Okun's law (...

Ho-Chuan (River) Huang

Papers

Estimation of Technical Inefficiencies with Heterogeneous Technologies

Smooth-time-varying Okun's coefficients

Inflation and the finance–growth nexus

The Effect of Growth Volatility on Income Inequality

Okun's law in panels of countries and states