Econometrics: A bird's eye view

As a unified discipline, econometrics is still relatively young and has been transforming and expanding very rapidly over the past few decades. Major advances have taken place in the analysis of cross sectional data by means of semi-parametric and non-parametric techniques. Heterogeneity of economic relations across individuals, firms and industries is increasingly acknowledge and attempts have been made to take them into account either by integrating out their effects or by modeling the sources of heterogeneity when suitable panel data exists. The counterfactual considerations that underlie policy analysis and treatment evaluation have been given a more satisfactory foundation. New time series econometric techniques have been developed and employed extensively in the areas of macroeconometrics and finance. Non-linear econometric techniques are used increasingly in the analysis of cross section and time series observations. Applications of Bayesian techniques to econometric problems have been given new impetus largely thanks to advances in computer power and computational techniques. The use of Bayesian techniques have in turn provided the investigators with a unifying framework where the tasks and forecasting, decision making, model evaluation and learning can be considered as parts of the same interactive and iterative process; thus paving the way for establishing the foundation of the "real time econometrics". This paper attempts to provide an overview of some of these developments

Temporal and spatial homogeneity in air pollutants panel EKC estimations: Two nonparametric tests applied to Spanish provinces

Although panel data have been used intensively by a wealth of studies investigating the GDP-pollution relationship, the poolability assumption used to model these data is almost never addressed. This paper applies a strategy to test the poolability assumption with methods robust to functional misspecification. Nonparametric poolability tests are performed to check the temporal and spatial homogeneity of the panel and their results are compared with the conventional F-tests for a balanced panel of 48 Spanish provinces on four air pollutant emissions (CH4, CO, CO2 and NMVOC) over the 1990-2002 period. We show that temporal homogeneity may allow the pooling of the data and drive to well-defined nonparametric and parametric cross-sectional U-inverted shapes for all air pollutants. However, the presence of spatial heterogeneity makes this shape compatible with different timeseries patterns in every province - mainly increasing or decreasing depending on the pollutant. These results highlight the extreme sensitivity of the income-pollution relationship to region- or country-specific factors.Environmental Kuznets Curve; Air pollutants; Non/Semiparametric estimations; Poolability tests

Temporal and spatial homogeneity in air pollutants panel EKC estimations: Two nonparametric tests applied to Spanish provinces

Although panel data have been used intensively by a wealth of studies investigating the GDP-pollution relationship, the poolability assumption used to model these data is almost never addressed. This paper applies a strategy to test the poolability assumption with methods robust to functional misspecification. Nonparametric poolability tests are performed to check the temporal and spatial homogeneity of the panel and their results are compared with the conventional F-tests for a balanced panel of 48 Spanish provinces on four air pollutant emissions (CH4, CO, CO2 and NMVOC) over the 1990-2002 period. We show that temporal homogeneity may allow the pooling of the data and drive to well-defined nonparametric and parametric cross-sectional U-inverted shapes for all air pollutants. However, the presence of spatial heterogeneity makes this shape compatible with different timeseries patterns in every province - mainly increasing or decreasing depending on the pollutant. These results highlight the extreme sensitivity of the income-pollution relationship to region- or country-specific factors.Environmental Kuznets Curve, Air pollutants, Non/Semiparametric estimations, Poolability tests

Hospital efficiency analysis through individual effects: A Bayesian approach

Monte Carlo Technique;Hospitals;Bayesian Statistics;Markov Chains;Panel Data

Neural Network Based Models for Efficiency Frontier Analysis: An Application to East Asian Economies' Growth Decomposition

There has been a long tradition in business and economics to use frontier analysis to assess a production unit’s performance. The first attempt utilized the data envelopment analysis (DEA) which is based on a piecewise linear and mathematical programming approach, whilst the other employed the parametric approach to estimate the stochastic frontier functions. Both approaches have their advantages as well as limitations. This paper sets out to use an alternative approach, i.e. artificial neural networks (ANNs) for measuring efficiency and productivity growth for seven East Asian economies at manufacturing level, for the period 1963 to 1998, and the relevant comparisons are carried out between DEA and ANN, and stochastic frontier analysis (SFA) and ANN in order to test the ANNs’ ability to assess the performance of production units. The results suggest that ANNs are a promising alternative to traditional approaches, to approximate production functions more accurately and measure efficiency and productivity under non-linear contexts, with minimum assumptions.total factor productivity, neural networks, stochastic frontier analysis, DEA, East Asian economies

Econometrics: A Bird’s Eye View

As a unified discipline, econometrics is still relatively young and has been transforming and expanding very rapidly over the past few decades. Major advances have taken place in the analysis of cross sectional data by means of semi-parametric and non-parametric techniques. Heterogeneity of economic relations across individuals, firms and industries is increasingly acknowledged and attempts have been made to take them into account either by integrating out their effects or by modeling the sources of heterogeneity when suitable panel data exists. The counterfactual considerations that underlie policy analysis and treatment evaluation have been given a more satisfactory foundation. New time series econometric techniques have been developed and employed extensively in the areas of macroeconometrics and finance. Non-linear econometric techniques are used increasingly in the analysis of cross section and time series observations. Applications of Bayesian techniques to econometric problems have been given new impetus largely thanks to advances in computer power and computational techniques. The use of Bayesian techniques have in turn provided the investigators with a unifying framework where the tasks of forecasting, decision making, model evaluation and learning can be considered as parts of the same interactive and iterative process; thus paving the way for establishing the foundation of “real time econometrics”. This paper attempts to provide an overview of some of these developments.history of econometrics, microeconometrics, macroeconometrics, Bayesian econometrics, nonparametric and semi-parametric analysis

Cleaning Up the Kitchen Sink: Growth Empirics When the World Is Not Simple

This paper explores the relevance of unknown nonlinearities for growth empirics. Recent theoretical contributions and case-study evidence suggest that nonlinearities are pervasive in the growth process. I show that the postwar data provide strong evidence in favor of generalized non-linearities. I provide two alternative mechanisms for making inference about the effects of production-function shifters on growth that do not make a priori assumptions about functional form: monotonicity tests and average derivative estimation. The results of these tests point towards a greater role for structural variables and a smaller role for policy variables than the linear model.Economic Growth, Cross-Country Growth Regressions, Non-linearities, Non-parametric econometrics

Boosting Techniques for Nonlinear Time Series Models

Many of the popular nonlinear time series models require a priori the choice of parametric functions which are assumed to be appropriate in specific applications. This approach is used mainly in financial applications, when sufficient knowledge is available about the nonlinear structure between the covariates and the response. One principal strategy to investigate a broader class on nonlinear time series is the Nonlinear Additive AutoRegressive (NAAR) model. The NAAR model estimates the lags of a time series as flexible functions in order to detect non-monotone relationships between current observations and past values. We consider linear and additive models for identifying nonlinear relationships. A componentwise boosting algorithm is applied to simultaneous model fitting, variable selection, and model choice. Thus, with the application of boosting for fitting potentially nonlinear models we address the major issues in time series modelling: lag selection and nonlinearity. By means of simulation we compare the outcomes of boosting to the outcomes obtained through alternative nonparametric methods. Boosting shows an overall strong performance in terms of precise estimations of highly nonlinear lag functions. The forecasting potential of boosting is examined on real data where the target variable is the German industrial production (IP). In order to improve the model's forecasting quality we include additional exogenous variables. Thus we address the second major aspect in this paper which concerns the issue of high-dimensionality in models. Allowing additional inputs in the model extends the NAAR model to an even broader class of models, namely the NAARX model. We show that boosting can cope with large models which have many covariates compared to the number of observations

Employee Heterogeneity and Within-Firm Experience-Earnings Profiles: A Nonparametric Analysis

Abstract Motivated by a priori uncertainty with respect to the parametric specification of the earnings function, I model the earnings function as semiparametric partially linear model and follow the estimation approach described in Robinson (1988). Using data from the personnel records of a large major UK based financial sector employer, I let years of within-firm and pre-firm experience form the nonparametrically modelled component of the earnings function. It is shown that the estimated within-firm experience earnings profiles, which are conditional upon a given number years of pre-firm experience accumulated before entry, converge and even overtake as years of pre-firm experience increases. This result can be explained with the recognition of unobservable explanatory variables, such as the match and individual quality of the employees, both of which are a function of years of within- and pre-firm experience and wages