Discriminative conditional restricted Boltzmann machine for discrete choice and latent variable modelling

Conventional methods of estimating latent behaviour generally use attitudinal questions which are subjective and these survey questions may not always be available. We hypothesize that an alternative approach can be used for latent variable estimation through an undirected graphical models. For instance, non-parametric artificial neural networks. In this study, we explore the use of generative non-parametric modelling methods to estimate latent variables from prior choice distribution without the conventional use of measurement indicators. A restricted Boltzmann machine is used to represent latent behaviour factors by analyzing the relationship information between the observed choices and explanatory variables. The algorithm is adapted for latent behaviour analysis in discrete choice scenario and we use a graphical approach to evaluate and understand the semantic meaning from estimated parameter vector values. We illustrate our methodology on a financial instrument choice dataset and perform statistical analysis on parameter sensitivity and stability. Our findings show that through non-parametric statistical tests, we can extract useful latent information on the behaviour of latent constructs through machine learning methods and present strong and significant influence on the choice process. Furthermore, our modelling framework shows robustness in input variability through sampling and validation

Specification and estimation of spatial econometric models : A discussion of alternative strategies for spatial economic modelling

The semantical insufficiency of (spatial) economic theories necessitates the making of additional assumptions — thereby introducing substantial specification uncertainty — in order to arrive at a fully specified econometric model. The traditional or current approach to econometric modelling treats specification uncertainty inadequately. This proposition is illustrated by two well-known examples from the spatial economic literature. Two alternative specification strategies for spatial economic modelling — designed to improve the current spatial econometric modelling approach — are proposed. One of these strategies is used for a specification analysis of agricultural output in Eire

Combining Revealed and Stated Preference Data to Estimate the Nonmarket Value of Ecological Services: An Assessment of the State of the Science

This paper reviews the marketing, transportation, and environmental economics literature on the joint estimation of revealed and stated preference data. The revealed preference and stated preference approaches are first described with a focus on the strengths and weaknesses of each. Recognizing these strengths and weaknesses, the potential gains from combining data are described. A classification system for combined data that emphasizes the type of data combination and the econometric models used is proposed. A methodological review of the literature is pursued based on this classification system. Examples from the environmental economics literature are highlighted. A discussion of the advantages and disadvantages of each type of jointly estimated model is then presented. Suggestions for future research, in particular opportunities for application of these models to environmental quality valuation, are presented.Nonmarket Valuation, Revealed Preference, Stated Preference

Multifactor Productivity and its Determinants: Al Empirical Analysis for Mexican Manufacturing.

We use data from the Annual Industrial Survey for 1996-2003. First, we estimate production functions by means of growth accounting exercises and panel data econometrics for the whole sector and for 14 comprehensive groups. Various measures of Multifactor Productivity (MFP) are constructed, as we consider diverse combinations of inputs with capital, labour, electricity and transport. This allows us to compare MFP growth rates between groups. Second, we analyse econometrically some of the determinants of MFP and Labour Productivity (LP) growth. We find that, on the one hand, there is some evidence of a positive relationship between market concentration and technology adoption; on the other hand, both technology adoption and human capital seem to be promoting productivity, whilst market concentration is exerting a negative influence on it. In sum, our results suggest that, once controlling for the effect on technology adoption, more concentration (conversely, less competition) has a negative impact on productivity.Panel data, Productivity, Manufacturing, Competition

Energy consumption in the US reconsidered. Evidence across sources and economic sectors

This study analyzes the impact of GDP shocks in USA on primary energy consumption and the reverse impact in a comprehensive and novel framework, distinguishing by economic sectors (commercial, industrial, residential and transportation) and energy source, i.e., total fossil (coal, natural gas and petroleum), nuclear, and renewable (hydroelectric, geothermal and biomass) for the period 1973:1 to 2015:2. To this end, we apply Granger causality analysis through the Hatemi-J [1] and Toda and Yamamoto [2] approaches from a time series perspective to evaluate the existence of asymmetries on this bidirectional relationship. The empirical results suggest that the impact of GDP on primary energy consumption is heterogeneous and energy source-specific, and an asymmetric behavior appears among cycles. Moreover, it seems clear that the US economy is highly dependent on petroleum energy consumption. The renewable energy sources do not seem to show any relationshipsources seem to show no relationship with economic growth, and finally, our results suggest that energy consumption in the industrial sector is key to economic growth and is also very sensitive to negative economic shocks

The Scientific Contributions of James Heckman and Daniel McFadden

Advanced Information. Microeconometric research is concerned with empirical analysis of the economic behavior of individuals and households, such as decisions on labor supply, consumption, migration or occupational choice. Microeconometric methods are equally relevant in studies of individual firms, for example their production and employment decisions. Over the last several decades, significant breakthroughs in empirical microeconomic research have been triggered by innovations in microeconometric methods and by greater availability of new types of data. The raw material in microeconometric research is microdata, where the units of observation are individuals, households or firms. Microdata appear as cross-section data and, to an increasing degree, as longitudinal (panel) data.Microeconometrics