Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

The Statistics of Canada

Energy is vital for sustainable development of any nation – be it social, economic or environment. In the past decade energy consumption has increased exponentially globally. Energy management is crucial for the future economic prosperity and environmental security. Energy is linked to industrial production, agricultural output, health, access to water, population, education, quality of life, etc. Energy demand management is required for proper allocation of the available resources. During the last decade several new techniques are being used for energy demand management to accurately predict the future energy needs. In this paper an attempt is made to review the various energy demand forecasting models. Traditional methods such as time series, regression, econometric, ARIMA as well as soft computing techniques such as fuzzy logic, genetic algorithm, and neural networks are being extensively used for demand side management. Support vector regression, ant colony and particle swarm optimization are new techniques being adopted for energy demand forecasting. Bottom up models such as MARKAL and LEAP are also being used at the national and regional level for energy demand management.

Energy models for demand forecasting—A review

This article extends the recent findings of Liu (2005), Ang (2007), Apergis et al. (2009) and Payne (2010) by implementing recent bootstrap panel unit root tests and cointegration techniques to investigate the relationship between carbon dioxide emissions, energy consumption, and real GDP for 12 Middle East and North African Countries (MENA) over the period 1981–2005. Our results show that in the long-run energy consumption has a positive significant impact on CO2 emissions. More interestingly, we show that real GDP exhibits a quadratic relationship with CO2 emissions for the region as a whole. However, although the estimated long-run coefficients of income and its square satisfy the EKC hypothesis in most studied countries, the turning points are very low in some cases and very high in other cases, hence providing poor evidence in support of the EKC hypothesis. Thus, our findings suggest that not all MENA countries need to sacrifice economic growth to decrease their emission levels as they may achieve CO2 emissions reduction via energy conservation without negative long-run effects on economic growth.

https://papers.ssrn.com/sol3/Delivery.cfm/dp6412.pdf?abstractid=2028212&mirid=1

Energy Consumption, Economic Growth and CO2 Emissions in Middle East and North African Countries

https://www.econstor.eu/bitstream/10419/58503/1/715714074.pdf

Energy consumption, economic growth and CO2 emissions in Middle East and North African countries $

A pseudo-panel data model of household electricity demand

In this paper we use a micro approach to model Quebec residential demand for electricity The model incorporates a joint continuous/discrete decision framework which allows for interrelationships between decisions on electricity-related durable holdings and those on usage In the spirit of recent studies, which used a continuous/discrete frame- work, the model parameters are estimated using a two-stage approach At the first stage the decisions regarding space and water heating systems are modelled with a very flexible Multinomial Probit (MNP) framework Then, at the second stage, the demand for electricity conditional on the chosen heating system is estimated using ordinary least squares, and a correction is applied in order to eliminate a potential estimation bias The estimated short-run and long-run price and income elasticities have the correct signs and are rather small, as is expected under such circumstances

Quebec residential electricity demand: a microeconometric approach

http://crde.umontreal.ca/cahiers/08-2001-cah.pdf

Simulation-Based Finite-Sample Tests for Heteroskedasticity and ARCH Effects.

Fluctuations in the prices of various natural resource products are of concern in both policy and business circles; hence, it is important to develop accurate price forecasts.

/pdf/forecasting-commodity-prices-garch-jumps-and-mean-reversion-1vc2j2ahgo.pdf

Forecasting Commodity Prices: GARCH, Jumps, and Mean Reversion

We consider an empirical estimation of the environmental Kuznets curve (EKC) for carbon dioxide and sulphur, with a focus on confidence set estimation of the tipping point. Various econometric—parametric and nonparametric—methods are considered, reflecting the implications of persistence, endogeneity, the necessity of breaking down our panel regionally, trends and temporal instability, and the small number of countries within each panel. In particular, we propose a parametric inference method that corrects for potential weak-identification of the tipping point. Weak identification may occur if the true EKC is linear while a quadratic income term is nevertheless imposed into the estimated equation. Relevant literature to date confirms that non-linearity of the EKC is indeed not granted, which provides the motivation for our work. We also propose a non-parametric counterpart to the parametric confidence set, for sensitivity analysis. Viewed collectively, our results confirm an inverted U-shaped EKC in the OECD countries but generally not elsewhere, although a local-pollutant analysis suggests favorable exceptions beyond the OECD. Our measures of uncertainty confirm that it is difficult to identify economically plausible tipping points. Policy-relevant estimates of the tipping point can nevertheless be recovered from a local-pollutant long-run or non-parametric perspective.

/pdf/the-environmental-kuznets-curve-tipping-points-uncertainty-247cadycub.pdf

Jean-Thomas Bernard

Papers

A pseudo-panel data model of household electricity demand

Quebec residential electricity demand: a microeconometric approach

Simulation-Based Finite-Sample Tests for Heteroskedasticity and ARCH Effects.

Forecasting Commodity Prices: GARCH, Jumps, and Mean Reversion

The Environmental Kuznets Curve: Tipping Points, Uncertainty and Weak Identification