Econometric Literature Research Articles

The Impact of the EU Emissions Trading System on Competitiveness and Carbon Leakage

Concerns about the potential negative effects on domestic firms’ international competitiveness and ensuing carbon leakage are the main obstacle to the unilateral use of carbon pricing for reducing greenhouse gas emissions. Since 2005, the EU Emissions Trading System (EU ETS) has put a price on about half of the EU’s overall emissions and presently remains the largest cap-and-trade scheme in the world. Monitoring the competitiveness effects of the EU ETS is vital for evaluating the cost effectiveness of the policy instrument and for amending it if needed. This paper surveys the econometric literature that tests for the occurrence of competitiveness effects and carbon leakage under the EU ETS. Emphasised is the specific or more general relevance of estimation results for sectors, countries and years. Organised in this way, the empirical evidence may better inform policy. The review also covers several studies analysing the competitiveness effects of the EU ETS through the lens of stock markets. By far, the most frequently encountered conclusion is that no evidence was found of negative statistically significant effects on firms’ competitiveness (nor, therefore, of carbon leakage). Among the few adverse effects, those concerning increased FDI activity in certain regulated sectors deserve special consideration.

Conditional Heteroskedasticity in Crypto-Asset Returns

In a recent contribution to the financial econometrics literature, Chu et al. (2017) provide the first examination of the time-series price behaviour of the most popular cryptocurrencies. However, insufficient attention was paid to correctly diagnosing the distribution of GARCH innovations. When these data issues are controlled for, their results lack robustness and may lead to either underestimation or overestimation of future risks. The main aim of this paper therefore is to provide an improved econometric specification. Particular attention is paid to correctly diagnosing the distribution of GARCH innovations by means of Kolmogorov type non-parametric tests and Khmaladze's martingale transformation. Numerical computation is carried out by implementing a Gauss-Kronrod quadrature. Parameters of GARCH models are estimated using maximum likelihood. For calculating P-values, the parametric bootstrap method is used. Further reference is made to the merits and demerits of statistical techniques presented in the related and recently published literature.

English

In a recent contribution to the financial econometrics literature, Chu et al. (2017) provide the first examination of the time-series price behaviour of the most popular cryptocurrencies. However, insufficient attention was paid to correctly diagnosing the distribution of GARCH innovations. When these data issues are controlled for, their results lack robustness and may lead to either underestimation or overestimation of future risks. The main aim of this paper therefore is to provide an improved econometric specification. Particular attention is paid to correctly diagnosing the distribution of GARCH innovations by means of Kolmogorov type non-parametric tests and Khmaladze's martingale transformation. Numerical computation is carried out by implementing a Gauss-Kronrod quadrature. Parameters of GARCH models are estimated using maximum likelihood. For calculating P-values, the parametric bootstrap method is used. Further reference is made to the merits and demerits of statistical techniques presented in the related and recently published literature.

The ET Interview: Professor Hashem Pesaran

Since graduating with a PhD in Economics from Cambridge, Hashem Pesaran has been a prominent contributor to the theoretical econometrics literature, undertaking work on testing of non-nested models, cointegration, forecasting in the presence of model instability, forecasting of multinomial data, and estimation and testing of time-series and panel data models. His work also covers several areas of applied econometrics, including studies of oil prices, prices in financial markets, rational expectations and economic surveys, analysis of panel data models with cross sectional dependence, and spillover effects and interdependencies across international economies. Hashem is the founding editor of the Journal of Applied Econometrics and has served on the editorial boards of numerous journals in economics and econometrics. He is a Fellow of the British Academy, the Econometric Society, and the Journal of Econometrics. Previously, he was head of the Economic Research Department of the Central Bank of Iran and Under-Secretary of the Ministry of Education, Iran. He has held positions as a Professor of Economics at Cambridge University and at UCLA. He is currently an emeritus Professor of Economics at Cambridge University, a life time Fellow of Trinity College, Cambridge, and holds the John Elliott Distinguished Chair in Economics at the University of Southern California.

Fixed and Long Time Span Jump Tests: New Monte Carlo and Empirical Evidence

Numerous tests designed to detect realized jumps over a fixed time span have been proposed and extensively studied in the financial econometrics literature. These tests differ from “long time span tests” that detect jumps by examining the magnitude of the intensity parameter in the data generating process, and which are consistent. In this paper, long span tests, including the tests of Corradi et al. (2018) (called CSS tests), are compared and contrasted with a variety of fixed span tests, including the ASJ test of A¨it-Sahalia and Jacod (2009), the BNS test of Barndorff-Nielsen and Shephard (2006), and the PZ test of Podolskij and Ziggel (2010), in an extensive series of Monte Carlo experiments. The long span tests that we examine are consistent against the null hypothesis of zero jump intensity, while the fixed span tests are not designed to detect jumps in the data generating process, and instead detect realized jumps over a fixed time span. It is found that both the ASJ and CSS tests exhibit reasonably good finite sample properties, for time spans both short and long. The other tests suffer from finite sample distortions, both under sequential testing (as is well known) and under long time spans. The latter finding is new, and confirms the “pitfall” discussed in Huang and Tauchen (2005), of using asymptotic approximations associated with finite time span tests in order to study long time spans of data. An extensive empirical analysis is carried out to investigate the implications of these findings. In particular, when applied to stock price and stock index data, “time-span robust” tests indicate that the prevalence of jumps is not as universal as might be expected. Various sector ETFs and individual stocks, for example, appear to exhibit no jumping behavior during a number of quarterly and annual periods.

Current status linear regression

We construct $\sqrt{n}$-consistent and asymptotically normal estimates for the finite dimensional regression parameter in the current status linear regression model, which do not require any smoothing device and are based on maximum likelihood estimates (MLEs) of the infinite dimensional parameter. We also construct estimates, again only based on these MLEs, which are arbitrarily close to efficient estimates, if the generalized Fisher information is finite. This type of efficiency is also derived under minimal conditions for estimates based on smooth nonmonotone plug-in estimates of the distribution function. Algorithms for computing the estimates and for selecting the bandwidth of the smooth estimates with a bootstrap method are provided. The connection with results in the econometric literature is also pointed out.

Volatility Spillovers between Energy and Agricultural Markets: A Critical Appraisal of Theory and Practice

Energy and agricultural commodities and markets have been examined extensively, albeit separately, for a number of years. In the energy literature, the returns, volatility and volatility spillovers (namely, the delayed effect of a returns shock in one asset on the subsequent volatility or covolatility in another asset), among alternative energy commodities, such as oil, gasoline and ethanol across different markets, have been analysed using a variety of univariate and multivariate models, estimation techniques, data sets, and time frequencies. A similar comment applies to the separate theoretical and empirical analysis of a wide range of agricultural commodities and markets. Given the recent interest and emphasis in bio-fuels and green energy, especially bio-ethanol, which is derived from a range of agricultural products, it is not surprising that there is a topical and developing literature on the spillovers between energy and agricultural markets. Modelling and testing spillovers between the energy and agricultural markets has typically been based on estimating multivariate conditional volatility models, specifically the Baba, Engle, Kraft, and Kroner (BEKK) and dynamic conditional correlation (DCC) models. A serious technical deficiency is that the Quasi-Maximum Likelihood Estimates (QMLE) of a Full BEKK matrix, which is typically estimated in examining volatility spillover effects, has no asymptotic properties, except by assumption, so that no valid statistical test of volatility spillovers is possible. Some papers in the literature have used the DCC model to test for volatility spillovers. However, it is well known in the financial econometrics literature that the DCC model has no regularity conditions, and that the QMLE of the parameters of DCC has no asymptotic properties, so that there is no valid statistical testing of volatility spillovers. The purpose of the paper is to evaluate the theory and practice in testing for volatility spillovers between energy and agricultural markets using the multivariate Full BEKK and DCC models, and to make recommendations as to how such spillovers might be tested using valid statistical techniques. Three new definitions of volatility and covolatility spillovers are given, and the different models used in empirical applications are evaluated in terms of the new definitions and statistical criteria.

Does Systematic Sampling Preserve Granger Causality with an Application to High Frequency Financial Data?

In applied econometric literature, the causal inferences are often made based on temporally aggregated or systematically sampled data. A number of studies document that temporal aggregation has distorting effects on causal inference and systematic sampling of stationary variables preserves the direction of causality. Contrary to the stationary case, this paper shows for the bivariate VAR(1) system that systematic sampling induces spurious bi-directional Granger causality among the variables if the uni-directional causality runs from a non-stationary series to either a stationary or a non-stationary series. An empirical exercise illustrates the relative usefulness of the results further.

Linear Dynamic Panel-data Estimation Using Maximum Likelihood and Structural Equation Modeling

Panel data make it possible both to control for unobserved confounders and to include lagged, endogenous regressors. However, trying to do both simultaneously leads to serious estimation difficulties. In the econometric literature, these problems have been addressed by using lagged instrumental variables together with the generalized method of moments, while in sociology the same problems have been dealt with using maximum likelihood estimation and structural equation modeling. While both approaches have merit, we show that the maximum likelihood–structural equation models method is substantially more efficient than the generalized method of moments method when the normality assumption is met and that the former also suffers less from finite sample biases. We introduce the command xtdpdml, which has syntax similar to other Stata commands for linear dynamic panel-data estimation. xtdpdml greatly simplifies the structural equation model specification process; makes it possible to test and relax many of the constraints that are typically embodied in dynamic panel models; allows one to include time-invariant variables in the model, unlike most related methods; and takes advantage of Stata's ability to use full-information maximum likelihood for dealing with missing data. The strengths and advantages of xtdpdml are illustrated via examples from both economics and sociology.

Estimating and Forecasting Conditional Risk Measures with Extreme Value Theory: A Review

One of the key components of financial risk management is risk measurement. This typically requires modeling, estimating and forecasting tail-related quantities of the asset returns’ conditional distribution. Recent advances in the financial econometrics literature have developed several models based on Extreme Value Theory (EVT) to carry out these tasks. The purpose of this paper is to review these methods.

THE DEMAND FOR LIQUID ASSETS: EVIDENCE FROM THE MINFLEX LAURENT DEMAND SYSTEM WITH CONDITIONALLY HETEROSKEDASTIC ERRORS

We investigate the demand for money and the degree of substitutability among monetary assets in the United States using the generalized Leontief and the Minflex Laurent (ML) models as suggested by Serletis and Shahmoradi (2007). In doing so, we merge the demand systems literature with the recent financial econometrics literature, relaxing the homoskedasticity assumption and instead assuming that the covariance matrix of the errors of flexible demand systems is time-varying. We also pay explicit attention to theoretical regularity, treating the curvature property as a maintained hypothesis. Our findings indicate that only the curvature constrained ML model with a Baba, Engle, Kraft, and Kroner (BEKK) specification for the conditional covariance matrix is able to generate inference consistent with theoretical regularity.

Instrumental variable estimation of factor models with possibly many variables

ABSTRACTIn this paper, we consider the instrumental variables (IV) estimation of factor models. In the psychometrics literature, although the two-stage least squares (2SLS) estimator is routinely used in IV estimation of factor models, alternative estimators have been proposed in the econometrics literature. Therefore, in this paper, we compare the performance of these alternative IV estimators in the context of factor models. Monte Carlo simulation results reveal that the HLIM/HFUL estimator by Hausman et al. (2012) outperforms the 2SLS estimator and performs best in many cases.

Instrumental Variable Estimation of Factor Models with Possibly Many Variables

In this paper, we consider the instrumental variables (IV) estimation of factor models. In the psychometrics literature, although the two-stage least squares (2SLS) estimator is routinely used in IV estimation of factor models, alternative estimators have been proposed in the econometrics literature. Therefore, in this paper, we compare the performance of these alternative IV estimators in the context of factor models. Monte Carlo simulation results reveal that the HLIM/HFUL estimator by Hausman, Newey, Woutesen, Chao and Swanson (2012) outperforms the 2SLS estimator and performs best in many cases.

Improving Quantile Forecast Accuracy: An Application to Oil Market

Recent literature in financial econometrics literature has witnessed the growing interest of quantile forecast with the application to risk management. However, the global financial crisis has shown that existing approaches to forecasting quantiles, especially Value-at-Risk, are flawed. Nevertheless, the continuing importance of VaR in risk management still demands the methods that can improve quantile forecast accuracy. Contributing to literature, this paper works on the WTI Crude Oil Futures and finds that the use of the realized volatility-based models and quantile combination can enhance the accuracy of quantile forecasts.

Joint Distribution of Forecasts and Outcomes: Impact of Non-Normality on the Measurement of Forecasting Skill, with Applications to Analysts’ Target Prices

The purpose of this paper is to provide a detailed analysis of the joint distribution of forecasts and outcomes in the context of financial forecasting. We use Edgeworth expansions to model this joint distribution. In turn this allows us to assess the impact of non-normality, either in forecasts, or outcomes or both. This leads to multiple results; we can deduce the distribution of the forecast error; we can analyse the properties of the hit rate (H), as a statistical concept in its own right, and also in its relation to the information coefficient (IC); two tools that are used in the assessment of forecasting ability by active fund managers and financial analysts. Our paper contributes to the recent econometric literature on directional forecasting as well as the empirical literature that examines analyst performance. We find that the close link between H and IC under normality breaks down in the more general case. We provide further evidence on the richness of this approach by looking at simulation and empirical evidence.

Misclassification in travel surveys and implications to choice modeling: application to household auto ownership decisions

Travel surveys that elicit responses to questions regarding daily activity and travel choices form the basis for most of the transportation planning and policy analysis. The response variables collected in these surveys are prone to errors leading to mismeasurement or misclassification. Standard modeling methods that ignore these errors while modeling travel choices can lead to biased parameter estimates. In this study, methods available in the econometrics literature were used to quantify and assess the impact of misclassification errors in auto ownership choice data. The results uncovered significant misclassification rates ranging from 1 to 40% for different auto ownership alternatives. Also, the results from latent class models provide evidence for variation in misclassification probabilities across different population segments. Models that ignore misclassification were not only found to have lower statistical fit but also significantly different elasticity effects for choice alternatives with high misclassification probabilities. The methods developed in this study can be extended to analyze misclassification in several response variables (e.g., mode choice, activity purpose, trip/tour frequency, and mileage) that constitute the core of advanced travel demand models including tour and activity-based models.

A Quantile Approach to the Estimation of First-Price Private Value Auction

The first-price auction model has been intensively studied in the econometric literature. Probably one of the best well-known papers in this field is the one by Guerre et al. (2000) where the authors show the nonparametric identification of the distribution of the private values within the independent private values paradigm and provide a two-steps estimation method. In our paper, we treat the same problems of identification and estimation but while using a quantile approach. The first-price auction gives rise to a complex nonlinear inverse problem. Although the information contained in the quantile functions is the same as the information contained in the cumulative distribution functions, the use of quantiles leads to a linearization of the problem. By contrast with previous approaches used in the literature of first-price auctions, within this framework, we are able to find a closed-form solution for the quantile of the private values. Therefore we obtain a constructive identification (see Matzkin (2013)) that allow for a one stage estimation procedure. The estimation is performed using three regularization methods: Tikhonov regularization, Landweber-Friedman regularization and estimation by kernels. We discuss as well the asymptotic properties of these estimators.

Unemployment and econometric learning

We apply well-known results of the econometric learning literature to the Mortensen and Pissarides real business cycle model. Agents can always learn the unique rational expectations equilibrium (REE), for all possible well-defined sets of parameter values, by using the minimum-state-variable solution to the model and decreasing gain learning. From this perspective the assumption of rational expectations in the model could be seen as reasonable. But using a parametrisation with UK data, simulations show that the speed of convergence to the REE is slow. This type of learning dampens the cyclical response of unemployment to small structural shocks.

Oil Price Shocks and Economic Growth in the US

We apply both conventional and spatial techniques to panel data for US states to examine the effects of plausibly exogenous oil price shocks on economic growth. Contrary to the oil curse claims, we find that oil price shocks have numerically moderate but highly statistically significant positive direct effects on growth while the indirect effects are insignificant. We also find that positive impact of oil occurs only in states with a high value of the economic freedom index. In a technical contribution to the spatial econometrics literature we propose a procedure for estimating marginal effects of oil price shocks in a model with interaction terms. In addition, we show that the cumulative direct oil price effects on economic growth are persistent over time.

A Century of Interfuel Substitution

We investigate the demand for energy and the degree of substitutability among fossil fuels in the United States using the Normalized Quadratic (NQ) expenditure function and (to our knowledge) the longest span prices and quantities that have ever been studied before, from 1919 to 2012. In doing so, we merge the empirical energy demand systems literature with the recent financial econometrics literature, relaxing the homoskedasticity assumption and instead assuming that the covariance matrix of the errors of the flexible demand system is time-varying. We generate inference, in terms of a full set of elasticities, consistent with neoclassical microeconomic theory and the data generating process. Our results are in line with earlier findings in the literature based on shorter duration samples and different methodologies. They have important implications for climate policy intervention designed to reduce greenhouse gas emissions. We show that there is a small but statistically significant substitution possibility between crude oil and natural gas, but not between crude oil and coal. We also provide evidence that natural gas is a substitute for coal when the price of coal changes, but coal is not a substitute for natural gas when the price of natural gas changes.