Recent Developments In Econometrics Research Articles

Forecast performance of noncausal autoregressions and the importance of unit root pretesting

AbstractBased on a large simulation study, this paper investigates which strategy to adopt in order to choose the most accurate forecasting model for mixed causal‐noncausal autoregressions (MAR) data generating processes: always differencing (D), never differencing (L), or unit root pretesting (P). Relying on recent econometric developments regarding forecasting and unit root testing in the MAR framework, the main results suggest that from a practitioner's point of view, the P strategy at the 10% level is a good compromise. In fact, it never departs too much from the best model in terms of forecast accuracy, unlike the L (respectively, D) strategy when the DGP becomes very persistent (respectively, less persistent). This approach is illustrated using recent monthly Brent crude oil price data.

Econometric Advances in the Estimation of Housing Price Determinants

This paper aims to help improve the estimations produced by researchers who rely on conventional housing market pricing models to determine housing prices. The widespread use of panel data in estimating housing prices is justified by the richness of cross-sectional regional or metropolitan data analysed over several periods. Unfortunately, panel data has slope coefficient heterogeneity and cross-sectional dependence, producing inconsistent and misleading estimates of the coefficients using the Ordinary Least Squares (OLS) estimator. Recent advances in econometrics address these panel data limitations, producing better estimates. We analysed the empirical application of these new estimators on housing market panel data, showing that the Fully Modified Ordinary Least Squares Augmented Mean Group (FMOLS-MG) estimator produces the best estimates of the long-term housing market equilibrium and that the Dynamic Common Correlated Effects Mean Group (DCCE-MG) estimator produces the best estimates of the housing market's short-term dynamics. Adopting a trending methodology like Difference-in-Differences (DID) in housing market research to explain the effects of policy decisions on housing prices also has complications related to using the OLS estimator with fixed effects when the data has serial correlation. We show these problems can be overcome using the Feasible Generalised Least Squares estimator in a Seemingly Unrelated Regression Equations (FGLS-SURE) system. Recent econometric developments produce more accurate housing price determinant estimates than conventional econometric methods. These new methodologies can help researchers better estimate the effects of fundamental economic changes and policy decisions on housing prices, which can, in turn, support policymakers in implementing better housing policies.

The subjective treatment effects of COVID-19 on child well-being: evidence from Luxembourg

Estimating the impact of COVID-19 on the multiple dimensions of child well-being requires quasi-random variation in exposure to it, which is unlikely to occur during a pandemic. Recent developments in econometrics have highlighted the relevance of subjective evaluations of treatment effects in the absence of randomization. This paper delivers new evidence, based on primary data collected in Luxembourg in Spring 2021 about their subjective appraisal of the effects of COVID-19 on multiple dimensions of children’s well-being. Effects are recovered through specific survey questions, asking children to compare actual outcomes with counterfactual ones, that they believe would have occurred in the absence of COVID-19. Children report negative effects of COVID-19 on subjective health and on schooling outcomes, as well as disruptions on the time used to interact with the family. The paper explores the sources of heterogeneity behind these results.

Racial/Ethnic Health Disparity in the U.S.: A Decomposition Analysis

Following recent econometric developments, we use self-assessed general health on a Likert scale conditioned by several objective determinants to measure health disparity between non-Hispanic Whites and minority groups in the United States. A statistical decomposition analysis is conducted to determine the contributions of socio-demographic and neighborhood characteristics in generating disparities. Whereas, 72% of health disparity between Whites and Blacks is attributable to Blacks’ relatively worse socio-economic and demographic characteristics, it is only 50% for Hispanics and 65% for American Indian Alaska Natives. The role of a number of factors including per capita income and income inequality vary across the groups. Interestingly, “blackness” of a county is associated with better health for all minority groups, but it affects Whites negatively. Our findings suggest that public health initiatives to eliminate health disparity should be targeted differently for different racial/ethnic groups by focusing on the most vulnerable within each group.

Warning: Bilateral trade agreements do not create trade

ABSTRACTBoth bilateral and plurilateral trade agreements have grown in number over the past three decades, but their potential impact on trade may be different. This paper sheds light on the ex post (partial) effect of bilateral versus plurilateral trade agreements using the most recent econometric developments for the gravity equation on a dataset that covers 211 countries and territories over the period 1948–2013. Our results show that plurilateral trade agreements have significantly increased trade among trading partners, while this has not been the case for bilateral trade agreements.

Some tendencies in modern econometrics

All analytical subjects, probably all scholarly fields of investigation, for the matter, should be exploring new avenues of research and seeking new findings. In economics, generally, there is an explosion of research activity, associated with new information, new devices, new scholarly journals, and new professional opportunities. Econometrics, which greatly expanded as a sub-dis? cipline of economics in the second half of this century, is presently branching out in many directions, some of which appear to be fruitful and some of which do not. There are now several books and articles on the historical development of econometrics from sparse early roots in the 19th century and much more significant growth activity in the 20th century. It is worth while to reconsider some earlier research, in relation to recent developments. The subject grew quite naturally as formal economics and mathematical statistics expanded, but a breakthrough occurred that came to be known as the 'Haavelmo Revolution'. A manuscript authored by Trygve Haavelmo circulated among academic economists and statisticians in the late 1930s and early 1940s; it was published as a supplement to Econometrica in 19441. An important interpretation of Haavelmo's contribution, viewed largely from the side of mathematical statistics, has been published by Theodore W. Anderson on the occasion of the award of the prize in memory of Alfred Nobel to Haavelmo in 1989.2 In order to assess some recent developments in econometrics, I want to emphasize a few important aspects of Haavelmo's contribution, which indeed has many facets. Essentially, Haavelmo placed econometric methodology in the midst of developments that were taking place, or had already taken place, in statistical inference. Statistics in economics, which was already well established, but under development at the time of Haavelmo's work, was largely concenrned with descriptive statistics. The main problems were those of describing the economy in quantitative terms. Haavelmo's approach showed how to relate mathematical specifications of economic relationships to probability interpretations of statistical estimates of the specified relationships. All econometrics does not deal with the estimation of behavioural relationships or economic decision-making, but a large portion of the discipline is involved in analyzing economic behaviour, and this forms the focus for the present discussion. To show how probability and economic relationships are involved together, let us consider the fundamental, but general, model that formed the basis for Haavelmo's approach.

Does the GATT/WTO promote trade? After all, Rose was right

This paper re-examines the effect of the GATT/WTO on trade using recent econometric developments that allow us estimating structural gravity equations with the Poisson pseudo-maximum likelihood (PPML) estimator on a large dataset that requires computing high-dimensional fixed effects. By doing so, we overcome computational limitations that are present in previous studies. In line with Rose’s (Am Econ Rev 94:98–114, 2004) seminal work, we find that, unlike regional trade agreements and currency unions, the GATT/WTO accession has not generated positive trade effects. This result is robust to the use of alternative measures of trade flows, across periods and country groups, to changes in the periodicity of the data, when taking into account the GATT/WTO accession dynamics, to controlling for the participation of only one country of the dyad in GATT/WTO, to the consideration of non-member participants, and to the use of alternative datasets. Notwithstanding, we also find that PPML results are sensitive to the definition of the dependent variable.

Reconciling the Porter hypothesis with the traditional paradigm about environmental regulation: a nonparametric approach

This paper estimates the impact of pollution abatement investments on the production technology of firms by pursuing two new directions. First, we take advantage of recent econometric developments in productivity, efficiency analysis and nonparametric kernel regression by adopting a conditional nonparametric frontier analysis. Second, we focus not only on the average effect but also search for potential nonlinearities. We provide new results suggesting that pollution abatement capital affects with a bell-shaped fashion technological catch-up (inefficiency distribution) and does not affect technological change (shifts in the frontier). These results have relevant implications both for modeling and for the purposes of advice on environmentally friendly policy.

Bayesian inference of the fractional Ornstein–Uhlenbeck process under a flow sampling scheme

Using recent developments in econometrics and computational statistics we consider the estimation of the fractional Ornstein–Uhlenbeck process under a flow sampling scheme. To address the problem, we adopt throughout the paper an exact discretization approach. A flow sampling scheme arises, for example, naturally in modelling asset prices in continuous time since the time integral over successive observations defines the observable increments of asset log-prices. Exact discretization delivers an ARIMA(1,1,1) model for log-prices with a fractional driving noise. Building on the resulting exact discretization formulae and covariance function, a new Markov Chain Monte Carlo scheme is proposed and apply it to investigate the properties of both the time and frequency domain likelihoods/posteriors. For the exact discrete model, we adopt a general sampling interval of length h. This allows us to determine the optimal choice of h independent of the sample size. To illustrate the methods, with no ambition to a comprehensive data analysis, we use high frequency stock price data showing the relevance of aggregation over time issues in modelling asset prices.

Testing the validity of instruments in an exactly identified equation

Whenever the number of instruments equals the number of endogenous variables testing the validity of the instruments is not a feasible task. Based on recent econometric developments (Lewbel, 2012), this paper aims at spreading among applied economists and econometricians an empirical strategy to overcome this setback, thus sidestepping the exact identification problem.

The State of Applied Econometrics: Causality and Policy Evaluation

In this paper, we discuss recent developments in econometrics that we view as important for empirical researchers working on policy evaluation questions. We focus on three main areas, in each case, highlighting recommendations for applied work. First, we discuss new research on identification strategies in program evaluation, with particular focus on synthetic control methods, regression discontinuity, external validity, and the causal interpretation of regression methods. Second, we discuss various forms of supplementary analyses, including placebo analyses as well as sensitivity and robustness analyses, intended to make the identification strategies more credible. Third, we discuss some implications of recent advances in machine learning methods for causal effects, including methods to adjust for differences between treated and control units in high-dimensional settings, and methods for identifying and estimating heterogenous treatment effects.

International Real Estate Review

This paper revisits the relationships among macroeconomic variables and asset returns. Based on recent developments in econometrics, we categorize competing models of asset returns into different "Equivalence Predictive Power Classes" (EPPCs). During the pre-crisis period (1975-2005), some models emphasize that imperfect capital markets outperform an AR(1) for the forecast of housing returns. After 2006, a model that includes both an external finance premium (EFP) and the TED spread "learns and adjusts" faster than competing models. Models that encompass GDP experience a significant decay in predictive power. We also demonstrate that a simulation-based approach is complementary to the EPPC methodology.

Losing Track of the Asset Markets: The Case of Housing and Stock

This paper revisits the relationships among macroeconomic variables and asset returns. Based on recent developments in econometrics, we categorize competing models of asset returns into different Equivalence Predictive Power Classes (EPPC). During the pre-crisis period (1975-2005), some models that emphasize imperfect capital markets outperform an AR(1) for the forecast of housing returns. After 2006, a model that includes both an external finance premium (EFP) and the TED spread learns and adjusts faster than competing models. Models that encompass GDP experience a significant decay in predictive power. We also demonstrate that a simulation-based approach is complementary to the EPPC methodology.

Causes-of-Death Mortality: What Do We Know on Their Dependence?

Over the last century, the assumption usually made was that causes of death are independent, although it is well-known that dependancies exist. Recent developments in econometrics allow, through Vector Error Correction Models (VECMs), to model multivariate dynamic systems including time dependency between economic variables. Common trends that exist between the variables may then be highlighted, the relation between these variables being represented by a long-run equilibrium relationship. In this work, VECMs are developed for causes-of-death mortality. We analyze the five main causes of death across 10 major countries representing a diversity of developed economies. The World Health Organization website provides cause-of-death information for about the last 60 years. Our analysis reveals that long-run equilibrium relationships exist between the five main causes of death, improving our understanding of the nature of dependence between these competing risks over recent years. It also highlights that countries usually had different past experience in regard to cause-of-death mortality trends, and, thus, applying results from one country to another may be misleading.

Wojciech W. Charemza andDerekF. Deadman. NewDirections in Econometric Practice, General to Specific Modelling,Cointegration and Vector Auto Regression. Cheltenham, U.K.: Edward Elgar Publishing Limited. 1997. Pages 360. £28.00 (Paperback).

ighly acclaimed and endorsed by leading econometricians, the book “New Directions in Econometric Practice” is not new among the econometrics and statisticians. It is more of a textbook for students of econometrics and statistics at various levels.It impressively attempts to addressthe main objective of explaining ‘how to practice econometrics’. It provides an accessibleand user-friendly approach to a new approach and methodology presented by David Hendry in his book,‘Dynamic Econometrics’. The book under review provides a practical and hands-on illustration ofHendry’s approach, enabling students to use it for themselves inreal world time-series econometric problems. The second edition of thebook attempts to address the shortfalls identified by some reviewers in the first edition. By providing practical guidelines in terms of empirical illustration of each technique,using DHSY’s suggested aggregated timeseries consumption function on PC-Gives (8.1 Professional), it opens new trails ofresearch. The book is primarily designed for providing an intuitive understanding of recent developments in econometrics to nonspecialist econometricians and is widely adopted by teachers, students and practitioners alike

THE DISTANCE PUZZLE AND LOW‐INCOME COUNTRIES: AN UPDATE

AbstractThe ‘distance effect' measuring the elasticity of trade flows to distance has been found to be rising since the early 1970s in a host of studies based on the gravity model, leading observers to call it the ‘distance puzzle'. However, this puzzle is regularly challenged by new developments in the specification of the gravity equation or in its estimations. We propose an original survey on the existing methods used to quantify the distance puzzle – basically the computation of an average distance of trade, a meta‐analysis on existing gravity papers and the implementation of recent econometric developments, all on a well‐specified gravity equation both in cross‐section and panel data. We apply all these methods to a unique large database (124 countries from 1970 to 2006). It appears that if all these new developments can change the amplitude of the increase in the trade elasticity to distance, none solve the distance puzzle. We confirm the existence of this puzzle and identify that it only applies to low‐income countries which exhibit a significant rising distance effect on their trade of around 18% between 1970 and 2006 while the distance ‘puzzle' for trade within richer countries disappears.

Reexamining the Relationship Between Audit and Nonaudit Fees

The authors introduce some new econometric tests and techniques for identifying and overcoming the problem of weak instruments in the context of joint provision of audit and nonaudit fees. The authors use this context because identifying appropriate instruments is difficult due to the lack of theoretical guidance as well as due to the difficulty in intuitively identifying instruments that satisfy the econometric requirements. The authors introduce a battery of empirical tests based on recent developments in econometrics to test for the appropriateness of the instruments. The authors then illustrate two approaches of using instruments from existing data: the size industry average portfolio approach and the synthetic-instrument approach. Although the approach using synthetic instruments sidesteps issues of identifying proxies with desirable properties, it requires some stringent assumptions that cannot be directly tested. However, as a methodological alternative, this approach can be used for robustness tests. The authors find that when the instruments are not weak, audit and nonaudit fees are positively associated. This relationship holds for audit and tax-related nonaudit fees as well. Overall, the evidence suggests the existence of economies of scope benefits from the joint supply of audit and nonaudit services. Methodologically, the authors illustrate the importance of testing the appropriateness of the instruments utilized when accounting for endogeneity.

Reexamining the Relation Between Audit and Non-Audit Fees: Dealing with Weak Instruments in Two-Stage Least Squares Estimation

We introduce some new econometric tests and techniques for identifying and overcoming the problem of weak instruments in the context of joint provision of audit and non-audit fees. We use this context because identifying appropriate instruments is difficult due to the lack of theoretical guidance as well as the difficulty in intuitively identifying instruments that satisfy the econometric requirements. We introduce a battery of empirical tests based on recent developments in econometrics to test for the appropriateness of the instruments. We then illustrate two approaches of using instruments from existing data: the size-industry average portfolio approach and the synthetic instrument approach. While the approach using synthetic instruments side-steps issues of identifying proxies with desirable properties, it requires some stringent assumptions that cannot be directly tested. However, as a methodological alternative, this approach can be used for robustness tests. We find that when the instruments are not weak, audit and non-audit fees are positively associated. This relationship holds for audit and tax-related non-audit fees as well. Overall, the evidence suggests the existence of economies of scope benefits from the joint supply of audit and non-audit services. Methodologically, we illustrate the importance of testing the appropriateness of the instruments utilized when accounting for endogeneity.

Co-integrated money in the production function–evidence and implications

The notion of money as a factor of production has been debated both theoretically and empirically by a number of researchers in the past four decades. However, the empirical support for money as an input along with labour and capital has been mixed and thus, the issue appears to be unsettled. Recent developments in econometrics regarding co-integration and error correction provide a rich environment in which the role of money in the production function can be reexamined. In a co-integrated space, this article presents empirical evidence indicating that different definitions of money (simple sum or the divisia M2) play an input role in the Cobb–Douglas production function.

Change of regime and Phillips curve stability: The case of Spain, 1964–2002

Following the emergence of the Lucas critique, traditional Phillips curves relating inflation to a measure of the level of activity, and augmented to include past inflation (assumed to proxy expected inflation), have been deemed to be highly unstable over time. In this paper we try to investigate, using recent econometric developments, whether such a statement can be supported over a long time period. In the empirical application, we analyze the case of Spain along the period 1964–2002.