Agriculture, trade openness and emissions: an empirical analysis and policy options

This article investigates the impact of sectoral production allocation, energy usage patterns and trade openness on pollutant emissions in a panel consisting of high-, medium- and low-income countries. Extended STIRPAT (Stochastic Impact by Regression on Population, Affluence and Technology) and EKC (Environmental Kuznets Curve) models are conducted to systematically identify these factors driving CO2 emissions in these countries during the period 1980–2010. To this end, the studyemploys three different heterogeneous, dynamic mean group-type linear panel modelsand one nonlinear panel data estimation procedure that allows for cross-sectionaldependence. While affluence, nonrenewable energy consumption and energy intensity variables are found to drive pollutant emissions in linear models, population is also found to be a significant driver in the nonlinear model. Both service sector and agricultural value-added levels play a significant role in reducing pollution levels, whereas industrialisation increases pollution levels. Although the linear model fails totrack any significant impact of trade openness, the nonlinear model finds trade liberalisation to significantly affect emission reduction levels. All of these results suggest that economic development, and especially industrialisation strategies and environmental policies, need to be coordinated to play a greater role in emission reduction due to trade liberalisation.

Examining the behaviour of visitor arrivals to Australia from 28 different countries

Testing the integrational properties of visitor arrivals has important implications for policy, for if visitor arrivals are integrated of order one (nonstationary) then it implies that shocks to visitor arrivals are permanent. However, if visitor arrivals are found to be integrated or order zero (stationary) then this implies that shocks to visitor arrivals are temporary. In this paper we examine whether visitor arrivals to Australia are stationary or nonstationary, using the recently developed univariate and panel Lagrange multiplier tests, and the Im, Pesaran and Shin [Im, K.S., Pesaran, M.H., Shin, Y., 1997. Testing for Unit Roots in Heterogeneous Panels. Manuscript, Department of Applied Economics, University of Cambridge; Im, K.S., Pesaran, M.H., Shin, Y., 2003. Testing for Unit Roots in Heterogeneous Panels. Journal of Econometrics, 115, 53–74] panel t-test. Our exercise involves Australia’s 28 tourist source markets. Our main findings are: (1) that visitor arrivals to Australia from 28 tourist source markets are stationary, implying that any shock will have only a temporary effect and (2) the second structural break, which mainly coincides with the September 11 terrorist attacks and the Asian financial crisis, has slowed down the growth rate in visitor arrivals to Australia from 22 out of 28 (79%) of the tourism source markets.

On the estimation and inference in factor-augmented panel regressions with correlated loadings

In an influential paper, Pesaran [Pesaran, M.H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica 74, 967–1012] proposes a very simple estimator of factor-augmented regressions that has since then become very popular. In this note we demonstrate how the presence of correlated factor loadings can render this estimator inconsistent.

Does electricity consumption panel Granger cause GDP? A new global evidence

The goal of this paper is to undertake a panel data investigation of long-run Granger causality between electricity consumption and real GDP for seven panels, which together consist of 93 countries. We use a new panel causality test and find that in the long-run both electricity consumption and real GDP have a bidirectional Granger causality relationship except for the Middle East where causality runs only from GDP to electricity consumption. Finally, for the G6 panel the estimates reveal a negative sign effect, implying that increasing electricity consumption in the six most industrialised nations will reduce GDP. © 2010 Elsevier Ltd. All rights reserved.

Testing for error correction in panel data

This paper proposes new error correction-based cointegration tests for panel data. The limiting distributions of the tests are derived and critical values provided. Our simulation results suggest that the tests have good small-sample properties with small size distortions and high power relative to other popular residual-based panel cointegration tests. In our empirical application, we present evidence suggesting that international healthcare expenditures and GDP are cointegrated once the possibility of an invalid common factor restriction has been accounted for. © 2007 Blackwell Publishing Ltd.

Mixed signals among tests for panel cointegration

In this paper, we study the effect that different serial correlation adjustment methods can have on panel cointegration testing. As an example, we consider the very popular tests developed by Pedroni [Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics 61, 653670., Pedroni, P. (2004). Panel cointegration: asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econometric Theory 20, 597-625.]. Results based on both simulated and real data suggest that different adjustment methods can lead to significant variations in test outcome, and thus also in the conclusions. © 2007 Elsevier B.V. All rights reserved.

Output and labor productivity in organized manufacturing: a panel cointegration analysis for India

One of the policy puzzles faced in India during the last two and half decades has been the weak association between output and labor markets, particularly in the manufacturing sector. In this research, we investigate the long-run relationship between output, labor productivity and real wages in the case of organized manufacturing. We adjust the measure of labor productivity incorporating bottlenecks, such as lack of infrastructure, access to external finance, and labor regulations, which all may influence labor market outcomes. Using panel data from seventeen manufacturing industries, we establish long-run dynamics for the output-labor productivity-real wages series over a period of nearly three decades. We employ recently developed panel unit root and cointegration tests for cross-sectional dependence to incorporate heterogeneity across industries. Long-run elasticities are generally found to be low for labor productivity compared to real wages due to the changes in manufacturing output. There are variations across industries within the manufacturing sector for the effects of the labor market on manufacturing output. In some industries, lower wages are associated with higher output, and the reason for the positive relationship in other industries could be due to workers' bargaining power.

Fault-tolerant scheduling with dynamic number of replicas in heterogeneous systems

In the existing studies on fault-tolerant scheduling, the active replication schema makes use of ε + 1 replicas for each task to tolerate ε failures. However, in this paper, we show that it does not always lead to a higher reliability with more replicas. Besides, the more replicas implies more resource consumption and higher economic cost. To address this problem, with the target to satisfy the user’s reliability requirement with minimum resources, this paper proposes a new fault tolerant scheduling algorithm: MaxRe. In the algorithm, we incorporate the reliability analysis into the active replication schema and the theoretical analysis and experiments prove that the MaxRe algorithm’s schedule can certainly satisfy user’s reliability requirements. And the MaxRe scheduling algorithm can achieve the corresponding reliability with at most 70% fewer resources than the FTSA algorithm.

Dynamic virtual workspace for teleoperation of heterogeneous robots in non-deterministic environments

Robots are ever increasing in a variety of different workplaces providing an array of benefits such alternative solutions to traditional human labor. While developing fully autonomous robots is the ultimate goal in many robotic applications the reality is that there still exist many situationswere robots require some level of teleoperation in order to achieve assigned goals especially when deployed in non-deterministic environments. For instance teleoperation is commonly used in areas such as search and rescue, bomb disposal and exploration of inaccessible or harsh terrain. This is due to a range of factors such as the lack of ability for robots to quickly and reliably navigate unknown environments or provide high-level decision making especially intime critical tasks. To provide an adequate solution for such situations human-in-the-loop control is required. When developing human-in-the-loop control it is important to take advantage of the complimentary skill-sets that both humans and robots share. For example robots can performrapid calculations, provide accurate measurements through hardware such as sensors and store large amounts of data while humans provide experience, intuition, risk management and complex decision making capabilities. Shared autonomy is the concept of building robotic systems that take advantage of these complementary skills-sets to provide a robust an efficient robotic solution. While the requirement of human-in-the-loop control exists Human Machine Interaction (HMI) remains an important research topic especially the area of User Interface (UI) design.In order to provide operators with an effective teleoperation system it is important that the interface is intuitive and dynamic while also achieving a high level of immersion. Recent advancements in virtual and augmented reality hardware is giving rise to innovative HMI systems. Interactive hardware such as Microsoft Kinect, leap motion, Oculus Rift, Samsung Gear VR and even CAVE Automatic Virtual Environments [1] are providing vast improvements over traditional user interface designs such as the experimental web browser JanusVR [2]. This combined with the introduction of standardized robot frameworks such as ROS and Webots [3] that now support a large number of different robots provides an opportunity to develop a universal UI for teleoperation control to improve operator efficiency while reducing teleoperation training.This research introduces the concept of a dynamic virtual workspace for teleoperation of heterogeneous robots in non-deterministic environments that require human-in-the-loop control. The system first identifies the connected robots through the use kinematic information then determines its network capabilities such as latency and bandwidth. Given the robot type and network capabilities the system can then provide the operator with available teleoperation modes such as pick and place control or waypoint navigation while also allowing them to manipulate the virtual workspace layout to provide information from onboard camera’s or sensors.

Dynamic parallel job scheduling in multi-cluster computing systems

Job scheduling is a complex problem, yet it is fundamental to sustaining and improving the performance of parallel processing systems. In this paper, we address an on-line parallel job scheduling problem in heterogeneous multi-cluster computing systems. We propose a new space-sharing scheduling policy and show that it performs substantially better than the conventional policies.

Propagation characteristics of lamb waves in stringer-stiffened panels

Stringer-stiffened plate-like structure is a typical engineering structure and its structural integrity is critical. A guided Lamb wave-based damage identification scheme and an online structural health monitoring (SHM) system with an integrated PZT-sensor network were developed. In the previous studies, the specimens were relatively simple. In this paper, the above mentioned method was extended to the stiffened plate-like structure—a flat plate reinforced by stringer. FE dynamic simulation was applied to investigate the Lamb wave propagation characteristics due to the existence of stringer with the consideration of its material and geometric configurations.

Dynamic price discrimination with customer recognition

This paper studies a general two-period model of product line pricing with customer recognition. Specifically, we consider a monopolist who can sell vertically differentiated products over two periods to heterogeneous consumers. Each consumer demands one unit of the product in each period. In the second period, the monopolist can condition the price-quality offers on the observed purchasing behavior in the first period. In this setup, the monopolist can price discriminate consumers in two dimensions: by quality as well as by purchase history. We fully characterize the monopolist's optimal pricing strategy when there are two types of consumers. When the type space is a continuum, we show that there is no fully separating equilibrium, and some properties of the optimal contracts (price-quality pairs) are characterized within the class of partitional perfect Bayesian equilibria.

Nonparametric rank tests for non-stationary panels

We develop a set of nonparametric rank tests for non-stationary panels based on multivariate variance ratios which use untruncated kernels. As such, the tests do not require the choice of tuning parameters associated with bandwidth or lag length and also do not require choices with respect to numbers of common factors. The tests allow for unrestricted cross-sectional dependence and dynamic heterogeneity among the units of the panel, provided simply that a joint functional central limit theorem holds for the panel of differenced series. We provide a discussion of the relationships between our setting and the settings for which first- and second generation panel unit root tests are designed. In Monte Carlo simulations we illustrate the small-sample performance of our tests when they are used as panel unit root tests under the more restrictive DGPs for which panel unit root tests are typically designed, and for more general DGPs we also compare the small-sample performance of our nonparametric tests to parametric rank tests. Finally, we provide an empirical illustration by testing for income convergence among countries.

The asymptotic distribution of the CADF unit root test in the presence of heterogeneous AR(p) errors

The CADF test of Pesaran (J Appl Econ 22:265–312, 2007) are among the most popular univariate tests for cross-section correlated panels around. Yet, the existing asymptotic analysis of this test statistic is limited to a model in which the errors are assumed to follow a simple AR(1) structure with homogenous autoregressive coefficients. One reason for this is that the model involves an intricate identification issue, as both the serial and cross-section correlation structures of the errors are unobserved. The purpose of the current paper is to tackle this issue and in so doing extend the existing analysis to the case of AR((Formula presented.)) errors with possibly heterogeneous coefficients.