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This paper aims to investigate spillover effects of public capital stock in a production function model that accounts for spatial dependencies. In many settings, ignoring spatial dependency yields inefficient, biased and inconsistent estimates in cross country panels. Although there are a number of studies aiming to estimate the output elasticity of public capital stock, many of those fail to reach a consensus on refining the elasticity estimates. We argue that accounting for spillover effects of the public capital stock on the production efficiency and incorporating spatial dependences are crucial. For this purpose, we employ a spatial autoregressive stochastic frontier model based on a number of specifications of the spatial dependency structure. Using the data of 21 OECD countries from 1960 to 2001, we estimate a spatial autoregressive stochastic frontier model and derive the mean indirect marginal effects of public capital stock, which are interpreted as spillover effects. We found that spillover effects can be an important factor explaining variations in technical inefficiency across countries as well as in explaining the discrepancies among various levels of output elasticity of public capital stock in traditional production function approaches.

In the two seminal papers Anderson and Hsiao (1981, 1982), the linear panel regression model without cross-sectional correlation is thoroughly discussed. This uncorrelatedness assumption is now often examined in empirical work, using tests such as those by Pesaran, Ullah, and Yamagata (2008), Hsiao, Pesaran, and Pick (2012), or Pesaran (2015). All these tests in turn improve upon the so-called error-components test suggested in Breusch and Pagan (1980). In this chapter, the author revisits this error-components test and derives its asymptotic distribution under various scenarios: (a) both time-series dimension T and cross-sectional dimension N go to ∞ jointly (Phillips & Moon, 1999); (b) T → ∞ while N is fixed, and (c) N → ∞ while T is fixed. To the best of the author’s knowledge, the results under Scenarios (b) and (c) are new. Moreover, while the distributions under (a) and (b) are normal, that under (c) is not and it is even asymmetric. The critical values under (c) can be simulated. A Monte Carlo experiment is performed and it aims to throw light on the choice among the critical values suggested in the three scenarios, given a T and an N.

Tourism demand forecasting is vital for the airline industry and tourism sector. Combination forecasting has the advantage of fusing several forecasts to reduce the risk of inappropriate model selection for analyzing decisions. This paper investigated the effects of a time-varying weighting strategy on the performance of linear and nonlinear forecast combinations in the context of tourism.

This study used grey prediction models, which did not require that the available data satisfy statistical assumptions, to generate forecasts. A quality-control technique was applied to determine when to change the combination weights to generate combined forecasts by using linear and nonlinear methods.

The empirical results showed that except for when the Choquet fuzzy integral was used, forecast combination with time-varying weights did not significantly outperform that with fixed weights. The Choquet integral with time-varying weights significantly outperformed that with fixed weights for all model combinations, and had a superior forecasting accuracy to those of other combination methods.

The tourism sector can benefit from the use of the Choquet integral with time-varying weights, by using it to formulate suitable strategies for tourist destinations.

Combining forecasts with time-varying weights may improve the accuracy of the predictions. This study investigated incorporating a time-varying weighting strategy into combination forecasting by using CUSUM. The results verified the effectiveness of the time-varying Choquet integral for tourism forecast combination.

This chapter estimates heterogeneous productivity growth and spatial spillovers through industrial linkages in the United States and China from 1981 to 2010. The authors employ a spatial Durbin stochastic frontier model and estimates with a spatial weight matrix based on inter-country input–output linkages to describe the spatial interdependencies in technology. The authors estimate productivity growth and spillovers at the industry level using the World KLEMS database. The spillovers of factor inputs and productivity growth are decomposed into domestic and international effects. Most of the spillover effects are found to be significant and the spillovers of productivity growth offered and received provide detailed information reflecting interdependence of the industries in the global value chain (GVC). The authors use this model to evaluate the impact of a US–Sino decoupling of trade links based on simulations of four scenarios of the reductions in bilateral intermediate trade. Their estimation results and their simulations are as mentioned based on date that ends in 2010, as this is the only KLEMS data available for these countries at this level of industrial disaggregation. As the GVC linkages between the United States and China have expanded since the end of their sample period their results can be viewed as informative in their own right for this period as well as possible lower bounds on the extent of the spillovers generated by an expanding GVC.

Owing to increased energy demands, China has become the world’s top CO2 emitter, with electricity generation accounting for the majority of emissions. Therefore, the Chinese Government aspires to achieve a low-carbon transformation of the electric industry by enhancing its green innovation capacity. However, little attention has been paid to the green development of electric technology. Thus, this paper aims to uncover the spatiotemporal evolution of electric technology in the context of China’s low-carbon transformation through patent analysis.

Using granted green invention patent data for China’s electric industry between 2000 and 2021, this paper conducted an exploratory, spatial autocorrelation and time-varying difference-in-differences (DID) analysis to reveal the landscape of electric technology.

Exploratory analysis shows that the average growth rate of electric technology is 8.1%, with spatial heterogeneity, as there is slower growth in the north and west and faster growth in the south and east. In addition, electric technology shows spatial clustering in local areas. Finally, the time-varying DID analysis provides positive evidence that low-carbon policies improve the green innovation capacity of electric technology.

The different effects of the low-carbon pilot policy (LCPC) on R&D subjects and the LCPC’s effectiveness in enhancing the value of patented technology were not revealed.

This paper reveals the spatiotemporal evolutionary characteristics of electric technology in mainland China. The results can help the Chinese Government clarify how to carry out innovative development in the electric industry as part of the low-carbon transformation and provide a theoretical basis and research direction for newcomers in this field.

This article provides an overview of the panel vector autoregressive models (VAR) used in macroeconomics and finance to study the dynamic relationships between heterogeneous assets, households, firms, sectors, and countries. We discuss what their distinctive features are, what they are used for, and how they can be derived from economic theory. We also describe how they are estimated and how shock identification is performed. We compare panel VAR models to other approaches used in the literature to estimate dynamic models involving heterogeneous units. Finally, we show how structural time variation can be dealt with.

The purpose of this paper is to propose an improved method which can shorten the calculation time and improve the calculation efficiency under the premise of ensuring the calculation accuracy for calculating the response of dynamic systems with periodic time-varying characteristics.

An improved method is proposed based on Runge–Kutta method according to the composition characteristics of the state space matrix and the external load vector formed by the reduction of the dynamic equation of the periodic time-varying system. The recursive scheme of the holistic matrix of the system using the Runge–Kutta method is improved to be the sub-block matrix that is divided into the upper and lower parts to reduce the calculation steps and the occupied computer memory.

The calculation time consumption is reduced to a certain extent about 10–35% by changing the synthesis method of the time-varying matrix of the dynamics system, and the method proposed of paper consumes 43–75% less calculation time in total than the original Runge–Kutta method without affecting the calculation accuracy. When the ode45 command that implements the Runge–Kutta method in the MATLAB software used to solve the system dynamics equation include the time variable which cannot provide its specific analytic function form, so the time variable value corresponding to the solution time needs to be determined by the interpolation method, which causes the calculation efficiency of the ode45 command to be substantially reduced.

The proposed method can be applied to solve dynamic systems with periodic time-varying characteristics, and can consume less calculation time than the original Runge–Kutta method without affecting the calculation accuracy, especially the superiority of the improved method of this paper can be better demonstrated when the degree of freedom of the periodic time-varying dynamics system is greater.

Purpose – This chapter considers a Hausman and Taylor (1981) panel data model that exhibits a Cliff and Ord (1973) spatial error structure.

Methodology/approach – We analyze the small sample properties of a generalized moments estimation approach for that model. This spatial Hausman–Taylor estimator allows for endogeneity of the time-varying and time-invariant variables with the individual effects. For this model, the spatial fixed effects estimator is known to be consistent, but its disadvantage is that it wipes out the effects of time-invariant variables which are important for most empirical studies.

Findings – Monte Carlo results show that the spatial Hausman–Taylor estimator performs well in small samples.