https://isiarticles.com/bundles/Article/pre/pdf/11019.pdf

Energy consumption, carbon emissions, and economic growth in China

This paper examines the role of tourism in the Spanish long-run economic development. The tourism-led growth hypothesis is confirmed through cointegration and causality testing. The results indicate that, at least, during the last three decades, economic growth in Spain has been sensible to persistent expansion of international tourism. The increase of this activity has produced multiplier effects over time. External competitivity has also been proved in the model to be a fundamental variable for Spanish economic growth. From the empirical analysis it can be inferred the positive effects on income that government policy, in the adequacy of supply as well as in the promotion of tourist activity, may bring about.

/pdf/tourism-as-a-long-run-economic-growth-factor-the-spanish-3q8q8ke1ee.pdf

Tourism as a long-run economic growth factor: the Spanish case

Energy consumption, income, and carbon emissions in the United States

https://isiarticles.com/bundles/Article/pre/pdf/11051.pdf

Energy consumption, economic growth, and carbon emissions: Challenges faced by an EU candidate member

Energy consumption and GDP in developing countries: A cointegrated panel analysis

Statistical inference in vector autoregressions with possibly integrated processes

In this note, a manageable expression for the asymptotic mean square error of predicting more than one‐step ahead from an estimated autoregressive model is derived. The result relies on a useful lemma on matrix differentiation, and differs from recent similar work of Bhansali (1974).

Asymptotic Mean Square Prediction Error for an Autoregressive Model with Estimated Coefficients

This article investigates major effects of misspecification in stationary linear time series models when we fit a pth-order autoregressive model. The true model can be an autoregressive moving average model. We derive the formulas of bias and mean squared error (MSE) of the least squares estimator and the hth period ahead prediction MSE in the time domain. Contrary to previous studies, the process in estimation is not necessarily independent of the process in prediction, and the distribution of process is not necessarily Gaussian. We examine the effects of this dependence and nonnormality on prediction in misspecified models.

Properties of Predictors in Misspecified Autoregressive Time Series Models

SUMMARY A simple formula for multiperiod predictions of multivariate autoregressive-moving average models is derived.The formula is explicitly given as a function of suitably defined parameter matrices and an observation vector, and includes simpler models as its special cases. The asymptotic prediction mean squared error is also derived for predictions with estimated parameters.

Predictions of multivariate autoregressive-moving average models

This paper proposes an inference procedure for a possibly integrated vector autoregression (VAR) model. We modify the lag augmented VAR (LA-VAR) estimator to exclude the quasiasymptotic bias, which is associated with the term Op(T-1), using the jackknife method. The new estimator has an asymptotic normal distribution and then the Wald statistic to test for the parameter restrictions has an asymptotic chi-square distribut,ion. We investigate the finite sample properties of this approach by comparing with the LA-VAR approach. We find t,hat our modified LA-VAR (MLA-VAR) approach excels the LA-VAR approach in view of an accuracy of the empirical size and the robustness to the tnisspecification of the lag length. The MLA-VAR approach may be used when the researchers place importance on an accuracy of the size, and also be used to complement other testing procedures that may suffer from serious size distortion.

Taku Yamamoto

Papers

Statistical inference in vector autoregressions with possibly integrated processes

Asymptotic Mean Square Prediction Error for an Autoregressive Model with Estimated Coefficients

Properties of Predictors in Misspecified Autoregressive Time Series Models

Predictions of multivariate autoregressive-moving average models

Modified lag augmented vector autoregressions