scholarly journals Parameter Heterogeneity, Persistence and Cross-Sectional Dependence: New Insights on Fiscal Policy Reaction Functions for the Euro Area

2018 ◽  
Author(s):  
Roberto Golinelli ◽  
Irene Mammi ◽  
Antonio Musolesi
Keyword(s):  
Fiscal Policy ◽  
Euro Area ◽  
Cross Sectional ◽  
Reaction Functions ◽  
Parameter Heterogeneity ◽  
Cross Sectional Dependence

Discretionary fiscal policy in the Euro area: Past, present and future

2020 ◽  
pp. 55-85
Author(s):  
Francesco Caprioli ◽  
Marzia Romanelli ◽  
Pietro Tommasino
Keyword(s):  
Fiscal Policy ◽  
Euro Area ◽  
Discretionary Fiscal Policy

Extent Pursuit for Cross-Sectional Dependence in Large Panels

2019 ◽  
Author(s):  
Jiti Gao ◽  
Guangming Pan ◽  
Yanrong Yang ◽  
Bo Zhang
Keyword(s):  
Cross Sectional ◽  
Large Panels ◽  
Cross Sectional Dependence

Nonparametric panel data regression with parametric cross-sectional dependence

2021 ◽  
Author(s):  
Alexandra Soberon ◽  
Juan M Rodriguez-Poo ◽  
Peter M Robinson
Keyword(s):  
Panel Data ◽  
Monte Carlo Study ◽  
Generalized Least Squares ◽  
Linear Estimator ◽  
Dependence Structure ◽  
Finite Sample ◽  
Cross Sectional ◽  
Panel Data Regression ◽  
Local Linear Estimator ◽  
Cross Sectional Dependence

Abstract In this paper, we consider efficiency improvement in a nonparametric panel data model with cross-sectional dependence. A Generalized Least Squares (GLS)-type estimator is proposed by taking into account this dependence structure. Parameterizing the cross-sectional dependence, a local linear estimator is shown to be dominated by this type of GLS estimator. Also, possible gains in terms of rate of convergence are studied. Asymptotically optimal bandwidth choice is justified. To assess the finite sample performance of the proposed estimators, a Monte Carlo study is carried out. Further, some empirical applications are conducted with the aim of analyzing the implications of the European Monetary Union for its member countries.

What Goes Up Might Not Come Down: Modeling Directional Asymmetry with Large-N, Large-T Data

2021 ◽  
pp. 008117502110463
Author(s):  
Ryan P. Thombs ◽  
Xiaorui Huang ◽  
Jared Berry Fitzgerald
Keyword(s):  
Fixed Effects ◽  
Directional Asymmetry ◽  
Cross Sectional ◽  
Monte Carlo Experiments ◽  
Long Run ◽  
Large N ◽  
Fixed Effects Estimator ◽  
Cross Sectional Dependence ◽  
Using Data ◽  
Heterogeneous Panel

Modeling asymmetric relationships is an emerging subject of interest among sociologists. York and Light advanced a method to estimate asymmetric models with panel data, which was further developed by Allison. However, little attention has been given to the large- N, large- T case, wherein autoregression, slope heterogeneity, and cross-sectional dependence are important issues to consider. The authors fill this gap by conducting Monte Carlo experiments comparing the bias and power of the fixed-effects estimator to a set of heterogeneous panel estimators. The authors find that dynamic misspecification can produce substantial biases in the coefficients. Furthermore, even when the dynamics are correctly specified, the fixed-effects estimator will produce inconsistent and unstable estimates of the long-run effects in the presence of slope heterogeneity. The authors demonstrate these findings by testing for directional asymmetry in the economic development–CO2 emissions relationship, a key question in macro sociology, using data for 66 countries from 1971 to 2015. The authors conclude with a set of methodological recommendations on modeling directional asymmetry.

Detecting common breaks in the means of high dimensional cross-dependent panels

2021 ◽  
Author(s):  
Lajos Horváth ◽  
Zhenya Liu ◽  
Gregory Rice ◽  
Yuqian Zhao
Keyword(s):  
Panel Data ◽  
Common Factors ◽  
Real Data ◽  
Change Points ◽  
High Dimensional ◽  
Asymptotic Results ◽  
Cross Sectional ◽  
Data Set ◽  
Monte Carlo Simulation Study ◽  
Cross Sectional Dependence

Abstract The problem of detecting change points in the mean of high dimensional panel data with potentially strong cross–sectional dependence is considered. Under the assumption that the cross–sectional dependence is captured by an unknown number of common factors, a new CUSUM type statistic is proposed. We derive its asymptotic properties under three scenarios depending on to what extent the common factors are asymptotically dominant. With panel data consisting of N cross sectional time series of length T, the asymptotic results hold under the mild assumption that min {N, T} → ∞, with an otherwise arbitrary relationship between N and T, allowing the results to apply to most panel data examples. Bootstrap procedures are proposed to approximate the sampling distribution of the test statistics. A Monte Carlo simulation study showed that our test outperforms several other existing tests in finite samples in a number of cases, particularly when N is much larger than T. The practical application of the proposed results are demonstrated with real data applications to detecting and estimating change points in the high dimensional FRED-MD macroeconomic data set.

Modelling tourism competitiveness in small Pacific island countries

2021 ◽  
pp. 135481662110409
Author(s):  
Nikeel N Kumar ◽  
Arvind Patel ◽  
Rup Singh
Keyword(s):  
Pacific Island ◽  
Group Approach ◽  
Cross Sectional ◽  
Cook Islands ◽  
Quarterly Data ◽  
Price Elasticities ◽  
Pooled Mean Group ◽  
Competitive Behaviour ◽  
Cross Sectional Dependence ◽  
Pacific Island Countries

This study models overall and bilateral tourism competitiveness in small Pacific island countries (PICs), namely, Cook Islands, Fiji, Tonga, Samoa and Vanuatu. The pooled mean group approach, which corrects for cross-sectional dependence and non-stationarity, is used for estimation with quarterly data from 2002 to 2019. The findings indicate that for Fiji and Vanuatu, other PICs are competing destinations and that Fiji and Vanuatu face the strongest bilateral competition amongst the selected PICs. Cross-price elasticities are insignificant for Tonga and are generally negative for the Cook Islands and Samoa. Thus, while for Fiji and Vanuatu, the Cook Islands is a competing destination, Fiji and Vanuatu are complementary destinations for the Cook Islands. Therefore, destinations that more closely resemble each other face stronger competition, and the nature and strength of competitive behaviour between two destinations are different for each concerned destination.

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