Forecasting with High‐Dimensional Panel VARs

Koop, Gary; Korobilis, Dimitris

doi:10.1111/obes.12303

Cited by 25 publications

(31 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, as noted above, this form can lead to over-parameterization if no restriction is imposed. Therefore, as an extension of the model used by Canova and Ciccarelli (2009), Koop and Korobilis (2019) proposed a time-varying version that assumes a random walk of the factors…”

Section: Methodsmentioning

confidence: 99%

“…where bold-italicu true^ bold-italict bold-italicu true^ bold-italict ′ = ( Y t − X ~ ′ t E ( normalθ t | D t − 1 ) ) ( Y t − X ~ ′ t E ( normalθ t | D t − 1 ) ) ′ and 0<κ ≤ 1. κ is the decay factor. Following the version introduced by Koop and Korobilis (2019), a relatively diffuse choice of an initial value of 0 . 1 × I is set for Σ true^ t .…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Scenario Forecasting for Global Tourism

Wu¹,

Cao

Wen

et al. 2020

Journal of Hospitality & Tourism Research

View full text Add to dashboard Cite

This study provides innovative forecasts of the probabilities of certain scenarios of tourism demand. The scenarios of interest are constructed in relation to tourism growth and economic growth. The probability forecasts based on these scenarios provide valuable information for destination policy makers. The time-varying parameter panel vector autoregressive (TVP-PVAR) model is adopted for scenario forecasting. Both the accuracy rate and the Brier score are used to evaluate the forecasting performance. A global set of 25 tourism destinations is empirically examined, and the results confirm that the TVP-PVAR model with a time-varying error covariance matrix is generally a promising tool for forecasting. Our study contributes to tourism forecasting literature in advocating the use of scenario forecasting to facilitate industry decision making in situations wherein forecasts are defined by two or more dimensions simultaneously. In addition, it is the first study to introduce the TVP-PVAR model to tourism demand forecasting.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Scenario Forecasting for Global Tourism

Wu¹,

Cao

Wen

et al. 2020

Journal of Hospitality & Tourism Research

View full text Add to dashboard Cite

show abstract

“…Second, by exploiting the time-series and cross-sectional aspects of our data, the panel VAR allows us to model additional complexities than if we had either only time-series or cross-sectional data. Like many static and dynamic panel data models, we are able to control for unobserved characteristics across states that simultaneously determine income inequality and crime using state fixed effects, while the time-series aspect allows us to include sufficiently long lags of the endogenous variables, thereby practically eliminating concerns about endogeneity (Koop and Korobilis, 2016). Third, the time period for estimation, before taking lags, differencing, and other standard data transformations into account, is 1960-2015 (T = 56) for the fifty states and DC.…”

Section: Identification and Estimation Methodologymentioning

confidence: 99%

Identifying the Dynamic Effects of Income Inequality on Crime

Atems

2020

Oxf Bull Econ Stat

View full text Add to dashboard Cite

What happens to crime after an increase in income inequality? The microeconomics literature that attempts to answer this question often employs identification strategies that exploit external sources of variation that provide quasi‐experiments to identify causal effects. In contrast, this paper tackles this question by using structural vector autoregressions (SVAR), a methodology typically employed in modern empirical macroeconomics to identify and estimate dynamic causal effects of exogenous shocks. Unlike the macroeconomic SVAR models that are often applied to time‐series data, we exploit the time series and cross‐sectional dimensions of our data, leading to the estimation of panel SVAR models. Using U.S. state‐level data for the period 1960–2015, our results indicate that structural shocks to inequality increase both violent and property crime. Variance decomposition analyses show that inequality has little explanatory power for movements in crime.

show abstract

“…Furthermore, through PVAR approach, we are able to conquer the issues generated by using granger causality analysis or Vector Error Correction model individually [ 51 , 52 ]. PVAR model enables all variables being considered to be treated as interdependent and endogenous, besides, it is able to model how shocks are transmitted among different countries [ 53 , 54 ].…”

Section: Methodsmentioning

confidence: 99%

The Nexus between Economic Complexity and Energy Consumption under the Context of Sustainable Environment: Evidence from the LMC Countries

Liu

Liang

Cui

2020

IJERPH

View full text Add to dashboard Cite

The wide application of various energy resources in economic development is allegedly responsible for deepening environmental deterioration in terms of increasing pollution emissions and other negative consequences including climate change. This current work investigates the interdependent correlation between energy consumption (both fossil fuel energy consumption and renewable energy consumption) and economic complexity among Lancang-Mekong Cooperation (hereafter LMC) countries, from 1991 to 2017. As for empirical analysis, a panel vector autoregression (PVAR) model was employed. Outcomes of this research confirm the existence of a unidirectional relationship between energy consumption and economic complexity index. It is verified that renewable energy usage is a possible alternative to traditional energy and is able to increase economic complexity. This current research proposed to contribute as a pioneering exploration on LMC countries by adding original observations into existing studies. Finally, we will discuss policy implications of this work.

show abstract

Forecasting with High‐Dimensional Panel VARs

Cited by 25 publications

References 40 publications

Scenario Forecasting for Global Tourism

Scenario Forecasting for Global Tourism

Identifying the Dynamic Effects of Income Inequality on Crime

The Nexus between Economic Complexity and Energy Consumption under the Context of Sustainable Environment: Evidence from the LMC Countries

Contact Info

Product

Resources

About