scholarly journals Non-Negativity of a Quadratic form with Applications to Panel Data Estimation, Forecasting and Optimization

Stats ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 185-202
Author(s):  
Bhimasankaram Pochiraju ◽  
Sridhar Seshadri ◽  
Dimitrios Thomakos ◽  
Konstantinos Nikolopoulos
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Symmetric Matrix ◽  
Panel Data Models ◽  
Random Effects Models ◽  
Panel Data Estimation ◽  
Quadratic Minimization ◽  
Fixed And Random Effects ◽  
Estimated Error ◽  
Data Estimation

For a symmetric matrix B, we determine the class of Q such that Q t BQ is non-negative definite and apply it to panel data estimation and forecasting: the Hausman test for testing the endogeneity of the random effects in panel data models. We show that the test can be performed if the estimated error variances in the fixed and random effects models satisfy a specific inequality. If it fails, we discuss the restrictions under which the test can be performed. We show that estimators satisfying the inequality exist. Furthermore, we discuss an application to a constrained quadratic minimization problem with an indefinite objective function.

Bridging Methodological Divides Between Macro- and Microresearch: Endogeneity and Methods for Panel Data

2019 ◽  
Vol 46 (1) ◽  
pp. 70-99 ◽  
Author(s):  
Paul D. Bliese ◽  
Donald J. Schepker ◽  
Spenser M. Essman ◽  
Robert E. Ployhart
Keyword(s):  
Panel Data ◽  
Random Effects ◽  
Fixed Effects ◽  
Theory Development ◽  
Modeling Framework ◽  
Random Effects Models ◽  
Data Support ◽  
Fixed And Random Effects

Both macro- and micro-oriented researchers frequently use panel data where the outcome of interest is measured repeated times. Panel data support at least five different modeling frameworks (within, between, incremental/emergent, cross-level, and growth). Researchers from macro- and micro-oriented domains tend to differentially use the frameworks and also use different analytic tools and terminology when using the same modeling framework. These differences have the potential to inhibit cross-discipline communication. In this review, we explore how macro- and microresearchers approach panel data with a specific emphasis on the theoretical implications of choosing one framework versus another. We illustrate how fixed-effects and random-effects models differ and how they are similar, and we conduct a thorough review of 142 articles that used panel data in leading management journals in 2017. Ultimately, our review identifies ways that researchers can better employ fixed- and random-effects models, model time as a meaningful predictor or ensure unobserved time heterogeneity is controlled, and align hypotheses to analytic choice. In the end, our goal is to help facilitate communication and theory development between macro- and micro-oriented management researchers.

scholarly journals Exchange Rate Volatility and Domestic Consumption: A Comparative Study of Developed, Developing and Emerging Economies

2019 ◽  
Vol 9 (3) ◽  
pp. 164
Author(s):  
Muhammad Zubair Chishti ◽  
Hafiz Syed Muhammad Azeem ◽  
Farrukh Mahmood ◽  
Adeel Ahmed Sheikh
Keyword(s):  
Exchange Rate ◽  
Panel Data ◽  
Developing Economies ◽  
Exchange Rate Volatility ◽  
Domestic Consumption ◽  
Aggregate Consumption ◽  
Developed Economies ◽  
Panel Data Estimation ◽  
The Exchange Rate ◽  
Data Estimation

The current study endeavors to explore the effects of oscillations in the exchange rate on the household aggregate consumption of developed, emerging, and developing economies, employing the panel data from 1995 to 2017. To select an appropriate panel data estimation technique, we apply Brush-Pagan & Hausman Tests for each set of chosen economies. Further, our study deduces that, in the case of developed economies, the oscillations in the exchange rate, significantly, affect the domestic consumption, supporting Alexander’s (1952) conjecture. However, in the case of emerging and developing economies, aggregate consumption does not respond to the exchange rate volatility.

scholarly journals Sars-Cov-2 in Argentina: Lockdown, Mobility, and Contagion

2020 ◽  
Author(s):  
Juan M.C. Larrosa
Keyword(s):  
Panel Data ◽  
Granger Causality ◽  
Random Effects ◽  
Social Costs ◽  
Political Discussion ◽  
Fixed And Random Effects ◽  
The World ◽  
Causality Tests ◽  
Granger Causality Tests ◽  
Over Time

AbstractThere is a debate in Argentina about the effectiveness of mandatory lockdown measures in containing COVID-19 that lasts five months making it one of the longest in the World. The population effort to comply the lockdown has been decreasing over time given the economic and social costs that it entails. We contributes by analyzing the Argentinian case through information of mobility and contagion given answers to recurrent questions on these topics. This paper aims to fill the gap in the literature by assessing the effects of lockdown measures and the regional relaxation on the numbers of rate of new infections. We also respond to issues of internal political discussion on regional contagion and the effect of marches and unexpected crowd events. We use pool, fixed and random effects panel data modeling and Granger causality tests identifying relations between mobility and contagion. Our results show that lockdown in Argentina has been effective in reducing the mobility but not in way that reduces the rate of contagion. Strict lockdown seems to be effective in short periods of time and by extend it without complementary measures loss effectiveness. Contagion rate seems to be discretely displaced in time and resurging amidst slowly increasing in mobility.

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