Panel Data in Econometrics

In today’s world, social media is playing an indispensable role on the learning behavior of university students to achieve sustainable education. The impact of social media on sustainable education is becoming an essential and impelling factor. The world has become a global village and technology use has made it a smaller world through social media and how it is changing instruction. This original study is amongst the few to perform a focalized investigation on revealing the relationship between positive and negative characteristics of social media and the learning attitude of university students for sustainable education. However, this study aims to examine the constructive and adverse factors that impact on students’ minds and how these helped students to share positive and negative aspects with others. It is increasingly noticeable that social networking sites and their applications present enormous benefits for as well as risks to university students and their implications on students’ psychological adjustment or learning behaviors are not well understood. This study adapted the cluster sampling method, and respondents participated from five selected regions. Researchers distributed 1013 questionnaires among the targeted sample of university students with an age range of 16 to 35 years, and they collected 831 complete/valid responses. This study applied the social gratification theory to examine students’ behavior practicing social media usage. This study specifically identified 18 adversarial and constructive factors of social media from the previous literature. The findings revealed that the usage of social media in Pakistan has a negative influence on a student’s behavior as compared to positive aspects. Results may not be generalized to the entire student community as findings are specific to the specific respondents only. This study presents a relationship between antithetical and creative characteristics of social media and exhibits avenues for future studies by facilitating a better understanding of web-based social network use.

The Impact of Social Media on Learning Behavior for Sustainable Education: Evidence of Students from Selected Universities in Pakistan

Despite the demonstrated importance of customer sentiment in social media for outcomes such as purchase behavior and of firms’ increasing use of customer engagement initiatives, surprisingly few studies have investigated firms’ ability to influence the sentiment of customers’ digital engagement. Many firms track buyers’ offline interactions, design online content to coincide with customers’ experiences, and face varied performance during events, enabling the modification of marketer-generated content to correspond to the event outcomes. This study examines the role of firms’ social media engagement initiatives surrounding customers’ experiential interaction events in influencing the sentiment of customers’ digital engagement. Results indicate that marketers can influence the sentiment of customers’ digital engagement beyond their performance during customers’ interactions, and for unfavorable event outcomes, informational marketer-generated content, more so than emotional content, can enhance customer sentiment. This study also highlights sentiment’s role as a leading indicator for customer lifetime value.

The Role of Marketer-Generated Content in Customer Engagement Marketing:

In this article, I introduce a new package with five commands to perform econometric convergence analysis and club clustering as proposed by Phillips and Sul (2007, Econometrica 75: 1771–1855). The logtreg command performs the log t regression test. The psecta command implements the clustering algorithm to identify convergence clubs. The scheckmerge command conducts the log t regression test for all pairs of adjacent clubs. The imergeclub command tries to iteratively merge adjacent clubs. The pfilter command extracts the trend and cyclical components of a time series of each individual in panel data. I provide an example from Phillips and Sul (2009, Journal of Applied Econometrics 24: 1153–1185) to illustrate the use of these commands. Additionally, I use Monte Carlo simulations to exemplify the effectiveness of the clustering algorithm.

https://journals.sagepub.com/doi/pdf/10.1177/1536867X1801700407

Econometric convergence test and club clustering using Stata

A multi-country convergence analysis of ecological footprint and its components

Summary
Phillips and Sul (Journal of Applied Econometrics 2009, 24, 1153–1185) provide an algorithm to identify convergence clubs in a dynamic factor model of economic transition and growth. We provide a narrow replication of their key results, using the open source R software instead of the original GAUSS routines. We are able to exactly replicate their results on convergence clubs, corresponding point estimates and standard errors. We comment on minor differences between their reported results and their clustering algorithm. We propose simple adjustments of the original algorithm to make manual intervention unnecessary. The adjustments allow automated application of the algorithm to other data. Copyright © 2016 John Wiley & Sons, Ltd.

Economic Transition and Growth: A Replication

User- and marketer-generated content items on social media platforms are supposed to have an impact on economic target variables, such as variables measuring consumers' purchase behavior. The position of each content item – and thus the impact on economic variables – changes with newly appearing items. We propose a hierarchy score to capture the dynamics of the content items on social media platforms. In order to mimic the reduced visibility of earlier content items, our hierarchy score computes the position of content items based on the number of text line equivalents of content items above a particular item. Employing the proposed hierarchy score in a dynamic regression framework for data of a large online store yields improved estimates and predictions compared to a variety of other models.

Dynamic effects of user- and marketer-generated content on consumer purchase behavior: Modeling the hierarchical structure of social media websites

Recently, using mixed data on Canadian housing, Parmeter, Henderson, and Kumbhakar (Journal of Applied Econometrics 2007; 22: 695-699) found that a nonparametric approach for estimating a hedonic house price function is superior to formerly suggested parametric and semipararnetric specifications. We carefully reanalyze these specifications for this dataset by applying a recent nonparametric specification test and simulation-based prediction comparisons. For the case at issue our results suggest that a previously proposed parametric specification does not have to be rejected and we illustrate how nonparametric methods provide valuable insights during all modeling steps. Copyright (C) 2010 John Wiley & Sons, Ltd.

/pdf/on-nonparametric-estimation-of-a-hedonic-price-function-1l9cmxt8wx.pdf

On Nonparametric Estimation of a Hedonic Price Function

We study the estimation of the lag parameter of linear dynamic panel data models with first order dynamics based on the quadratic Ahn and Schmidt (1995) moment conditions. Our contribution is twofold: First, we show that extending the standard assumptions by mean stationarity and time series homoscedasticity and employing these assumptions in estimation restores standard asymptotics and mitigates the non-standard distributions found in the literature. Second, we consider an IV estimator based on the quadratic moment conditions that consistently identifies the true population parameter under standard assumptions. Standard asymptotics hold for the estimator when the cross section dimension is large and the time series dimension is finite. We also suggest a data-driven approach to obtain standard errors and confidence intervals that preserves the time series dependence structure in the data.

https://www.econstor.eu/bitstream/10419/204583/1/167818893X.pdf

Practical aspects of using quadratic moment conditions in linear dynamic panel data models

We propose an instrumental variables (IV) estimator based on nonlinear (in param- eters) moment conditions for estimating linear dynamic panel data models and derive the large sample properties of the estimator. We assume that the only explanatory variable in the model is one lag of the dependent variable and consider the setting where the absolute value of the true lag parameter is smaller or equal to one, the cross section dimension is large, and the time series dimension is either fixed or large. Estimation of the lag parameter involves solving a quadratic equation and we find that the lag parameter is point identified in the unit root case; otherwise, two distinct roots (solutions) result. We propose a selection rule that identifies the consistent root asymptotically in the latter case and derive the asymptotic distribution of the estimator for the unit root case and for the case when the absolute value of the lag parameter is smaller than one.

https://www.econstor.eu/bitstream/10419/204582/1/1678188328.pdf

Large sample properties of an IV estimator based on the Ahn and Schmidt moment conditions

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