WHY PANEL DATA?

2005 ◽  
Vol 50 (02) ◽  
pp. 143-154 ◽  
Author(s):  
CHENG HSIAO
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Cross Section ◽  
Human Behavior ◽  
Time Series Data ◽  
Data Modeling ◽  
Data Availability ◽  
Series Data ◽  
Single Cross Section ◽  
Single Cross

We explain the proliferation of panel data studies in terms of (i) data availability; (ii) the heightened capacity for modeling the complexity of human behavior than a single cross-section or time series data can possibly allow; and (iii) challenging methodology. Advantages and issues of panel data modeling are also discussed.

scholarly journals Pengaruh Dana Desa Dan Alokasi Dana Gampong Terhadap Kemiskinan Di Kecamatan Makmur Kabupaten Bireuen

2020 ◽  
Vol 3 (1) ◽  
pp. 11
Author(s):  
Aida Fitri ◽  
Khairil Anwar
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Panel Data ◽  
Cross Section ◽  
Time Series Data ◽  
Panel Data Analysis ◽  
Series Data ◽  
Analysis Method ◽  
Fund Allocation ◽  
Data Analysis Method

This study aims to determine how much Influence funds and village fund allocation have on poverty in Makmur District, Bireuen Regency. This study uses the panel data analysis method. Which is a combination of time-series data from 2015 to 2019, and a cross-section involving 27 villages and results in 135 observations. The results show that village funds have a negative and significant effect on poverty in the Makmur sub-district. Meanwhile, the allocation of village fund has no significant effect on poverty in the Makmur sub-district.Keywords:Village Fund, VillageFund Allocation, Poverty.

scholarly journals GRANGER CAUSALITY AND STRUCTURAL CAUSALITY IN CROSS-SECTION AND PANEL DATA

2016 ◽  
Vol 33 (2) ◽  
pp. 263-291 ◽  
Author(s):  
Xun Lu ◽  
Liangjun Su ◽  
Halbert White
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Cross Section ◽  
Conditional Independence ◽  
Time Series Data ◽  
Structural Heterogeneity ◽  
Series Data ◽  
Direct Link ◽  
Structural Effects ◽  
Definition Of

Granger noncausality in distribution is fundamentally a probabilistic conditional independence notion that can be applied not only to time series data but also to cross-section and panel data. In this paper, we provide a natural definition of structural causality in cross-section and panel data and forge a direct link between Granger (G–) causality and structural causality under a key conditional exogeneity assumption. To put it simply, when structural effects are well defined and identifiable,G–non-causality follows from structural noncausality, and with suitable conditions (e.g., separability or monotonicity), structural causality also impliesG–causality. This justifies using tests ofG–non-causality to test for structural noncausality under the key conditional exogeneity assumption for both cross-section and panel data. We pay special attention to heterogeneous populations, allowing both structural heterogeneity and distributional heterogeneity. Most of our results are obtained for the general case, without assuming linearity, monotonicity in observables or unobservables, or separability between observed and unobserved variables in the structural relations.

scholarly journals Fuzzy panel data analysis

2021 ◽  
Vol 48 (3) ◽  
Author(s):  
Muhammet O. Yalçin ◽  
◽  
Nevin Güler Dincer ◽  
Serdar Demir ◽  
◽  
...  
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Panel Data ◽  
Cross Section ◽  
Time Series Data ◽  
Panel Data Analysis ◽  
Series Data ◽  
Percentage Error ◽  
Cross Sectional ◽  
Statistical Assumptions

In statistical and econometric researches, three types of data are mostly used as cross-section, time series and panel data. Cross-section data are obtained by collecting the observations related to the same variables of many units at constant time. Time series data are data type consisted of observations measured at successive time points for single unit. Sometimes, the number of observations in cross-sectional or time series data is insufficient for carrying out the statistical or econometric analysis. In that cases, panel data obtained by combining cross-section and time series data are often used. Panel data analysis (PDA) has some advantages such as increasing the number of observations and freedom degree, decreasing of multicollinearity, and obtaining more efficient and consistent predictions results with more data information. However, PDA requires to satisfy some statistical assumptions such as “heteroscedasticity”, “autocorrelation”, “correlation between units”, and “stationarity”. It is too difficult to hold these assumptions in real-time applications. In this study, fuzzy panel data analysis (FPDA) is proposed in order to overcome these drawbacks of PDA. FPDA is based on predicting the parameters of panel data regression as triangular fuzzy number. In order to validate the performance of efficiency of FPDA, FPDA, and PDA are applied to panel data consisted of gross domestic production data from five country groups between the years of 2005-2013 and the prediction performances of them are compared by using three criteria such mean absolute percentage error, root mean square error, and variance accounted for. All analyses are performed in R 3.5.2. As a result of analysis, it is observed that FPDA is an efficient and practical method, especially in case required statistical assumptions are not satisfied.

scholarly journals Import Flows of Bangladesh: Gravity Model Approach under Panel Data Methodology

2012 ◽  
Vol 60 (2) ◽  
pp. 153-157 ◽  
Author(s):  
Mili Roy ◽  
Md. Israt Rayhan
Keyword(s):  
Panel Data ◽  
Gravity Model ◽  
Cross Section ◽  
Time Series Data ◽  
Random Effect ◽  
Least Square ◽  
Series Data ◽  
Ordinary Least Square ◽  
Extended Gravity ◽  
Trading Partners

In counterpoint to export growth, Bangladesh import growth has remained much less strong, despite impressive progress in import liberalization. This study gives an overview of different methodologies related to gravity model analysis in Bangladesh’s import flow. A pooled cross section and time series data were analyzed to incorporate the country specific heterogeneity in country pair trading partners. The import flows are justified by the basic gravity model since Bangladesh’s imports are positively significant by the economy size and inversely related to trade barrier. Accordingly, we have analyzed pooled ordinary least square, fixed effect, random effect. This study also explores extended gravity model using several variables in the light of gravity model panel data approach. Bangladesh’s import is determined by the home and foreign country’s gross domestic product and exchange rate. In addition, Cross section results show that regional trade arrangement which is South Asian Association for Regional Co-operation and border are significant for Bangladesh’s importimplies that Bangladesh should import more from intra regional country and also should import from India.DOI: http://dx.doi.org/10.3329/dujs.v60i2.11485 Dhaka Univ. J. Sci. 60(2): 153-157, 2012 (July)  

scholarly journals ESTIMATION OF AUTOREGRESSIVE ROOTS NEAR UNITY USING PANEL DATA

2000 ◽  
Vol 16 (6) ◽  
pp. 927-997 ◽  
Author(s):  
Hyungsik R. Moon ◽  
Peter C.B. Phillips
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Time Series Data ◽  
Initial Conditions ◽  
Series Data ◽  
Test Statistics ◽  
Rate Condition ◽  
Panel Models ◽  
Stochastic Trends ◽  
Efficient Extraction

Time series data are often well modeled by using the device of an autoregressive root that is local to unity. Unfortunately, the localizing parameter (c) is not consistently estimable using existing time series econometric techniques and the lack of a consistent estimator complicates inference. This paper develops procedures for the estimation of a common localizing parameter using panel data. Pooling information across individuals in a panel aids the identification and estimation of the localizing parameter and leads to consistent estimation in simple panel models. However, in the important case of models with concomitant deterministic trends, it is shown that pooled panel estimators of the localizing parameter are asymptotically biased. Some techniques are developed to overcome this difficulty, and consistent estimators of c in the region c < 0 are developed for panel models with deterministic and stochastic trends. A limit distribution theory is also established, and test statistics are constructed for exploring interesting hypotheses, such as the equivalence of local to unity parameters across subgroups of the population. The methods are applied to the empirically important problem of the efficient extraction of deterministic trends. They are also shown to deliver consistent estimates of distancing parameters in nonstationary panel models where the initial conditions are in the distant past. In the development of the asymptotic theory this paper makes use of both sequential and joint limit approaches. An important limitation in the operation of the joint asymptotics that is sometimes needed in our development is the rate condition n/T → 0. So the results in the paper are likely to be most relevant in panels where T is large and n is moderately large.

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