A Moving Cross-Section Analysis of Demand for Toothpaste

1970 ◽  
Vol 7 (4) ◽  
pp. 439-449 ◽  
Author(s):  
Kristian S. Palda ◽  
Larry M. Blair
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Least Squares ◽  
Cross Section ◽  
Single Equation ◽  
Cross Sectional ◽  
Least Squares Estimates ◽  
Section Analysis

MRCA panel data on toothpaste expenditures are used to demonstrate how time series and cross-sectional bias can be eliminated by the method of covariance regression from single-equation demand least squares estimates.

scholarly journals Applications of Generalized Method of Moments Estimation

2001 ◽  
Vol 15 (4) ◽  
pp. 87-100 ◽  
Author(s):  
Jeffrey M Wooldridge
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Least Squares ◽  
Cross Section ◽  
Method Of Moments ◽  
Generalized Method Of Moments ◽  
Ordinary Least Squares ◽  
Estimation Problems ◽  
Method Of Moments Estimation ◽  
Moments Estimators

I describe how the method of moments approach to estimation, including the more recent generalized method of moments (GMM) theory, can be applied to problems using cross section, time series, and panel data. Method of moments estimators can be attractive because in many circumstances they are robust to failures of auxiliary distributional assumptions that are not needed to identify key parameters. I conclude that while sophisticated GMM estimators are indispensable for complicated estimation problems, it seems unlikely that GMM will provide convincing improvements over ordinary least squares and two-stage least squares--by far the most common method of moments estimators used in econometrics--in settings faced most often by empirical researchers.

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 The Effect of Foreign Portfolio Equity Purchases on Security Returns in Kenya: Evidence from NSE Listed Financial Institutions

2017 ◽  
Vol 9 (4) ◽  
pp. 202
Author(s):  
Loice Koskei
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Stock Returns ◽  
Financial Institutions ◽  
Sampling Technique ◽  
Ordinary Least Squares ◽  
Capital Requirements ◽  
Cross Sectional ◽  
International Portfolio ◽  
Security Returns

Foreign portfolio inflows increase the liquidity and the volume of finance available for financial institutions. At the same time, as foreign portfolio inflows finances in part the capital requirements of local companies, it can also increase the competitiveness of these companies. A huge surge of the inflows can be very inflationary because this forces the Central Bank of Kenya to expand the country’s monetary base by releasing counterpart domestic currency which eventually feeds into the inflationary process. The main aim of this study was to find out the effect of international portfolio equity purchases on security returns of listed financial institutions in Kenya. The study population was 21 financial institutions listed on the Nairobi Securities Exchange. Using purposive sampling technique the study concentrated on 14 financial institutions. The research design of the study was causal as it is concerned more with understanding the connection between cause and effect relationships. The study adopted panel data regression using the Ordinary Least Squares (OLS) method where the data included time series and cross-sectional. A unit root test was carried in this study to examine stationarity of variables because it used panel data which combined both cross-sectional and time series information. Panel estimation results indicated that international portfolio equity purchases have no effect on stock returns of listed financial institutions in Kenya. The study recommended implementation of regulations and policies that would attract foreign portfolio equity inflows in financial institutions.

Nuisance vs. Substance: Specifying and Estimating Time-Series-Cross-Section Models

1996 ◽  
Vol 6 ◽  
pp. 1-36 ◽  
Author(s):  
Nathaniel Beck ◽  
Jonathan N. Katz
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Cross Section ◽  
Generalized Least Squares ◽  
Ordinary Least Squares ◽  
Correlated Errors ◽  
Section Data ◽  
Variable Approach ◽  
Parameter Heterogeneity ◽  
Lagged Dependent Variable

In a previous article we showed that ordinary least squares with panel corrected standard errors is superior to the Parks generalized least squares approach to the estimation of time-series-cross-section models. In this article we compare our proposed method with another leading technique, Kmenta's “cross-sectionally heteroskedastic and timewise autocorrelated” model. This estimator uses generalized least squares to correct for both panel heteroskedasticity and temporally correlated errors. We argue that it is best to model dynamics via a lagged dependent variable rather than via serially correlated errors. The lagged dependent variable approach makes it easier for researchers to examine dynamics and allows for natural generalizations in a manner that the serially correlated errors approach does not. We also show that the generalized least squares correction for panel heteroskedasticity is, in general, no improvement over ordinary least squares and is, in the presence of parameter heterogeneity, inferior to it. In the conclusion we present a unified method for analyzing time-series-cross-section data.

scholarly journals PERKEMBANGAN INTERMEDIASI BPR DI WILAYAH EKS KARESIDENAN BANYUMAS DAN FAKTOR-FAKTOR YANG MEMENGARUHINYA PERIODE TAHUN 2005-2011

2018 ◽  
Vol 6 (4) ◽  
pp. 52
Author(s):  
Sheila Ardilla Yughi
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Cross Section

ABSTRAKPenelitian ini berjudul “Perkembangan Intermediasi BPR di Wilayah Eks-Karesidenan Banyumas dan Faktor-Faktor yang Memengaruhinya Periode Tahun 2005-2011”. Tujuan dari penelitian ini adalah untuk mengetahui perkembanganan Loan to Deposit Ratio (LDR) pada Bank Perkreditan Rakyat (BPR) di wilayah Eks-Karesidenan Banyumas yang menunjukkan fungsi intermediasi BPR di wilayah Eks-Karesidenan Banyumas, untuk mengetahui pengaruh variabel BI rate, deposito, Produk Domestik Regional Bruto (PDRB), dan Non Performing Loan (NPL) baik secara bersama-sama maupun secara parsial terhadap intermediasi Bank Perkreditan Rakyat (BPR) di wilayah Eks-Karesidean Banyumas, dan untuk mengetahui variabel manakah yang paling berpengaruh besar terhadap intermediasi Bank Perkreditan Rakyat (BPR) di wilayah Eks-Karesidenan Banyumas.Metode penelitian yang digunakan adalah metode analisis kuantitatif menggunakan analisis trend dan analisis regresi non linier berganda data panel. Data yang digunakan merupakan data panel yaitu gabungan dari data time series dan data cross section, dan data yang digunakan merupakan data tahunan dari tahun 2005 sampai dengan tahun 2011 sehingga jumlah data sebanyak 28 pengamatan.Berdasarkan hasil uji regresi analisis data trend, perkembangan intermediasi yang dilihat dari besaran Loan to Deposit Ratio (LDR) menunjukkan perkembangan yang terus meningkat setiap tahunnya dan besaran LDR tersebut rata-rata sebesar 122 persen setiap tahunnya. Sedangkan berdasarkan hasil uji regresi linier berganda pada variabel dependen Loan to Deposit Ratio (LDR) BPR di wilayah Eks-Karesidenan Banyumas dapat diketahui variabel BI rate, deposito, Produk Domestik Regional Bruto (PDRB), dan Non Performing Loan (NPL) secara bersama-sama berpengaruh signifikan terhadap intermediasi BPR di wilayah Eks-Karesidenan Banyumas. Namun, secara parsial hanya variabel PDRB bernilai positif dan signifikan terhadap intermediasi BPR di wilayah Eks-Karesidenan Banyumas,dan variabel deposito berpengaruhsignifikan, namun bernilai negatif terhadap intermediasi BPR di wilayah Eks-Karesidenan Banyumas. Kata Kunci: Intermediasi, BPR, Eks-Karesidenan Banyumas.

scholarly journals Pemodelan Regresi Panel pada Data Pendapatan Asli Daerah (PAD) Terhadap Dana Alokasi Umum (DAU)

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Rezzy Eko Caraka
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Cross Section

Data panel adalah gabungan dari data time series (antar waktu) dan data cross section (antar individu/ ruang). Untuk menggambarkan panel data secara singkat, misalkan pada data cross section, nilai dari satu variabel atau lebih dikumpulkan untuk beberapa unit sampel pada suatu waktu waktu. Dalam panel data, unit cross section yang sama di-survey dalam beberapa waktu.Regresi data panel digunakan untuk menentukan model regresi yang paling sesuai digunakan untuk memodelkan pendapatan asli daerah (PAD) terhadap dana alokasi umum (DAU) untuk tujuh kabupaten/kota provinsi Jawa Tengah anggaran 2008-2010. Model yang dihasilkan dengan REM didapat nilai R2 sebesar 43,8893% Pendapatan Asli Daerah (PAD) dipengaruhi oleh Dana Alokasi Umum (DAU), sedangkan sisanya dipengaruhi oleh faktor lain.

scholarly journals The Effect of Foreign Portfolio Equity Sales on Stock Returns in Kenya: Evidence from NSE Listed Financial Institutions

2017 ◽  
Vol 9 (4) ◽  
pp. 185
Author(s):  
Loice Koskei
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Stock Returns ◽  
Financial Institutions ◽  
Unit Root Test ◽  
Sampling Technique ◽  
Ordinary Least Squares ◽  
Cross Sectional ◽  
High Volatility ◽  
Study Population

Fluctuations of foreign portfolio equity intensify risk and unpredictability in financial institutions leading to high volatility. The main aim of this study was to find out the effect of foreign portfolio equity outflows on stock returns of listed financial institutions in Kenya. The study population was 21 financial institutions listed on the Nairobi Securities Exchange. Using purposive sampling technique the study concentrated on 14 financial institutions. The research design of the study was causal as it is concerned more with understanding the connection between cause and effect relationships. The study adopted panel data regression using the Ordinary Least Squares (OLS) method where the data included time series and cross-sectional. A unit root test was carried in this study to examine stationarity of variables because it used panel data which combined both cross-sectional and time series information. Panel estimation results indicated that foreign portfolio equity outflows have no effect on stock returns of listed financial institutions in Kenya. The study recommended implementation of policies that would curb foreign portfolio outflows in financial institutions in order to minimize reversals of foreign portfolio investments. 

scholarly journals Forecasting using cross-section average–augmented time series regressions

2020 ◽  
Author(s):  
Hande Karabiyik ◽  
Joakim Westerlund
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Regression Models ◽  
Common Factor ◽  
Principal Component ◽  
Small Sample ◽  
Interactive Effects ◽  
Cross Sectional ◽  
Second Development ◽  
The Cross

Summary There is a large and growing body of literature concerned with forecasting time series variables by the use of factor-augmented regression models. The workhorse of this literature is a two-step approach in which the factors are first estimated by applying the principal components method to a large panel of variables, and the forecast regression is then estimated, conditional on the first-step factor estimates. Another stream of research that has attracted much attention is concerned with the use of cross-section averages as common factor estimates in interactive effects panel regression models. The main justification for this second development is the simplicity and good performance of the cross-section averages when compared with estimated principal component factors. In view of this, it is quite surprising that no one has yet considered the use of cross-section averages for forecasting. Indeed, given the purpose to forecast the conditional mean, the use of the cross-sectional average to estimate the factors is only natural. The present paper can be seen as a reaction to this. The purpose is to investigate the asymptotic and small-sample properties of forecasts based on cross-section average–augmented regressions. In contrast to most existing studies, the investigation is carried out while allowing the number of factors to be unknown.

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