Testing Increasing Sensitivity of Enrollment at Private Institutions to Tuition and other Costs

1996 ◽  
Vol 40 (1) ◽  
pp. 40-45 ◽  
Author(s):  
Yu Hsing ◽  
Hui S. Chang
Keyword(s):  
Higher Education ◽  
Time Series ◽  
Functional Form ◽  
Time Series Data ◽  
Series Data ◽  
Standard Errors ◽  
Private Institutions ◽  
Test Statistics ◽  
Sample Period ◽  
Income Elasticities

This paper re-examines the demand for higher education at private institutions and tests if in recent years enrollment has become more sensitive to rising tuition and other related costs. Time series data between FY 1964–65 and FY 1990–91 are used as the sample. Major findings are interesting. The general functional form yields coefficients with smaller standard errors and larger value of the test statistics. The logarithmic form can be rejected at the 5% level. Tuition elasticities rose from −0.261 to −0.557 and income elasticities also increased from 0.493 to 1.093 during the sample period. Thus, enrollment has become more sensitive to changes in tuition and other costs. However, part of the loss of enrollment due to tuition increases can be recovered by rising income elasticities.

scholarly journals The Role of Human Capital Formation in Poverty Mitigation: A Co-integration Analysis from Pakistan

2016 ◽  
Vol 2 (2) ◽  
pp. 129-138
Author(s):  
Romaisa Arif ◽  
Muhammad Zahir Faridi ◽  
Fatima Farooq
Keyword(s):  
Higher Education ◽  
Time Series ◽  
Human Capital ◽  
Infant Mortality ◽  
Time Series Data ◽  
Capital Formation ◽  
Series Data ◽  
Human Capital Formation ◽  
Literacy Rate ◽  
Unit Change

The present study tries to explore the dynamic relationship between human capital formation and poverty mitigation by adopting the course of investment in education and health substances. For this sake, study takes heath expenditure and infant mortality rate as health indicators while status of education is captured with literacy rate and enrollment in higher education. Time series data is employed ranges from 1973-2013. The properties of time series data are inspected with the ADF test whilst PP test is employed for the robustness of unit root results. Mixed order of integration of data compels us to make use of ARDL technique for the estimation. Similarly, one unit change in health expenditures lead to reduce 0.251 units of poverty and one unit change in infant mortality cause to reduce poverty by 0.04 units. In last, one unit increase in literacy rate changes 1.03 units in poverty and one unit change in higher education results in 0.003 unit's change in poverty. The results of the study leave us with a clear finale for an optimal policy formulation that, Pakistan is in sturdy need of investment in health and education substances for a noteworthy accumulation of human capital for a right way poverty mitigation policy.

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.

The Demand for Alcoholic Beverages: An Aggregate Time-Series Analysis

1985 ◽  
Vol 4 (1) ◽  
pp. 47-54 ◽  
Author(s):  
David Levy ◽  
Neil Sheflin
Keyword(s):  
Time Series ◽  
Alcohol Consumption ◽  
Time Series Data ◽  
Income Elasticity ◽  
Alcoholic Beverages ◽  
Series Data ◽  
Sample Period ◽  
Weak Evidence ◽  
Price Effects ◽  
Alternative Measures

We estimate the total demand for alcoholic beverages with annual U. S. time-series data from 1940–80 using two alternative measures of alcohol consumption. By concentrating on the total demand for alcoholic beverages we subsume the cross-price effects. Our results indicate a price elasticity of (minus)0.5 and an income elasticity of 0.4 and weak evidence of a somewhat higher propensity to consume alcoholic beverages by those under 21. After correcting for heteroskedasticity, the estimates are found to be statistically stable over the sample period.

Terrorism and Wine Tourism: The Case of Museum Attendance

2018 ◽  
Vol 13 (4) ◽  
pp. 375-383 ◽  
Author(s):  
Olivier Gergaud ◽  
Florine Livat ◽  
Haiyan Song
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Terrorist Attacks ◽  
Series Data ◽  
Regression Estimation ◽  
Sample Period ◽  
Wine Tourism ◽  
The Impact ◽  
The City ◽  
Jel Classifications

AbstractIn this article, we use attendance data from La Cité du Vin, a wine museum in the city of Bordeaux, to assess the impact of the recent wave of terror that affected France on wine tourism. We use recent count regression estimation techniques suited for time series data to build a prediction model of the demand for attendance at this museum. We conclude that the institution lost about 5,000 visitors over 426 days, during which 14 successive terrorist attacks took place. This corresponds to almost 1% of the total number of visitors in the sample period. (JEL Classifications: L83, Z30)

scholarly journals Comparison of six statistical methods for interrupted time series studies: empirical evaluation of 190 published series

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Simon L. Turner ◽  
Amalia Karahalios ◽  
Andrew B. Forbes ◽  
Monica Taljaard ◽  
Jeremy M. Grimshaw ◽  
...  
Keyword(s):  
Time Series ◽  
Statistical Method ◽  
Statistical Methods ◽  
Time Series Data ◽  
Statistical Significance ◽  
Empirical Evaluation ◽  
Interrupted Time Series ◽  
Series Data ◽  
Standard Errors ◽  
The Impact

Abstract Background The Interrupted Time Series (ITS) is a quasi-experimental design commonly used in public health to evaluate the impact of interventions or exposures. Multiple statistical methods are available to analyse data from ITS studies, but no empirical investigation has examined how the different methods compare when applied to real-world datasets. Methods A random sample of 200 ITS studies identified in a previous methods review were included. Time series data from each of these studies was sought. Each dataset was re-analysed using six statistical methods. Point and confidence interval estimates for level and slope changes, standard errors, p-values and estimates of autocorrelation were compared between methods. Results From the 200 ITS studies, including 230 time series, 190 datasets were obtained. We found that the choice of statistical method can importantly affect the level and slope change point estimates, their standard errors, width of confidence intervals and p-values. Statistical significance (categorised at the 5% level) often differed across the pairwise comparisons of methods, ranging from 4 to 25% disagreement. Estimates of autocorrelation differed depending on the method used and the length of the series. Conclusions The choice of statistical method in ITS studies can lead to substantially different conclusions about the impact of the interruption. Pre-specification of the statistical method is encouraged, and naive conclusions based on statistical significance should be avoided.

scholarly journals DYNAMIC MODELING OF TIME SERIES DATA USING BEKK-GARCH MODEL

2020 ◽  
Vol 17 (36) ◽  
pp. 1186-1198
Author(s):  
Mustofa USMAN ◽  
N INDRYANI ◽  
WARSONO A. ◽  
AMANTO WAMILIANA
Keyword(s):  
Time Series ◽  
Granger Causality ◽  
Impulse Response ◽  
Time Series Data ◽  
Granger Causality Test ◽  
Share Price ◽  
Series Data ◽  
P Value ◽  
Test Statistics ◽  
Causality Test

The Vector Autoregressive Moving Average (VARMA) model is one of the models that is often used in modeling multivariate time series data. In time-series data of economics, especially data return, they usually have high fluctuations in some periods, so the return volatility is unstable. In modeling data return of share prices ADRO and ITMG, the behavior of high volatility will be considered. This study aims to find the best model that fits the data return of share price of the energy companies of PT Adaro Energy Tbk (ADRO) and PT Indo Tambangraya Megah Tbk (ITMG), to analyze the behavior of impulse response of the variables data return ADRO and ITMG, to analyze the granger causality test, and to forecast the next 12 periods. Based on the selection of the best model using the criteria of AICC, HQC, AIC, and SBC, it was found that the VARMA (2.2) -GARCH (1.1) model is the best one for the data in this study. The model VARMA(2,2)-GARCH (1,1) is then written as a univariate model. For the univariate ADRO model, the test statistics F = 4,73 and P-value = 0,0084, which indicates the model is very significant; and for the univariate ITMG model, the test statistics is F = 5,82 and P-value 0,0001, which indicates the model is significant. Based on the best model selected, the impulse response, Granger causality test, and forecasting for the next 12 periods are discussed.

scholarly journals Testing collinearity of vector time series

2019 ◽  
Vol 22 (2) ◽  
pp. 97-116
Author(s):  
Tucker S McElroy ◽  
Agnieszka Jach
Keyword(s):  
Time Series ◽  
Density Matrix ◽  
Spectral Density ◽  
Time Series Data ◽  
Series Data ◽  
Test Statistics ◽  
Rank Reduction ◽  
Spectral Density Matrix ◽  
Schur Complements ◽  
Vector Time Series

Summary We investigate the collinearity of vector time series in the frequency domain, by examining the rank of the spectral density matrix at a given frequency of interest. Rank reduction corresponds to collinearity at the given frequency. When the time series is nonstationary and has been differenced to stationarity, collinearity corresponds to co-integration at a particular frequency. We examine rank through the Schur complements of the spectral density matrix, testing for rank reduction via assessing the positivity of these Schur complements, which are obtained from a nonparametric estimator of the spectral density. New asymptotic results for the test statistics are derived under the fixed bandwidth ratio paradigm; they diverge under the alternative, but under the null hypothesis of collinearity the test statistics converge to a non-standard limiting distribution. Subsampling is used to obtain the limiting null quantiles. A simulation study and an empirical illustration for 6-variate time series data are provided.

scholarly journals The demand for beer in Czech Republic: inderstanding longrun on- and off-trade price elasticities

2017 ◽  
Vol 35 (No. 2) ◽  
pp. 165-170 ◽  
Author(s):  
Grosová Stanislava ◽  
Masár Michal ◽  
Kutnohorská Olga ◽  
Kubeš Vladimír
Keyword(s):  
Time Series ◽  
Czech Republic ◽  
Time Series Data ◽  
Income Elasticity ◽  
Econometric Models ◽  
Series Data ◽  
Empirical Results ◽  
Income Elasticities ◽  
Beer Consumption ◽  
Price And Income Elasticities

We provided estimates of price, cross-price, and income elasticities for on- and off-trade beer consumption using econometric models on time series data from 1994 to 2014. The empirical results indicate that the most important determinants of on-trade demand are the price of off-trade beer, the price of substitutes and past consumption, while the income elasticity was not found to be important. The most important determinants of off-trade beer demand were the price of on-trade beer and the price of substitutes.

scholarly journals Estimation with Aggregate Shocks

2019 ◽  
Vol 87 (3) ◽  
pp. 1365-1398 ◽  
Author(s):  
Jinyong Hahn ◽  
Guido Kuersteiner ◽  
Maurizio Mazzocco
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Model Parameters ◽  
Test Statistics ◽  
Cross Sectional ◽  
Aggregate Shocks ◽  
Combined Use ◽  
The Cross ◽  
Estimation Of Model Parameters

Abstract Aggregate shocks affect most households’ and firms’ decisions. Using three stylized models, we show that inference based on cross-sectional data alone generally fails to correctly account for decision making of rational agents facing aggregate uncertainty. We propose an econometric framework that overcomes these problems by explicitly parameterizing the agents’ decision problem relative to aggregate shocks. Our framework and examples illustrate that the cross-sectional and time-series aspects of the model are often interdependent. Therefore, estimation of model parameters in the presence of aggregate shocks requires the combined use of cross-sectional and time-series data. We provide easy-to-use formulas for test statistics and confidence intervals that account for the interaction between the cross-sectional and time-series variation. Lastly, we perform Monte Carlo simulations that highlight the properties of the proposed method and the risks of not properly accounting for the presence of aggregate shocks.

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