Measurement of the Effects of Regional Policy Instruments by Means of Linear Structural Equation Models and Panel Data

1981 ◽  
Vol 13 (11) ◽  
pp. 1435-1448 ◽  
Author(s):  
H Folmer
Keyword(s):  
Time Series ◽  
Measurement Errors ◽  
Structural Equation ◽  
Regional Policy ◽  
Structural Equation Models ◽  
Time Series Data ◽  
Policy Instruments ◽  
Equation Model ◽  
Series Data ◽  
Cross Sectional

Two kinds of problems have generally been encountered in measuring the effects of instruments of regional policy. The first concerns the construction of an adequate method, which should (a) take explicitly into account the multidimensional character of a regional profile, (b) disentangle policy effects from autonomous developments, (c) make it possible to compare the effects of different policy instruments, and (d) be capable of dealing with theoretical constructs and measurement errors. The second problem relates to the scarcity of regional data, both concerning time-series and cross-sectional data. A linear structural equation model is presented which meets the first set of conditions. This model is then modified in such a way that it can be estimated by pooling cross-sectional and time-series data, thus increasing the number of observations. In addition, as an application the effects of regional industrialization policy on investments in the Netherlands during the period 1973–1976 are estimated.

Loss of Parliamentary Control Due to Mediatization and Europeanization: A Longitudinal and Cross-Sectional Analysis of Agenda Building in the United Kingdom and the Netherlands

2008 ◽  
Vol 38 (3) ◽  
pp. 455-478 ◽  
Author(s):  
LONNEKE VAN NOIJE ◽  
JAN KLEINNIJENHUIS ◽  
DIRK OEGEMA
Keyword(s):  
Structural Equation ◽  
Structural Equation Models ◽  
Time Series Data ◽  
Series Data ◽  
Political Agenda ◽  
Agenda Building ◽  
Cross Sectional ◽  
Parliamentary Debates ◽  
Media Agenda ◽  
The Media

The central question in this study is whether the power of the media agenda over the political agenda has recently increased. The agenda-building dynamics are established using cross-country time-series data on four issues, covering fifteen and eight years respectively of British and Dutch parliamentary debates and newspaper articles. Structural equation models show that the parliamentary agenda is more influenced by the media agenda than the other way around, and that the power balance has shifted even more in favour of the media. It is additionally found that media power is especially associated with issues within the European domain. This study contributes empirically to the 'mediatization' debate in a EU context, which is largely limited to the realm of theoretical speculation.

Measurement of the Effects of Regional Policy Instruments

1980 ◽  
Vol 12 (10) ◽  
pp. 1191-1202 ◽  
Author(s):  
H Folmer
Keyword(s):  
Structural Equation Model ◽  
Measurement Errors ◽  
Structural Equation ◽  
Regional Policy ◽  
Policy Instruments ◽  
Equation Model ◽  
Policy Effects ◽  
Methodological Problems

In the literature on measuring of the effects of regional policy four methodological problems can be distinguished: First, handling the multidimensional nature of the regional profile; second, separation of policy effects from autonomous developments; third, comparison of different policy instruments; and fourth, handling theoretical constructs and measurement errors. In this paper a linear structural equation model is formulated that overcomes these problems. The paper concludes with a case study of the situation in Holland during the period 1974–1975.

An easy way to create duration variables in binary cross-sectional time-series data

2020 ◽  
Vol 20 (4) ◽  
pp. 916-930
Author(s):  
Andrew Q. Philips
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Time Series Data ◽  
Series Data ◽  
Duration Dependence ◽  
Cross Sectional ◽  
Common Solution ◽  
The Common

In cross-sectional time-series data with a dichotomous dependent variable, failing to account for duration dependence when it exists can lead to faulty inferences. A common solution is to include duration dummies, polynomials, or splines to proxy for duration dependence. Because creating these is not easy for the common practitioner, I introduce a new command, mkduration, that is a straightforward way to generate a duration variable for binary cross-sectional time-series data in Stata. mkduration can handle various forms of missing data and allows the duration variable to easily be turned into common parametric and nonparametric approximations.

Influence of Inclination Angle on Intermittent Two Phase Flows

2016 ◽  
Author(s):  
Josep Escrig Escrig ◽  
Buddhika Hewakandamby ◽  
Georgios Dimitrakis ◽  
Barry Azzopardi
Keyword(s):  
Time Series ◽  
Void Fraction ◽  
Time Series Data ◽  
Silicone Oil ◽  
Wire Mesh ◽  
Series Data ◽  
Intermittent Flow ◽  
Two Phase ◽  
Cross Sectional ◽  
Inclined Pipes

Intermittent gas and liquid two-phase flow was generated in a 6 m × 67 mm diameter pipe mounted rotatable frame (vertical up to −20°). Air and a 5 mPa s silicone oil at atmospheric pressure were studied. Gas superficial velocities between 0.17 and 2.9 m/s and liquid superficial velocities between 0.023 and 0.47 m/s were employed. These runs were repeated at 7 angles making a total of 420 runs. Cross sectional void fraction time series were measured over 60 seconds for each run using a Wire Mesh Sensor and a twin plane Electrical Capacitance Tomography. The void fraction time series data were analysed in order to extract average void fraction, structure velocities and structure frequencies. Results are presented to illustrate the effect of the angle as well as the phase superficial velocities affect the intermittent flows behaviour. Existing correlations suggested to predict average void fraction and gas structures velocity and frequency in slug flow have been compared with new experimental results for any intermittent flow including: slug, cap bubble and churn. Good agreements have been seen for the gas structure velocity and mean void fraction. On the other hand, no correlation was found to predict the gas structure frequency, especially in vertical and inclined pipes.

Analyzing Longitudinal Social Relations Model Data Using the Social Relations Structural Equation Model

2021 ◽  
pp. 107699862110565
Author(s):  
Steffen Nestler ◽  
Oliver Lüdtke ◽  
Alexander Robitzsch
Keyword(s):  
Social Relations ◽  
Structural Equation ◽  
Applied Research ◽  
Structural Equation Models ◽  
Social Groups ◽  
Equation Model ◽  
Model Data ◽  
Cross Sectional ◽  
Social Relations Model ◽  
The Social

The social relations model (SRM) is very often used in psychology to examine the components, determinants, and consequences of interpersonal judgments and behaviors that arise in social groups. The standard SRM was developed to analyze cross-sectional data. Based on a recently suggested integration of the SRM with structural equation models (SEM) framework, we show here how longitudinal SRM data can be analyzed using the SR-SEM. Two examples are presented to illustrate the model, and we also present the results of a small simulation study comparing the SR-SEM approach to a two-step approach. Altogether, the SR-SEM has a number of advantages compared to earlier suggestions for analyzing longitudinal SRM data, making it extremely useful for applied research.

scholarly journals The Effect of the Global Financial Crisis on the Level of Accounting Conservatism in Commercial Banks: Evidence from Jordan

2017 ◽  
Vol 12 (2) ◽  
pp. 151 ◽  
Author(s):  
Yusuf Ali Al-Hroot ◽  
Laith Akram Muflih AL-Qudah ◽  
Faris Irsheid Audeh Alkharabsha
Keyword(s):  
Time Series ◽  
Financial Crisis ◽  
Commercial Banks ◽  
Time Series Data ◽  
Global Financial Crisis ◽  
Ordinary Least Squares ◽  
Accounting Conservatism ◽  
Series Data ◽  
Cross Sectional ◽  
The Global Financial Crisis

This paper intends to investigate whether the financial crisis (2008) exerted an impact on the level of accounting conservatism in the case of Jordanian commercial banks before and during the financial crisis. The sample of this study includes 78 observations; these observations are based on the financial statements of all commercial banks in Jordan and may be referred to as cross-sectional data, whereas the period from 2005 to 2011 represents a range of years characterized by time series data. The appropriate regression model to measure the relationship between cross-sectional data and time series data is in this case the pooled data regression (PDR) using the ordinary least squares (OLS) method. The results indicate that the level of accounting conservatism had been steadily increasing over a period of three years from 2005 to 2007. The results also indicate that the level of accounting conservatism was subjected to an increase during crisis period between 2009 and 2011 compared with the level of accounting conservatism for the period 2005-2007 preceding the global financial crisis. The F-test was used in order to test the significant differences between the regression coefficients for the period before and during the global financial crisis. The results indicate a positive impact on the accounting conservatism during the global financial crisis compared with the period before the global financial crisis. The p-value is 0.040 which indicates that there are statistically significant differences between the two periods; these results are consistent with the results in Sampaio (2015).

Time Series Analysis in Pooled Cross-Sections

1986 ◽  
Vol 2 (3) ◽  
pp. 331-349 ◽  
Author(s):  
John J. Beggs
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Cross Sections ◽  
Spectral Methods ◽  
Time Series Data ◽  
Series Data ◽  
Cross Sectional ◽  
Series Analysis ◽  
Simulation Results ◽  
Spectral Representations

This article proposes the use of spectral methods to pool cross-sectional replications (N) of time series data (T) for time series analysis. Spectral representations readily suggest a weighting scheme to pool the data. The asymptotically desirable properties of the resulting estimators seem to translate satisfactorily into samples as small as T = 25 with N = 5. Simulation results, Monte Carlo results, and an empirical example help confirm this finding. The article concludes that there are many empirical situations where spectral methods canbe used where they were previously eschewed.

Applying Circular Statistics to the Analysis of Monitoring Data

2007 ◽  
Vol 23 (4) ◽  
pp. 227-237 ◽  
Author(s):  
Thomas Kubiak ◽  
Cornelia Jonas
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Ambulatory Monitoring ◽  
Circular Data ◽  
Circular Statistics ◽  
Series Data ◽  
Cross Sectional ◽  
Psychological Variables ◽  
Wide Range ◽  
Time Series Approach

Abstract. Patterns of psychological variables in time have been of interest to research from the beginning. This is particularly true for ambulatory monitoring research, where large (cross-sectional) time-series datasets are often the matter of investigation. Common methods for identifying cyclic variations include spectral analyses of time-series data or time-domain based strategies, which also allow for modeling cyclic components. Though the prerequisites of these sophisticated procedures, such as interval-scaled time-series variables, are seldom met, their usage is common. In contrast to the time-series approach, methods from a different field of statistics, directional or circular statistics, offer another opportunity for the detection of patterns in time, where fewer prerequisites have to be met. These approaches are commonly used in biology or geostatistics. They offer a wide range of analytical strategies to examine “circular data,” i.e., data where period of measurement is rotationally invariant (e.g., directions on the compass or daily hours ranging from 0 to 24, 24 being the same as 0). In psychology, however, circular statistics are hardly known at all. In the present paper, we intend to give a succinct introduction into the rationale of circular statistics and describe how this approach can be used for the detection of patterns in time, contrasting it with time-series analysis. We report data from a monitoring study, where mood and social interactions were assessed for 4 weeks in order to illustrate the use of circular statistics. Both the results of periodogram analyses and circular statistics-based results are reported. Advantages and possible pitfalls of the circular statistics approach are highlighted concluding that ambulatory assessment research can benefit from strategies borrowed from circular statistics.

Sign in / Sign up

Export Citation Format

Share Document