scholarly journals Sur l’estimation des équations de CANDIDE-R

2009 ◽  
Vol 51 (4) ◽  
pp. 626-633
Author(s):  
Alban D’Amours
Keyword(s):  
Time Series ◽  
Cross Sections ◽  
Economic Model ◽  
Time Series Data ◽  
Series Data ◽  
Necessary Condition ◽  
Regional Data ◽  
Regional Models ◽  
The Core ◽  
Error Components Models

Abstract CANDIDE-R is a huge simultaneous macro-economic model which raises estimations difficulties. We avoid the problem of identification assuming that the great number of variables in our model makes it impossible that the necessary condition be not satisfied. We assume that our system converges to a solution solving this way the problem of identification. The core of the paper gives justifications of the procedure we adopted to estimate CANDIDE-R. Because of the presence of regional equations and the limited amount of regional data, we are bound to pool cross sections and time series data. We then justified the use of Zellner's approach instead of the error components models within the class of regional models built on national premises.

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.

Forecasting the Impact of Socioeconomic and Demographic Change on Product Demand

1985 ◽  
Vol 22 (4) ◽  
pp. 415-423 ◽  
Author(s):  
John M. Mccann ◽  
David J. Reibstein
Keyword(s):  
Time Series ◽  
Cross Sections ◽  
Time Series Data ◽  
Series Data ◽  
Cross Sectional ◽  
Product Demand ◽  
New Methods ◽  
Differential Scheme ◽  
The Impact ◽  
The U.S

The U.S. population is expected to undergo significant shifts in its demographic and socioeconomic makeup. The authors present a series of methods for estimating the impact of these shifts on product demand. In addition, two new methods for pooling time-series and cross-sectional data are presented. One method combines disaggregate cross sectional data with aggregate time-series data and the second method involves a differential scheme for pooling cross sections for each variable in the model.

A Solution for Absent Spatial Data: The Common Correlated Effects Estimator

2020 ◽  
pp. 016001762095913
Author(s):  
Michael Beenstock ◽  
Daniel Felsenstein
Keyword(s):  
Time Series ◽  
Spatial Data ◽  
Time Series Data ◽  
Housing Stock ◽  
National Level ◽  
Series Data ◽  
Data Series ◽  
Regional Data ◽  
Common Correlated Effects ◽  
The Common

Informed regional policy needs good regional data. As regional data series for key economic variables are generally absent whereas national-level time series data for the same variables are ubiquitous, we suggest an approach that leverages this advantage. We hypothesize the existence of a pervasive “common factor” represented by the national time series that affects regions differentially. We provide an empirical illustration in which national FDI is used in place of panel data for FDI, which are absent. The proposed methodology is tested empirically with respect to the determinants of regional demand for housing. We use a quasi-experimental approach to compare the results of a “common correlated effects” (CCE) estimator with a benchmark case when absent regional data are omitted. Using three common factors relating to national population, income and housing stock, we find mixed support for the common correlated effects hypothesis. We conclude by discussing how our experimental design may serve as a methodological prototype for further tests of CCE as a solution to the absent spatial data problem.

scholarly journals ANALISIS PENGARUH INDUSTRI PARIWISATA TERHADAP PENYERAPAN TENAGA KERJA DI INDONESIA

2019 ◽  
Vol 7 (2) ◽  
pp. 155-159
Author(s):  
Annisa Nur Pita ◽  
Saiqa Ilham Akbar
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Cross Sections ◽  
Time Series Data ◽  
Secondary Data ◽  
Fixed Effect Model ◽  
Variable Number ◽  
Tourism Industry ◽  
Series Data ◽  
Tourism Sector

In 2017, the growth of the tourism sector in Indonesia ranked the ninth highest in the world (WTTC, 2018). Growth in the tourism sector also has an impact on employment in this sector. This study aims to estimate the effect of the development of the tourism industry on employment in the tourism sector and how much influence. The data used is secondary data from BPS. The form of data is panel data consisting of time series data and cross sections. The time series data is in 2010-2016 while the cross section data consists of 34 provinces in Indonesia. The analytical tool used is regression with panel data. The results of the fixed effect model panel regression can be seen that the probability value of each independent variable is less than the critical value of 5% (0.05). Then it can be concluded that the variable Number of Star Hotels, Number of Non-Star Hotels, Number of Domestic Tourists and Number of Foreign Tourists has a significant and positive effect on the Labor variable in Indonesia. The more the number of star hotels, the number of non-star hotels, the number of domestic tourists and the number of foreign tourists, the higher the absorption of the workforce in the tourism sector.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data
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