scholarly journals Determinants of Exports of Pakistan: A Country-wise Disaggregated Analysis.

2011 ◽  
Vol 50 (4II) ◽  
pp. 715-732 ◽  
Author(s):  
Naseeb Zada ◽  
Malik Muhammad ◽  
Khan Bahadar
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Export Performance ◽  
Small Samples ◽  
Series Data ◽  
Supply Side ◽  
Empirical Bayesian ◽  
Demand And Supply ◽  
Price And Income Elasticities ◽  
Trade Partners

Given the importance of international trade and export performance in economic growth, this study attempts to examine the determinants of exports of Pakistan, using a time series data over the period 1975-2008. A simultaneous equation approach is followed and the demand and supply side equations are specified with appropriate variables. This is a country-wise disaggregated analysis of Pakistan versus its trade partners and the estimation strategy is based on two approaches. First we employ the Generalised Methods of Moments (GMM), which is followed by the Empirical Bayesian technique to get consistent estimates. The GMM technique is believed to be efficient for time series data provided the sample size is sufficiently large. In case of small samples, the estimates might not be precise and might appear with unbelievable sign and insignificant magnitudes. To avoid the sample bias and other problems, we employ the Empirical Bayesian technique which provides much precise estimates. The factual results obtained via the GMM technique are a little bit mixed, although most of the coefficients are found to be statistically significant and carry their expected signs. In order to compare and validate these results, the Empirical Bayesian technique is employed. This offers considerable improvement over the previous results and all the variables are found to be highly significant with correct sign across the countries concerned with the exception of a few cases. The price and income elasticities in both the demand and supply side equations carry their expected signs and significant magnitudes for the trading partners. The findings suggest that exports of Pakistan are much sensitive to changes in the world demand and world prices. This establishes the importance of demand side factors like world GDP, Real exchange rate, and world prices to determine the exports of Pakistan. On the supply side, we find relatively small price and income elasiticities. The results reveal that demand for exports is relatively higher for countries in NAFTA, European Union and Middle East regions. The study recommends particular concentration on the trade partners in these regions to improve the export performance of Pakistan. Keywords: Exports, GMM, Empirical Bayesian Method, Pakistan

scholarly journals An improved framework to predict river flow time series data

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7183 ◽  
Author(s):  
Hafiza Mamona Nazir ◽  
Ijaz Hussain ◽  
Ishfaq Ahmad ◽  
Muhammad Faisal ◽  
Ibrahim M. Almanjahie
Keyword(s):  
Time Series ◽  
Water Resource Management ◽  
River Flow ◽  
Time Series Data ◽  
Flow Time ◽  
Indus Basin ◽  
Series Data ◽  
Percentage Error ◽  
Empirical Bayesian ◽  
Noise Characteristics

Due to non-stationary and noise characteristics of river flow time series data, some pre-processing methods are adopted to address the multi-scale and noise complexity. In this paper, we proposed an improved framework comprising Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Empirical Bayesian Threshold (CEEMDAN-EBT). The CEEMDAN-EBT is employed to decompose non-stationary river flow time series data into Intrinsic Mode Functions (IMFs). The derived IMFs are divided into two parts; noise-dominant IMFs and noise-free IMFs. Firstly, the noise-dominant IMFs are denoised using empirical Bayesian threshold to integrate the noises and sparsities of IMFs. Secondly, the denoised IMF’s and noise free IMF’s are further used as inputs in data-driven and simple stochastic models respectively to predict the river flow time series data. Finally, the predicted IMF’s are aggregated to get the final prediction. The proposed framework is illustrated by using four rivers of the Indus Basin System. The prediction performance is compared with Mean Square Error, Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Our proposed method, CEEMDAN-EBT-MM, produced the smallest MAPE for all four case studies as compared with other methods. This suggests that our proposed hybrid model can be used as an efficient tool for providing the reliable prediction of non-stationary and noisy time series data to policymakers such as for planning power generation and water resource management.

scholarly journals The Role of Cooperatives, Remittances, and Infrastructure in Export Performance of Nepal: ARDL Approach of Cointegration

2020 ◽  
pp. 151-159
Author(s):  
Ramesh C. Paudel ◽  
Chakrapani Acharya ◽  
Resham Thapa-Parajuli
Keyword(s):  
Time Series Data ◽  
Export Performance ◽  
Series Data ◽  
Supply Side ◽  
Policy Makers ◽  
Ardl Approach ◽  
Negative Role ◽  
Supply Side Factors

Cooperatives, remittances, and foreign direct investment (FDI) are crucial source of funds required for better entrepreneurships, which combinedly along with the quality of infrastructure can contribute to enhance the supply side factors of the export performance. Due to the well perceived role of cooperatives, Nepal’s constitution 2015 mentions this sector as one of the three pillars of the national economy while around 30 percent of Nepal’s GDP comes from remittances. As the country lacks the domestic sources for investment, FDI has become an indispensable part of the development sources of the developing countries in the recent decades. This paper analyzes the role of cooperatives, remittances, FDI and infrastructure in export performance of Nepal using the Autoregressive Distributive Lag (ARDL) approach of cointegration as suggested by the properties of the time series data for the period of 26 years from 1993 to 2018. The major finding shows that the cooperatives have not contributed to export performance as expected, however the role is positive. The remittances have a strong negative role on export performance, which is largely impacted by the number and quality of the infrastructure. The role of FDI is also negative and might be due to insufficient volume to contribute substantially. This fact seeks the urgent attention from the policy makers to make the country more investment friendly.

The transition probabilities for inflation episodes in Ghana

2018 ◽  
Vol 13 (6) ◽  
pp. 2028-2046
Author(s):  
Philip Kofi Adom ◽  
Mawunyo Prosper Agradi ◽  
Christopher Quaidoo
Keyword(s):  
Time Series Data ◽  
Transition Probabilities ◽  
Supply And Demand ◽  
Series Data ◽  
Supply Side ◽  
Demand And Supply ◽  
Content Type ◽  
High Inflation ◽  
Annual Time Series ◽  
Supply Side Factors

Purpose Following the reforms in monetary policy and shift in fiscal policies, it is logical to presume that these reforms may cause a significant structural change in the dynamic processes of inflation and hence affect the nature of inflation persistence. The purpose of this paper is to examine the persistence nature of the different inflation episodes while controlling for the effects of demand- and supply-side factors, which are modeled as regime-dependent. Design/methodology/approach This paper used the Markov-switching dynamic regression and annual time series data. Findings The results showed that high inflation regime is more persistent than low inflation regime, with a respective average duration of an escape of 3.5 and 2.57 years, which suggests that price stability achievements are less sustainable. In both regimes, demand- and supply-side factors play significant roles in driving inflation, but the effect of the latter dominates. Thus, on the argument of whether inflation in Ghana is structural or monetary, the results support the former. The roles of both structural and monetary factors have changed over time, but that of the former has been more significant and important in Ghana. Originality/value This study provides the first empirical attempt, in the case of Ghana, that examines the persistence nature of different inflation regimes, while modeling the effects of supply and demand factors as regime dependent. In the modeling sense, the authors also contribute by ruling out the assumption that the researcher knows the processes responsible for each observation at each point in time.

scholarly journals ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PERMINTAAN TENAGA LISTRIK KONSUMEN SEKTOR KONSUMTIF PERUSAHAAN LISTRIK NEGARA

2017 ◽  
Vol 14 (3) ◽  
pp. 330
Author(s):  
Aminullah Assagaf
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Explanatory Variable ◽  
Negative Impact ◽  
Data Distribution ◽  
Electricity Demand ◽  
Series Data ◽  
Analysis Model ◽  
Demand And Supply ◽  
Independent Variable

This research aims to analyse electricity demand, and focus for consumptive sector in PT Perusahaan listrik Negara (Persero) or PT PLN (Persero). While selected by consumptive sector is some region in Jawa Bali and otuside Jawa Bali. Step of research and process result based on SPSS calculation, and use time series data year 1995 - 2009. As for used analysis model follow its data distribution that is the non linear regression model being based on Ln with dependent variable is demand electricity or kWh sales, and independent variable consist of install capacity, average tariff, and rate of capacity using percustomers. Obtain result that install capacity and rate of capacity using percustomers have given positif impact, and average tariff have given negative impact. All of that independent variable have significant influence, and install capacity variable most its influence significant to electricity demand of consumptive sector. PLN’s management has to observe growth of explanatory variable to make policy for demand and supply equilibrium and toward customers satisfaction.

scholarly journals Data Requirements for Model-Based Cancer Prognosis Prediction

2015 ◽  
Vol 14s5 ◽  
pp. CIN.S30801 ◽  
Author(s):  
Lori A. Dalton ◽  
Mohammadmahdi R. Yousefi
Keyword(s):  
Time Series ◽  
Probability Distribution ◽  
Time Series Data ◽  
Population Data ◽  
Patient Data ◽  
Bayesian Regression ◽  
Cancer Prognosis ◽  
Small Samples ◽  
Series Data ◽  
Prognosis Prediction

Cancer prognosis prediction is typically carried out without integrating scientific knowledge available on genomic pathways, the effect of drugs on cell dynamics, or modeling mutations in the population. Recent work addresses some of these problems by formulating an uncertainty class of Boolean regulatory models for abnormal gene regulation, assigning prognosis scores to each network based on intervention outcomes, and partitioning networks in the uncertainty class into prognosis classes based on these scores. For a new patient, the probability distribution of the prognosis class was evaluated using optimal Bayesian classification, given patient data. It was assumed that (1) disease is the result of several mutations of a known healthy network and that these mutations and their probability distribution in the population are known and (2) only a single snapshot of the patient's gene activity profile is observed. It was shown that, even in ideal settings where cancer in the population and the effect of a drug are fully modeled, a single static measurement is typically not sufficient. Here, we study what measurements are sufficient to predict prognosis. In particular, we relax assumption (1) by addressing how population data may be used to estimate network probabilities, and extend assumption (2) to include static and time-series measurements of both population and patient data. Furthermore, we extend the prediction of prognosis classes to optimal Bayesian regression of prognosis metrics. Even when time-series data is preferable to infer a stochastic dynamical network, we show that static data can be superior for prognosis prediction when constrained to small samples. Furthermore, although population data is helpful, performance is not sensitive to inaccuracies in the estimated network probabilities.

scholarly journals ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PERMINTAAN TENAGA LISTRIK KONSUMEN SEKTOR KONSUMTIF PERUSAHAAN LISTRIK NEGARA

2018 ◽  
Vol 14 (3) ◽  
pp. 330-349
Author(s):  
Aminullah Assagaf
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Explanatory Variable ◽  
Negative Impact ◽  
Data Distribution ◽  
Electricity Demand ◽  
Series Data ◽  
Analysis Model ◽  
Demand And Supply ◽  
Independent Variable

This research aims to analyse electricity demand, and focus for consumptive sector in PT Perusahaan listrik Negara (Persero) or PT PLN (Persero). While selected by consumptive sector is some region in Jawa Bali and otuside Jawa Bali. Step of research and process result based on SPSS calculation, and use time series data year 1995 - 2009. As for used analysis model follow its data distribution that is the non linear regression model being based on Ln with dependent variable is demand electricity or kWh sales, and independent variable consist of install capacity, average tariff, and rate of capacity using percustomers. Obtain result that install capacity and rate of capacity using percustomers have given positif impact, and average tariff have given negative impact. All of that independent variable have significant influence, and install capacity variable most its influence significant to electricity demand of consumptive sector. PLN’s management has to observe growth of explanatory variable to make policy for demand and supply equilibrium and toward customers satisfaction. 

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.

Parameter Selection of SVR Based on Improved K-Fold Cross Validation

2013 ◽  
Vol 462-463 ◽  
pp. 182-186 ◽  
Author(s):  
Ju E Wang ◽  
Jian Zhong Qiao
Keyword(s):  
Time Series ◽  
Cross Validation ◽  
Time Series Data ◽  
Parameter Selection ◽  
Small Samples ◽  
Series Data ◽  
Support Vector ◽  
Model Parameters ◽  
Fold Cross Validation ◽  
Selection Of

This article firstly uses svm to forecast cashmere price time series. The forecasting result mainly depends on parameter selection. The normal parameter selection is based on k-fold cross validation. The k-fold cross validation is suitable for classification. In this essay, k-fold cross validation is improved to ensure that only the older data can be used to forecast latter data to improve prediction accuracy. This essay trains the cashmere price time series data to build mathematical model based on SVM. The selection of the model parameters are based on improved cross validation. The price of Cashmere can be forecasted by the model. The simulation results show that support vector machine has higher fitting precision in the situation of small samples. It is feasible to forecast cashmere price based on SVM.

Gasoline and Travel Demand Models Using Time Series and Cross-Section Data from United States

1996 ◽  
Vol 1558 (1) ◽  
pp. 83-89 ◽  
Author(s):  
Paul Schimek
Keyword(s):  
United States ◽  
Time Series ◽  
Travel Demand ◽  
Time Series Data ◽  
Fuel Efficiency ◽  
Rebound Effect ◽  
The United States ◽  
Series Data ◽  
Price And Income Elasticities ◽  
Demand Models

The price and income elasticities of highway gasoline and automobile travel demand are useful for forecasting gasoline tax revenues and highway investment needs and evaluating policies to reduce automobile use, improve fuel efficiency, or reduce greenhouse gas emissions. Gasoline and travel demand elasticities are calculated using 1950 to 1994 time series data for the United States and 1988 to 1992 pooled data for states of the United States. Gasoline demand was found to be price inelastic in the short run, but in the long run, it was found to be —0.7. Even in the United States, gasoline price has a significant impact on gasoline use. The response to price changes is divided among driving, fuel efficiency, and the size of the vehicle stock, although the latter is the smallest. The Corporate Average Fuel Economy (CAFE) program was found to be associated with an average 1 percent annual decline in per capita fuel consumption. The elasticity of driving with respect to fuel efficiency— the rebound effect—was found to be —0.3, confirming previous results. The state-level data produce inconclusive results; it is hypothesized that this is the result of the confounding effect of CAFE.

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