scholarly journals Multiple Imputation for Missing Values with an Empirical Application

2021 ◽  
pp. 1-18
Author(s):  
Theodora Sotiropoulou ◽  
Stefanos Giakoumatos ◽  
Antonios Georgopoulos
Keyword(s):  
Multiple Imputation ◽  
Financial Development ◽  
Missing Values ◽  
Time Series Data ◽  
Trade Openness ◽  
Banking System ◽  
Series Data ◽  
Diagnostic Tools ◽  
The European Union ◽  
Economic Data

Abstract Missing data are the most common problem in many research areas. For cross-section and time-series data, imputation can be a challenging problem. The most widely used method for filling missing observations is the multiple imputation which increase the number of the available data and thereby reducing biases that may occur when observations with missing values are simply deleted. The main purpose of this paper is to employ a bootstrapping expectation–maximization (EM) algorithm in order to impute missing values mainly to economic data. In the application we use a dataset that is consisted by annual panel data for the 27 countries of the European Union covering the period 2000-2017. The data were obtained from the databases of World Bank and Eurostat namely the Global Financial Development Database, The Standardized World Income Inequality Database by Solt (2019) and the World Development Indicators. Different indicators were chosen representing the development of banking system and stock markets, economic growth, economic inequality, innovation, fiscal policy, physical and human capital, and trade openness. Finally, diagnostic tools are used inspecting the imputations that are created. Keywords: Multiple imputation, Amelia II, Economic data, Financial development, Inequality.

scholarly journals The Effect of Inflation on Financial Development Indicators in Iran (2000–2015)

2017 ◽  
Vol 12 (2) ◽  
pp. 53-62 ◽  
Author(s):  
Mahyar Hami
Keyword(s):  
Financial Development ◽  
Time Series Data ◽  
Banking System ◽  
Series Data ◽  
Vector Error Correction ◽  
Development Indicators ◽  
Vector Error ◽  
Equilibrium Relationship ◽  
Proposed Model ◽  
Integration Test

Abstract Inflation and financial development are among the factors that influence economic growth and the interaction between them is a major issue in developing countries. The aim of this paper is to investigate the effect of inflation on financial development indicators in Iran using seasonal data over 2000-2015. To achieve the research objectives, time series data were collected from World Bank and seasonal inflation rate, with 5 financial development indicators were used to measure the research variables. Then I applied Johansen Co-integration Test and Vector Error Correction Model to estimate the proposed model. The results show that inflation has a negatively significant effect on financial depth and also positively significant effect on the ratio of total deposits in banking system to nominal GDP in Iran during the observation period. Also the existence of an equilibrium relationship between inflation and other 3 indicators of Iran`s financial development used in this study was rejected.

scholarly journals Analyzing the relationship between foreign direct investment and privatization in the European Union Founder Nations by using panel data approach

2018 ◽  
Vol 65 (5) ◽  
pp. 587-607
Author(s):  
Selim Tüzüntürk ◽  
Betül İnam ◽  
Filiz Giray
Keyword(s):  
European Union ◽  
Foreign Direct Investment ◽  
Panel Data ◽  
Direct Investment ◽  
Time Series Data ◽  
Trade Openness ◽  
Series Data ◽  
The European Union ◽  
Behavioral Models ◽  
Explanatory Variables

Foreign direct investment (FDI) and privatization are two of the most important components in liberalization World. The aim of this study is to analyze whether there exist a statistically significant relationship between FDI and privatization, or not. To do so, a panel data sample of fourteen European Union (EU) Founder Nations in 1998-2012 was used to estimate various panel data models. The special feature of panel data is that it allows researchers to construct and test more realistic behavioral models that could not be identified using crosssection or time series data alone. Based on the sample results, between privatization as the primary independent variable and FDI was found a statistically significant positive relationship. Although other explanatory variables such as growth, trade openness, and corruption perceptions index, were found to have statistically significant effects on FDI, budget deficit was found to be statistically insignificant. Moreover, statistically significant parameters? signs showed that all of the economic expectations were satisfied.

scholarly journals An Empirical Mode-Spatial Model for Environmental Data Imputation

Hydrology ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 63 ◽  
Author(s):  
Benjamin Nelsen ◽  
D. Williams ◽  
Gustavious Williams ◽  
Candace Berrett
Keyword(s):  
Time Series ◽  
Spatial Data ◽  
Missing Values ◽  
Time Series Data ◽  
Environmental Data ◽  
Series Data ◽  
Data Imputation ◽  
Accurate Data ◽  
Target Station ◽  
Periodic Components

Complete and accurate data are necessary for analyzing and understanding trends in time-series datasets; however, many of the available time-series datasets have gaps that affect the analysis, especially in the earth sciences. As most available data have missing values, researchers use various interpolation methods or ad hoc approaches to data imputation. Since the analysis based on inaccurate data can lead to inaccurate conclusions, more accurate data imputation methods can provide accurate analysis. We present a spatial-temporal data imputation method using Empirical Mode Decomposition (EMD) based on spatial correlations. We call this method EMD-spatial data imputation or EMD-SDI. Though this method is applicable to other time-series data sets, here we demonstrate the method using temperature data. The EMD algorithm decomposes data into periodic components called intrinsic mode functions (IMF) and exactly reconstructs the original signal by summing these IMFs. EMD-SDI initially decomposes the data from the target station and other stations in the region into IMFs. EMD-SDI evaluates each IMF from the target station in turn and selects the IMF from other stations in the region with periodic behavior most correlated to target IMF. EMD-SDI then replaces a section of missing data in the target station IMF with the section from the most closely correlated IMF from the regional stations. We found that EMD-SDI selects the IMFs used for reconstruction from different stations throughout the region, not necessarily the station closest in the geographic sense. EMD-SDI accurately filled data gaps from 3 months to 5 years in length in our tests and favorably compares to a simple temporal method. EMD-SDI leverages regional correlation and the fact that different stations can be subject to different periodic behaviors. In addition to data imputation, the EMD-SDI method provides IMFs that can be used to better understand regional correlations and processes.

Effects of Infrastructure Development on Foreign Direct Investment in Kenya

2016 ◽  
Vol 8 (2) ◽  
pp. 93-110 ◽  
Author(s):  
Carol Teresa Wekesa ◽  
Nelson H. Wawire ◽  
George Kosimbei
Keyword(s):  
Foreign Direct Investment ◽  
Direct Investment ◽  
Time Series Data ◽  
Trade Openness ◽  
Transport Infrastructure ◽  
Series Data ◽  
Infrastructure Development ◽  
Fdi Inflows ◽  
Year 2000 ◽  
Quality Infrastructure

Kenya’s foreign direct investment (FDI) inflows as a percentage of GDP have been increasing negligibly over the last 4 years, increasing from 0.4 per cent in 2010 to 0.9 per cent in 2013. And yet evidence shows that quality infrastructure lowers the cost of doing business and thus attracts FDI. Kenya has visible signs of infrastructure inadequacy and inefficiencies despite the fact that since the year 2000, there has been increased budgetary allocation to the infrastructure sector. This study, therefore, sought to determine the effects of transport, energy, communication and water and waste infrastructure development on FDI inflows in Kenya. The study used annual time series data sourced from Central Bank of Kenya, World Bank and the United Nations Conference on Trade and Development (UNCTAD). Using multiple regression analysis, it was established that improved transport infrastructure, communication infrastructure, water and waste infrastructure, exchange rate, economic growth and trade openness are important determinants of FDI inflows into Kenya. Hence, for Kenya to attract more FDI, continued infrastructural development is key since quality infrastructure affords investors a conducive investment climate in which to operate.

scholarly journals Consequences of Capital formation, Trade Liberalization on the Economic Growth: Pakistan’s Experience

2015 ◽  
Vol 2 (1) ◽  
pp. 1-4
Author(s):  
Nadia Bukhari ◽  
Anjum Iqbal
Keyword(s):  
Economic Growth ◽  
Time Series Data ◽  
Trade Openness ◽  
Capital Formation ◽  
Vital Role ◽  
Series Data ◽  
Distributed Lag ◽  
Long Run ◽  
Auto Regressive ◽  
Adf Test

This study considers the long run relationship between the liberalization of trade, capital formation and the economic growth of Pakistan by using the time series data from 1975-2013. The main aim of this study is to examine that how much liberalization of trade and capital formation affects the economic growth of Pakistan in long run. The approach that has been used for empirical analysis is Auto Regressive Distributed Lag (ARDL) model. Under the ADF test capital formation (CF) is stationary at its first level but the trade openness (TO) and GDP is stationary at its first difference. Moreover, the granger casualty test is evident that there become a casual relationship between the trade openness and GDP. The result of this study shows that both the trade openness and the capital formation determined the economic growth in long run and they both have statistically significant effect on the GDP. Furthermore it has has been depicted from the study that the trade has a vital role to influence the economic growth.

scholarly journals An Examination Into the Causal Links Among Inward FDI Determinants: Empirical Evidence From Jordan

2021 ◽  
Vol 12 (2) ◽  
pp. 195
Author(s):  
Mohamed Ibrahim Mugableh
Keyword(s):  
Gross Domestic Product ◽  
Time Series Data ◽  
Trade Openness ◽  
Vital Role ◽  
Domestic Product ◽  
Series Data ◽  
Foreign Direct Investments ◽  
Vector Error Correction ◽  
Long Span ◽  
Foreign Policies

This paper examined the causal links between inward foreign direct investments (FDI) and its determinants (i.e., gross domestic product, education, trade openness, infrastructure, and technological abilities) for Jordan over (the period 1980 – 2018). The paper used vector error correction model. The results of the study considered that gross domestic product, trade openness, education, infrastructure, and technological abilities are primary engine of inward FDI in (long term and short term). Thus, the results have vital role for the policy makers in Jordan to formulate domestic and foreign policies. This study relied on three essential parts. Firstly, FDI is a significant source of capital that promotes economic growth. Secondly, the question of what are the leading drivers of FDI remains inadequate in the literature. Finally, this research adds to the literature by using different econometrics techniques and long span of yearly time series data. 

scholarly journals Islamic Finance and Economic Growth Nexus: An Econometric Analysis

2019 ◽  
Vol 2 (1) ◽  
pp. 11-22
Author(s):  
Kashif Raza ◽  
Rashid Ahmad ◽  
Muhammad Abdul Rehman Shah ◽  
Muhammad Umar
Keyword(s):  
Economic Growth ◽  
Time Series Data ◽  
Trade Openness ◽  
Unit Root Test ◽  
Least Square ◽  
Series Data ◽  
Islamic Finance ◽  
Causality Test ◽  
Economic Agents ◽  
Fixed Capital Formation

Researchers have written chain of research papers about the dynamics of financial development and economic growth. The financial capital plays a productive role when it delivers to economic agents who are facing shortage or excess of funds.  This study explores the linkages among Islamic financing and economic growth for Pakistan, by using annual time series data from 2005-2018. Islamic banks’ financing funds used as a proxy of Islamic financing, Gross Domestic Product (GDP), Gross Fixed Capital Formation (GFCF), labor force (LF),Broad money(M) and Trade openness (TO) to presents real sector of an economy. For the exploration, the unit root test, Ordinary least square technique and Granger causality test are applied. The results validate a substantial causal relationship of Islamic financing and GDP, which supports the Schumpeter’s supply-leading view. The results indicate that Islamic finance contributed towards economic growth.  

scholarly journals Savings Mobilization and Financial Development during the Multicurrency Regime Period in Zimbabwe

2017 ◽  
Vol 9 (3(J)) ◽  
pp. 152-162
Author(s):  
Kunofiwa Tsaurai
Keyword(s):  
Financial Development ◽  
Time Series Data ◽  
Empirical Literature ◽  
Series Data ◽  
Correction Model ◽  
Vector Error Correction ◽  
Vector Error ◽  
Long Run ◽  
The Subject ◽  
The Relationship

This paper seeks to investigate the relationship between savings and financial development in Zimbabwe using both autoregressive distributive lag (ARDL) and vector error correction model (VECM) approaches for comparison purposes with monthly time series data from January 2009 to August 2015. Four distinct hypotheses emerged from the literature and these are the savings-led financial development, financial development-led savings, feedback effect and the insignificant/no relationship hypothesis. The existence of diverging and contradicting views in empirical literature on the subject matter is evidence that the linkage between savings and financial development is still far from being concluded. Both F-Bounds and Johansen co-integration tests observed that there is a long run relationship between savings and financial development in Zimbabwe. What is even more unique about this study is that both ARDL and VECM noted the presence of a bi-directional causality relationship between savings and financial development in the short and long run in Zimbabwe. The implication of this study is that in order to increase economic growth, Zimbabwe authorities should increase savings mobilization efforts in order to boost financial development, which in turn attracts more savings inflow into the formal financial system.

A Sectoral Analysis of the Role of Stock Market Development on Economic Growth: Empirical Evidence from Indian Economy

2017 ◽  
Vol 18 (4) ◽  
pp. 911-923 ◽  
Author(s):  
Madhu Sehrawat ◽  
A.K. Giri
Keyword(s):  
Economic Growth ◽  
Stock Market ◽  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Trade Openness ◽  
Series Data ◽  
Causality Test ◽  
Long Run ◽  
Short Run

The present study examines the relationship between Indian stock market and economic growth from a sectoral perspective using quarterly time-series data from 2003:Q4 to 2014:Q4. The results of the autoregressive distributed lag (ARDL) approach bounds test confirm the existence of a cointegrating relationship between sector-specific gross domestic product (GDP) and sector-specific stock indices. The empirical results reveal that sector-specific economic growth are significantly influenced by changes in the respective sector-specific stock price indices in the long run as well as in the short run. Apart from that, the control variables, such as trade openness and inflation, act as the instrument variables in explaining the variations in the sector-specific GDP of the economy. The results of Granger causality test demonstrate unidirectional long-run as well as short-run causality running from sector specific stock prices to respective sector GDP. The findings suggest that economic growth of the country is sensitive to respective sub-sector stock market investments. The findings highlight the reasons for cyclical and counter-cyclical business phase for the overall economy.

scholarly journals Time Series Imputation via L1 Norm-Based Singular Spectrum Analysis

2018 ◽  
Vol 17 (02) ◽  
pp. 1850017 ◽  
Author(s):  
Mahdi Kalantari ◽  
Masoud Yarmohammadi ◽  
Hossein Hassani ◽  
Emmanuel Sirimal Silva
Keyword(s):  
Time Series ◽  
Spectrum Analysis ◽  
Missing Values ◽  
Time Series Data ◽  
Singular Spectrum Analysis ◽  
Series Data ◽  
L1 Norm ◽  
Nonparametric Approach ◽  
Singular Spectrum ◽  
Simulated Time

Missing values in time series data is a well-known and important problem which many researchers have studied extensively in various fields. In this paper, a new nonparametric approach for missing value imputation in time series is proposed. The main novelty of this research is applying the [Formula: see text] norm-based version of Singular Spectrum Analysis (SSA), namely [Formula: see text]-SSA which is robust against outliers. The performance of the new imputation method has been compared with many other established methods. The comparison is done by applying them to various real and simulated time series. The obtained results confirm that the SSA-based methods, especially [Formula: see text]-SSA can provide better imputation in comparison to other methods.

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