ECONOMETRICS CONFERENCE


Econometrics Conference is one of the leading research topics in the international research conference domain. Econometrics is a conference track under the Economics Conference which aims to bring together leading academic scientists, researchers and research scholars to exchange and share their experiences and research results on all aspects of Economics.

internationalconference.net provides a premier interdisciplinary platform for researchers, practitioners and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of (Economics).

Econometrics is not just a call for academic papers on the topic; it can also include a conference, event, symposium, scientific meeting, academic, or workshop.

You are welcome to SUBMIT your research paper or manuscript to Econometrics Conference Track will be held at .

Econometrics is also a leading research topic on Google Scholar, Semantic Scholar, Zenedo, OpenAIRE, BASE, WorldCAT, Sherpa/RoMEO, Elsevier, Scopus, Web of Science.

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I. INTERNATIONAL ECONOMICS CONFERENCE

MARCH 19 - 20, 2019
ISTANBUL, TURKEY

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II. INTERNATIONAL ECONOMICS CONFERENCE

JUNE 26 - 27, 2019
PARIS, FRANCE

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III. INTERNATIONAL ECONOMICS CONFERENCE

AUGUST 21 - 22, 2019
LONDON, UNITED KINGDOM

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IV. INTERNATIONAL ECONOMICS CONFERENCE

OCTOBER 08 - 09, 2019
NEW YORK, UNITED STATES

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V. INTERNATIONAL ECONOMICS CONFERENCE

DECEMBER 12 - 13, 2019
ROME, ITALY

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VI. INTERNATIONAL ECONOMICS CONFERENCE

FEBRUARY 13 - 14, 2020
LONDON, UNITED KINGDOM

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VII. INTERNATIONAL ECONOMICS CONFERENCE

APRIL 15 - 16, 2020
BARCELONA, SPAIN

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VIII. INTERNATIONAL ECONOMICS CONFERENCE

MAY 11 - 12, 2020
ISTANBUL, TURKEY

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IX. INTERNATIONAL ECONOMICS CONFERENCE

JUNE 05 - 06, 2020
SAN FRANCISCO, UNITED STATES

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X. INTERNATIONAL ECONOMICS CONFERENCE

JULY 20 - 21, 2020
PARIS, FRANCE

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XI. INTERNATIONAL ECONOMICS CONFERENCE

AUGUST 10 - 11, 2020
NEW YORK, UNITED STATES

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XII. INTERNATIONAL ECONOMICS CONFERENCE

SEPTEMBER 10 - 11, 2020
TOKYO, JAPAN

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XIII. INTERNATIONAL ECONOMICS CONFERENCE

SEPTEMBER 16 - 17, 2020
ZÜRICH, SWITZERLAND

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XIV. INTERNATIONAL ECONOMICS CONFERENCE

OCTOBER 21 - 22, 2020
BARCELONA, SPAIN

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XV. INTERNATIONAL ECONOMICS CONFERENCE

NOVEMBER 02 - 03, 2020
SAN FRANCISCO, UNITED STATES

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XVI. INTERNATIONAL ECONOMICS CONFERENCE

NOVEMBER 12 - 13, 2020
ISTANBUL, TURKEY

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XVII. INTERNATIONAL ECONOMICS CONFERENCE

NOVEMBER 19 - 20, 2020
SINGAPORE, SINGAPORE

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XVIII. INTERNATIONAL ECONOMICS CONFERENCE

DECEMBER 15 - 16, 2020
BANGKOK, THAILAND

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XIX. INTERNATIONAL ECONOMICS CONFERENCE

DECEMBER 28 - 29, 2020
PARIS, FRANCE

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XX. INTERNATIONAL ECONOMICS CONFERENCE

FEBRUARY 13 - 14, 2021
LONDON, UNITED KINGDOM

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XXI. INTERNATIONAL ECONOMICS CONFERENCE

APRIL 15 - 16, 2021
BARCELONA, SPAIN

FINISHED

XXII. INTERNATIONAL ECONOMICS CONFERENCE

MAY 11 - 12, 2021
ISTANBUL, TURKEY

FINISHED

XXIII. INTERNATIONAL ECONOMICS CONFERENCE

JUNE 05 - 06, 2021
SAN FRANCISCO, UNITED STATES

FINISHED

XXIV. INTERNATIONAL ECONOMICS CONFERENCE

JULY 20 - 21, 2021
PARIS, FRANCE

FINISHED

XXV. INTERNATIONAL ECONOMICS CONFERENCE

AUGUST 10 - 11, 2021
NEW YORK, UNITED STATES

FINISHED

XXVI. INTERNATIONAL ECONOMICS CONFERENCE

SEPTEMBER 10 - 11, 2021
TOKYO, JAPAN

FINISHED

XXVII. INTERNATIONAL ECONOMICS CONFERENCE

SEPTEMBER 16 - 17, 2021
ZÜRICH, SWITZERLAND

FINISHED

XXVIII. INTERNATIONAL ECONOMICS CONFERENCE

OCTOBER 21 - 22, 2021
BARCELONA, SPAIN

FINISHED

XXIX. INTERNATIONAL ECONOMICS CONFERENCE

NOVEMBER 02 - 03, 2021
SAN FRANCISCO, UNITED STATES

FINISHED

XXX. INTERNATIONAL ECONOMICS CONFERENCE

NOVEMBER 12 - 13, 2021
ISTANBUL, TURKEY

FINISHED

XXXI. INTERNATIONAL ECONOMICS CONFERENCE

NOVEMBER 19 - 20, 2021
SINGAPORE, SINGAPORE

FINISHED

XXXII. INTERNATIONAL ECONOMICS CONFERENCE

DECEMBER 15 - 16, 2021
BANGKOK, THAILAND

FINISHED

XXXIII. INTERNATIONAL ECONOMICS CONFERENCE

DECEMBER 28 - 29, 2021
PARIS, FRANCE

Economics Conference Call For Papers are listed below:

Previously Published Papers on "Econometrics Conference"

  • Trade Policy Incentives and Economic Growth in Nigeria
    Authors: Emmanuel Dele Balogun, Keywords: Trade Policies, macroeconomic incentives, total factor productivity and economic growth. DOI:10.5281/zenodo.1124899 Abstract: This paper analyzes, using descriptive statistics and econometrics data which span the period 1981 to 2014 to gauge the effects of trade policy incentives on economic growth in Nigeria. It argues that the provided incentives penalize economic growth during pre-trade liberalization eras, but stimulated a rapid increase in total factor productivity during the post-liberalization period of 2000 to 2014. The trend analysis shows that Nigeria maintained high tariff walls in economic regulation eras which became low in post liberalization era. The protections were in favor of infant industries, which were mainly appendages of multinationals but against imports of competing food and finished consumer products. The trade openness index confirms the undue exposure of Nigeria’s economy to the vagaries of international market shocks; while banking sector recapitalization and new listing of telecommunications companies deepened the financial markets in post-liberalization era. The structure of economic incentives was biased in favor of construction, trade and services, but against the real sector despite protectionist policies. Total Factor Productivity (TFP) estimates show that the Nigerian economy suffered stagnation in pre-liberalization eras, but experienced rapid growth rates in post-liberalization eras. The regression results relating trade policy incentives to TFP growth rate yielded a significant but negative intercept suggesting that a non-interventionist policy could be detrimental to economic progress, while protective tariff which limits imports of competing products could spur productivity gains in domestic import substitutes beyond factor growth with market liberalization. The main constraint to the effectiveness of trade policy incentives is the failure of benefiting industries to leverage on the domestic factor endowments of the nation. This paper concludes that there is the need to review the current economic transformation strategies urgently with a view to provide policymakers with a better understanding of the most viable options that could make for rapid success.
  • Spatial Econometric Approaches for Count Data: An Overview and New Directions
    Authors: Paula Simões, Isabel Natário, Keywords: Spatial data analysis, spatial econometrics, Bayesian hierarchical models, count data. DOI:10.5281/zenodo.1111729 Abstract: This paper reviews a number of theoretical aspects for implementing an explicit spatial perspective in econometrics for modelling non-continuous data, in general, and count data, in particular. It provides an overview of the several spatial econometric approaches that are available to model data that are collected with reference to location in space, from the classical spatial econometrics approaches to the recent developments on spatial econometrics to model count data, in a Bayesian hierarchical setting. Considerable attention is paid to the inferential framework, necessary for structural consistent spatial econometric count models, incorporating spatial lag autocorrelation, to the corresponding estimation and testing procedures for different assumptions, to the constrains and implications embedded in the various specifications in the literature. This review combines insights from the classical spatial econometrics literature as well as from hierarchical modeling and analysis of spatial data, in order to look for new possible directions on the processing of count data, in a spatial hierarchical Bayesian econometric context.
  • The Link between Money Market and Economic Growth in Nigeria: Vector Error Correction Model Approach
    Authors: Ehigiamusoe, Uyi Kizito, Keywords: Economic Growth, Investments, Money Market, Money Market Challenges, Money Market Instruments. DOI:10.5281/zenodo.1089283 Abstract: The paper examines the impact of money market on economic growth in Nigeria using data for the period 1980-2012. Econometrics techniques such as Ordinary Least Squares Method, Johanson’s Co-integration Test and Vector Error Correction Model were used to examine both the long-run and short-run relationship. Evidence from the study suggest that though a long-run relationship exists between money market and economic growth, but the present state of the Nigerian money market is significantly and negatively related to economic growth. The link between the money market and the real sector of the economy remains very weak. This implies that the market is not yet developed enough to produce the needed growth that will propel the Nigerian economy because of several challenges. It was therefore recommended that government should create the appropriate macroeconomic policies, legal framework and sustain the present reforms with a view to developing the market so as to promote productive activities, investments, and ultimately economic growth.
  • Dynamic Interrelationship among the Stock Markets of India, Pakistan and United States
    Authors: A. Iqbal, N. Khalid, S. Rafiq, Keywords: Causality, Cointegration, India, Pakistan, Stock Markets, US. DOI:10.5281/zenodo.1055879 Abstract: The interrelationship between international stock markets has been a key study area among the financial market researchers for international portfolio management and risk measurement. The characteristics of security returns and their dynamics play a vital role in the financial market theory. This study is an attempt to find out the dynamic linkages among the equity market of USA and emerging markets of Pakistan and India using daily data covering the period of January 2003–December 2009. The study utilizes Johansen (Journal of Economic Dynamics and Control, 12, 1988) and Johansen and Juselius (Oxford Bulletin of Economics and Statistics, 52, 1990) cointegration procedure for long run relationship and Granger-causality tests based on Toda and Yamamoto (Journal of Econometrics, 66, 1995) methodology. No cointegration was found among stock markets of USA, Pakistan and India, while Granger-causality test showed the evidence of unidirectional causality running from New York stock exchange to Bombay and Karachi stock exchanges.
  • Forecasting Stock Price Manipulation in Capital Market
    Authors: F. Rahnamay Roodposhti, M. Falah Shams, H. Kordlouie, Keywords: Price Manipulation, Liquidity, Size of Company,Floating Stock, Information Clarity DOI:10.5281/zenodo.1329717 Abstract: The aim of the article is extending and developing econometrics and network structure based methods which are able to distinguish price manipulation in Tehran stock exchange. The principal goal of the present study is to offer model for approximating price manipulation in Tehran stock exchange. In order to do so by applying separation method a sample consisting of 397 companies accepted at Tehran stock exchange were selected and information related to their price and volume of trades during years 2001 until 2009 were collected and then through performing runs test, skewness test and duration correlative test the selected companies were divided into 2 sets of manipulated and non manipulated companies. In the next stage by investigating cumulative return process and volume of trades in manipulated companies, the date of starting price manipulation was specified and in this way the logit model, artificial neural network, multiple discriminant analysis and by using information related to size of company, clarity of information, ratio of P/E and liquidity of stock one year prior price manipulation; a model for forecasting price manipulation of stocks of companies present in Tehran stock exchange were designed. At the end the power of forecasting models were studied by using data of test set. Whereas the power of forecasting logit model for test set was 92.1%, for artificial neural network was 94.1% and multi audit analysis model was 90.2%; therefore all of the 3 aforesaid models has high power to forecast price manipulation and there is no considerable difference among forecasting power of these 3 models.

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