ORIGINAL_ARTICLE
Predicting the Country Commodity Imports Using Mixed Frequency Data Sampling (MIDAS) Model
P
redicting the amount of country imports toward assessing trade balance and its effect on the balance of payments (BOP) and finally money supply, general level of prices and the rate of economic growth is of paramount importance. Therefore, economic policymakers seriously need a model which cannot only predict the volume of imports well but also be capable of revising the initial prediction over time as soon as new data for the explanatory variables are available. To this purpose, mixed frequency data sampling model was used which allows time series variables with different annual, seasonal and even daily frequencies to be used in a single regression model. In estimating the model using the software R, annual real imports, real exports and quarterly of real GDP, real exchange rate and the volatilities of the real exchange rate in the range of 1988 to 2014 are used. Information related to 2014 is not used in preliminary estimation of relationship, so that the predictive power of the model outside of the estimated range can be tested. The proposed model predicts that real imports of goods as49948 million dollars for 2014 which is associated with an error of only41 million dollars, or about 8 percent, compared to its real amount achieved of49907 million dollars. The result suggests that the predictive power of the MIDAS model is very satisfactory.
https://ier.ut.ac.ir/article_67847_c87cfd54d317bd3e84a52c00bbae7050.pdf
2018-10-01
867
886
10.22059/ier.2018.67847
Keywords: Imports
Models with Different Frequencies
MIDAS. JEL Classification: F10
C53
E27
Vida
Varahrami
v_varahrami@sbu.ac.ir
1
Faculty of Economics and Political Sciences, Shahid Beheshti University, Tehran, Iran
LEAD_AUTHOR
Samaneh
Javaherdehi
s.javaherdehi69@gmail.com
2
Faculty of Economics and Political Sciences, Shahid Beheshti University,Tehran,Iran.
AUTHOR
Bahmani-Oskooee, M., Hengerty, S. W., & Zhang, R. )2014(. The Effects of Exchange Rate Volatility on Korean flows: Industry Level Estimates. Economic Paper, 33(1), 76-97.
1
Bayat, M., & Noferesti, M. (2015). Applied Econometric of Time Series: Mixed Frequency Data Sampling. Tehran: Noor-e-Elm Publication.
2
Derakhshan, M. (1995). Econometrics; Single Equations with Classical Assumptions. Tehran: Samt Publishing.
3
Ghysels, E., Kvedaras, V., & Zemlys, M. (2014). Mixed Frequency Data Sampling Regression Models: the Package Midas. Journal of Statistical Software, 10, 18-25.
4
Klein, L. R., & Sojo, E. (1989). Combinations of High and Low Frequency Data in Macroeconomic Models. Retrieved from
5
https://link.springer.com/chapter/10.1007/978-94-009-0463-7_1.
6
Noferest, M., & Dashtban, S. (2017). Effect of Changes in Age Structure of the Population on Government Tax Revenues and Predicting its Changes: An Approach of Mixed Frequency Data Sampling (MIDAS). Journal of Economic Research, 22(6), 59-75.
7
Noferesti, M., Varahrami, V., & Javaherdehi, S. (2017). Seasonal Variations and Prediction Revision of Annual Non-oil Export: A Mixed Data Sampling (MIDAS) Approach. Journal of Economic and Modeling, 8(29), 67-88.
8
Sayadi, F., & Moghadasi, R. (2015). The Effect of Energy Prices on the Price of Grain Using Regression Models with Complex Data. Research Journal of Applied Economics studies in Iran, 15, 149-160.
9
Sepanlou, H., & Ghanbari, A. (2010). The Factors Affecting the Demand for Imports in Terms of 223 Goods, Capital and Consumer. Journal of Business Research, 57, 209-210.
10
Souri, A. (2014). Econometrics; along with Eviews 8 Application & Stata 12. Tehran: Publishing Culturology.
11
Tsui, A. K., Xu, C. Y., & Zhang, Z. Y. (2013). Forecasting Singapore Economic Growth with Mixed-Frequency Data. 20th International Congress on Modeling and Simulation, Retrieved from
12
http://ro.ecu.edu.au/cgi/viewcontent.cgi?article=1339&context=ecuworks2013.
13
ORIGINAL_ARTICLE
An Economic Explanation of the Effect of Birth Order on Educational Achievement
T
he relationship between birth order and child's human capital is studied in this paper. A Microeconomic model is designed to analyze the intrahousehold behavior on resource allocation and its outcomes for children. Since adding up a child changes the method of intrahousehold resource allocation, the expenditures of investment in child's human capital has been changed too. The aim of this paper is to investigate the effect of birth order on educational achievement which measured by average of scores and score of math as two measures of educational quality and by years of schooling as a measure of quantity of education. In general, five samples from Tehran are used: the first includes primary students in grade 4 and the second and third samples cover single persons (male and female) who are at least 20 years old. The fourth and fifth samples include married persons (male and female) who are at least 35 years old. According to these data, the regression analysis is used to present evidence and test hypothesis. Results suggest that the increase of birth order reduces both the quantity and quality of children's education in the sense that the latest children have lower level of education than the earliest ones.
https://ier.ut.ac.ir/article_67848_753556d491f68f9fb50b7fc8d03a5124.pdf
2018-10-01
887
907
10.22059/ier.2018.67848
Keywords: Family
Child
Birth Order
Human capital
Education. JEL Classification: I20
J13
Vahid
Mehrbani
1
Faculty of Economics, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Behrman, J. R., & Taubman, P. (1986). Birth Order, Schooling and Earnings. Journal of Labor Economics, 4(3), S121-S145.
1
Behrman, J. R., Pollak, R. A., & Taubman, P. (1982). Parental Preferences and Provision for Progeny. The Journal of Political Economy, 90(1), 52-73.
2
Black, S. E., Devereux, P. J., & Salvanes, K. G. (2011). Older and Wiser? Birth Order and IQ of Young Men. CESifo Economic Studies, 57(1), 103-120.
3
Collin, M. (2006). Lining up to Eat: Birth Order and Nutritional Status in Rural Ethiopia (Master's Thesis, St. Antony's College, Oxford). Retrieved from
4
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.627.9668&rep=rep1&type=pdf.
5
Hauser, R. M., & Sewell, W. H. (1985). Birth Order and Educational Attainment in Full Sibships. American Educational Research Journal, 22(1), 1-23.
6
Kessler, D. (1991). Birth Order, Family Size and Achievement: Family Structure and Wage Determination. Journal of Labor Economics, 9(4), 413-426.
7
Laskov, I., Birnbaum, R., Amzallag, S., Maslovitz, S., Kupferminc, M., Lessing, J., Pauzner, D., & Many, A. (2009). Pregnancy in Women 45 Years Old: Risks and Hazards. American Journal of Obstetrics and Gynecology, 201(6), S62- S62.
8
Lindert, P. H. (1977). Sibling Position and Achievement. Journal of Human Resources, 12(2), 198-219.
9
Page, E. B., & Grandon, G. M. (1979). Family Configuration and Mental Ability: Two Theories Contrasted with U.S. Data. American Educational Research Journal, 16(3), 257-272.
10
Spiker, D., Lotspeich, L. J., Dimiceli, S., Szatmari, P., Myers, R. M., & Risch, N. (2001). Birth Order Effects on Nonverbal IQ Scores in Autism Multiplex Families. Journal of Autism and Developmental Disorders, 31(5), 449-460.
11
Velandia, W., Grandon, G. M., & Page, E. B. (1978). Family Size, Birth Order and Intelligence in a Large South American Sample. American Educational Research Journal, 15(3), 399-416.
12
Zajonc, R. B. (1976). Family Configuration and Intelligence. Science, 192, 227-236.
13
Zajonc, R. B., & Sulloway, F. J. (2007). The Confluence Model: Birth Order as a Within-Family or Between-Family Dynamic? Personality and Social Psychology Bulletin, 33(9), 1187-1194.
14
ORIGINAL_ARTICLE
The Effects of Oil Price Movement on Nigerian Macroeconomic Variables: Evidence from Linear near and Nonlinear ARDL Modelling
T he study seeks to investigate both linear and nonlinear effects of oil price movement on critical macroeconomic variables (output, price and exchange rate) in Nigeria using ARDL modeling approach. Previous studies substantially relied on linear methods using VAR approach to unravel this links without a clear conclusion. In an attempt to seek better results in this study, we employ both linear and nonlinear ARDL modeling techniques that inherently allows for asymmetric effect. Based on the theoretical proposition of ARDL methods that does require that all data are either stationary at level or at first difference or the combination of the two. We perform unit root tests and other required econometrics tests. Consequently, linear and nonlinear ARDL estimation techniques were carried out. The results from linear and non-linear estimations indicate that oil price movement has statistical significant effects on critical macroeconomic variables in Nigeria (output, price and exchange rate) both in the short-run and long-run but there is evidence of asymmetric effect for output and exchange rate only. Therefore the study concludes there is no asymmetric effect of oil price movement on general price level in Nigeria but there are statistically significant asymmetric effects of oil price movement on output and exchange rate in the country.
https://ier.ut.ac.ir/article_67849_eebac98e85b26193fd5a6a062f8fbb34.pdf
2018-10-01
908
933
10.22059/ier.2018.67849
Keywords: Macroeconomics
NARDL
Shock and Price. JEL Classification: C00
C55
E32
E31
Lukman
Oyelami
loyelami@unilag.edu.ng
1
Economics Unit, Distance Learning Institute University of Lagos. Nigeria
LEAD_AUTHOR
Abdulkareem, A., & Abdulhakeem, K. A. (2016). Analyzing Oil Price-Macroeconomic Volatility in Nigeria. CBN Journal of Applied Statistics, 7(1), 1-22.
1
Alley, I., Asekomeh, A., Mobolaji, H., & Adeniran, Y. A. (2014). Oil Price Shocks and Nigerian Economic Growth. European Scientific Journal, 10(19), 375-391.
2
Ayadi, O. F. (2005). Oil Price Fluctuations and the Nigerian Economy. OPEC Energy Review, 29(3), 199-217.
3
Bayar, Y., & Karamelikli, H. (2015). Impact of Oil and Natural Gas Prices on the Turkish Foreign Trade Balance: Unit Root and Cointegration Tests with Structural Breaks. Romanian Economic and Business Review, 10(3), 91-149.
4
Bayramoglu, A. T., & Yildirim, E. (2017). The Relationship between Energy Consumption and Economic Growth in the USA: A Non-Linear ARDL Bounds Test Approach. Energy and Power Engineering, 9(3), 170-186.
5
Brun‐Aguerre, R., Fuertes, A. M., & Greenwood‐Nimmo, M. (2017). Heads I Win; Tails You Lose: Asymmetry in Exchange Rate Pass‐Through into Import Prices. Journal of the Royal Statistical Society: Series A (Statistics in Society), 180(2), 587-612.
6
Burbidge, J., & Harrison, A. (1984). Testing for The Effects of Oil-Price Rises Using Vector Autoregressions. International Economic Review, 25(2), 459-484.
7
Chuku, C. A., Akpan, U. F., Sam, N. R., & Effiong, E. L. (2011). Oil Price Shocks and the Dynamics of Current Account Balances in Nigeria. OPEC Energy Review, 35(2), 119-139.
8
Darby, M. R. (1982). The Price of Oil and World Inflation and Recession. The American Economic Review, 72(4), 738-751.
9
Ekong, C. N., & Effiong, E. L. (2015). Oil Price Shocks and Nigeria’s Macroeconomy: Disentangling the Dynamics of Crude Oil Market Shocks. Global Business Review, 16(6), 920-935.
10
Gisser, M., & Goodwin, T. H. (1986). Crude Oil and the Macroeconomy: Tests of Some Popular Notions: Note. Journal of Money, Credit and Banking, 18(1), 95-103.
11
Hamilton, J. D. (2009). Understanding Crude Oil Prices. The Energy Journal, 30(2), 179-207.
12
---------- (1996). This Is What Happened to the Oil Price-Macroeconomy Relationship. Journal of Monetary Economics, 38(2), 215-220.
13
---------- (1983). Oil and the Macroeconomy since World War II. Journal of Political Economy, 91(2), 228-248.
14
Herrera, A. M., Lagalo, L. G., & Wada, T. (2011). Oil Price Shocks and Industrial Production: Is The Relationship Linear? Macroeconomic Dynamics, 15(S3), 472-497.
15
Huang, B. N., Hwang, M. J., & Peng, H. P. (2005). The Asymmetry of the Impact of Oil Price Shocks on Economic Activities: An Application of the Multivariate Threshold Model. Energy Economics, 27(3), 455-476.
16
Huntington, H. G. (1998). Crude Oil Prices and US Economic Performance: Where Does The Asymmetry Reside? The Energy Journal, Retrieved from
17
https://web.stanford.edu/group/emf-research/docs/occasional_papers/OP43.pdf.
18
Ibrahim, M. H. (2015). Oil and Food Prices in Malaysia: A Nonlinear ARDL Analysis. Agricultural and Food Economics, Retrieved from https://agrifoodecon.springeropen.com/track/pdf/10.1186/s40100-014-0020-3.
19
Iwayemi, A., & Fowowe, B. (2011). Impact of Oil Price Shocks on Selected Macroeconomic Variables in Nigeria. Energy Policy, 39(2), 603-612.
20
Jin, G. (2008). The Impact of Oil Price Shock and Exchange Rate Volatility on Economic Growth: A Comparative Analysis for Russia, Japan, and China. Research Journal of International Studies, 8(11), 98-111.
21
Katrakilidis, C., & Trachanas, E. (2012). What Drives Housing Price Dynamics in Greece: New Evidence from Asymmetric ARDL Cointegration. Economic Modelling, 29(4), 1064-1069.
22
Kilian, L. (2009). Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in The Crude Oil Market. The American Economic Review, 99(3), 1053-1069.
23
Kumar, S. (2009). The Macroeconomic Effects of Oil Price Shocks: Empirical Evidence for India. Economics Bulletin, 29(1), 15-37.
24
Lee, K., Ni, S., & Ratti, R. A. (1995). Oil Shocks and the Macroeconomy: The Role of Price Variability. The Energy Journal, 16(4), 39-56.
25
Mehrara, M. (2008). The Asymmetric Relationship between Oil Revenues and Economic Activities: The Case of Oil-Exporting Countries. Energy Policy, 36(3), 1164-1168.
26
Mordi, C. N., & Adebiyi, M. A. (2010). The Asymmetric Effects of Oil Price Shocks on Output and Prices in Nigeria Using a Structural VAR Model. Central Bank of Nigeria Economic and Financial Review, 48(1), 1-32.
27
Mork, K. A. (1989). Oil and the Macroeconomy When Prices Go Up and Down: An Extension of Hamilton's Results. Journal of Political Economy, 97(3), 740-744.
28
Mory, J. F. (1993). Oil Prices and Economic Activity: Is The Relationship Symmetric? The Energy Journal, 14(4), 151-161.
29
Moshiri, S. (2015). Asymmetric Effects of Oil Price Shocks in Oil‐Exporting Countries: The Role of Institutions. OPEC Energy Review, 39(2), 222-246.
30
Olomola, P. A. (2006). Oil Price Shock and Aggregate Economic Activity in Nigeria. African Economic and Business Review, 4(2), 40-45.
31
Omojolaibi, J. A. (2013). Does Volatility in Crude Oil Price Precipitate Macroeconomic Performance In Nigeria? International Journal of Energy Economics and Policy, 3(2), 143-152.
32
Pesaran, M. H., Shin, Y., & Smith, R. J. (2000). Structural Analysis of Vector Error Correction Models with Exogenous I(1) Variables. Journal of Econometrics, 97(2), 293-343.
33
Raza, N., Shahzad, S. J. H., Tiwari, A. K., & Shahbaz, M. (2016). Asymmetric Impact of Gold, Oil Prices and Their Volatilities on Stock Prices of Emerging Markets. Resources Policy, 49, 290-301.
34
Sadorsky, P. (1999). Oil Price Shocks and Stock Market Activity. Energy Economics, 21(5), 449-469.
35
Salisu, A. A., & Fasanya, I. O. (2013). Modelling Oil Price Volatility with Structural Breaks. Energy Policy, 52, 554-562.
36
Shin, Y., Yu, B., Greenwood-Nimmo, M. (2011). Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework. Mimeo, Retrieved from
37
http://www.academia.edu/download/46980906/Modelling_Asymmetric_Cointegration_and_D20160703-32282-1202754.pdf.
38
Taiwo, M., Abayomi, T., & Damilare, O. (2012). Crude Oil Price, Stock Price and Some Selected Macroeconomic Indicators: Implications on the Growth of Nigeria Economy. Research Journal of Finance and Accounting, 3(2), 42-48.
39
Zhu, H., Su, X., Guo, Y., & Ren, Y. (2016). The Asymmetric Effects of Oil Price Shocks on the Chinese Stock Market: Evidence from a Quantile Impulse Response Perspective. Sustainability, 8(8), 1-19.
40
ORIGINAL_ARTICLE
The Calculation of the Monetary Condition Index (MCI) in Iran Economy (1978–2012)
T he completed MCI includes three main channels of interest rate, exchange rate and credit rate. In developing countries such as Iran, this indicator, which contains a credit channel, could be better used to illustrate the country’s monetary condition. This study has been done to calculate this index for the period of 1978–2012. For this purpose, the function of the total economy demand is estimated in order to extract the variables weight in this index, using the self-explanatory Autoregressive Distributed Lag (ARDL) approach. According to the model estimation results, the exchange rates weights are higher than interest rate channel in the MCI calculation. Using the weights derived from the model estimation, the nominal and real MCI have been calculated. Eventually, by estimating the inflation equation and comparing the root mean squared error (RMSE) of the two, it has been found that the predictive power of inflation in the real MCI is higher than the nominal.
https://ier.ut.ac.ir/article_67850_6dbfcb4f0e23183f18cfd37f71c649d3.pdf
2018-10-01
934
955
10.22059/ier.2018.67850
Keywords: Monetary Policy
Nominal MCI
Real MCI
Root Mean Squared Error. JEL Classification: C01
C22
E40
E52
E58
Hamidreza
Horry
horryhr@uk.ac.ir
1
Shahid Bahonar University of Kerman
AUTHOR
Sayyed Abdolmajid
Jalaee Esfand Abadi
jalaee@uk.ac.ir
2
Shahid Bahonar University of Kerman
AUTHOR
Mehdi
Nejati
mehdi.nejati@uk.ac.ir
3
Department of Economics, University of Shahid Bahonar, Kerman, Iran
AUTHOR
Siminossadat
Mirhashemi Naeini
siminmirhashemi@aem.uk.ac.ir
4
Department of Economics, University of Shahid Bahonar, Kerman, Iran
LEAD_AUTHOR
Ball, L. (1999). Policy Rules for Open Economies. Chicago: University of Chicago Press.
1
Batini, N., & Turnbull, K. (2002). A Dynamic Monetary Conditions Index for the UK. Journal of Policy Modeling, 24, 257-281.
2
Bernanke, B., & Gertler, M. (1995). Inside the Black Box: the Credit Channel of Monetary Policy Transmission. Journal of Economic Perspectives, 9, 27-48.
3
Branson, W. H. (1979). Macroeconomic Theory and Policy.
4
New York: Harper and Row.
5
Burger, P., & Knedlik, T. (2004). The MCI as a Monetary Policy Guide in a Small, Open Emerging Market Economy. South African Journal of Economics, 72(2), 365-383.
6
Chow, H. K. (2012). Can a Financial Condition Guide Monetary Policy? The Case of Singapore. Singapore Management University, Retrieved from http://ink .library.smu.edu.sg/soe_research/1484.
7
Ericsson, N. R., Jansen, E. S., Kerbeshian, N. A., & Nymoen, R. (1997). Understanding a Monetary Condition Index. Board of Governors of the Federal Reserve System, Retrieved from
8
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.377.3614&rep=rep1&type=pdf.
9
Freedman, C. (1995). The Role of Monetary Conditions and the Monetary Conditions Index in the Conduct of Policy. Bank of Canada Review, Retrieved from
10
https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.bankofcanada.ca%2Fwpcontent%2Fuploads%2F2010%2F06%2Fr954c.pdf;h=repec:bca:bcarev:v:1995:y:1995:i:autumn95.
11
---------- (1994). The Use of Indicators and of the Monetary Condition Index in Canada. In T. J. T. Balino, & C. Cottarelli (Eds). Frameworks for Monetary Stability: Policy Issues and Country Experiences (458-476). Washington, DC: International Monetary Fund.
12
Gerlach, S., & Smets, F. (2000). MCIs and Monetary Policy. European Economic Review, 44, 1677-1700.
13
Kannan, R., Sanyal, S., & Bhoi, B. B. (2006). Monetary Condition Index for India; Reserve Bank of India. Occasional Papers, 3, 57-86.
14
Khorsandi, M., Slamlouian, K., & Zalnour, H. (2012). Appropriate Monetary Condition Index for the Iranian Economy. Quarterly Journal of Economic Research, 1, 31-57.
15
Oriela, K. (2011). Estimation of Weights for the Monetary Condition Index in Albania. Special Conference Paper of the Bank of Greece, Retrieved from
16
https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiHwav8wNvXAhVQEuwKHcekAVEQFggkMAA&url=http%3A%2F%2Fwww.bankofgreece.gr%2FBogEkdoseis%2FSCP201104.pdf&usg=AOvVaw2gnlqgUJLrGui41mem7bwv.
17
Peng, W. S., & Leung, F. (2005). A Monetary Condition Index for Mainland China. Bank for International Settlements, RM2005-01, Retrieved from
18
https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjGyLGkwdvXAhWSqKQKHVRRBLwQFggpMAA&url=http%3A%2F%2Fwww.hkma.gov.hk%2Fmedia%2Feng%2Fpublication-and research%2Fquarterlybulletin%2Fqb200506%2Ffa1.pdf&usg=AOvVaw1HphekWCjZnhWcdTvRHCB3.
19
Sadeghi, H., Rostamzadeh, P., & Asgharpour, H. (2007). The Separation of Monetary Policies Using the Monetary Condition Index (MCI) in Iran. Economic Letter, 2, 59-82.
20
Siklar, I., & Dogan, B. (2015). Monetary Condition Index with Time Varying Weights: an Application to Turkish Data. Business and Economic Research, 5, 117-132.
21
Thanh Ha, L. (2015). Measuring the Stance of Monetary Policy in Vietnam: a Structural VAR Analysis. Asian Journal of Economics and Empirical Research, 1, 8-22.
22
Wai-Ching, P. (2010). Augmented MCI: an Indicator of Monetary Policy Stance for ASEAN-5? Munash University, Department of Economics Discussion Paper, 25/10, 1-19.
23
Wet, D. (2002). Coping with Inflation and Exchange Rate Shocks in the South African Economy. The South African Journal of Economics, 70(1), 78-94.
24
Xiong, W. (2012). Constructing the Monetary Condition Index for China. Frontiers of Economics in China, 7(3), 373-406.
25
Yaaba, B. (2013). Monetary Policy Rule: a Broad Monetary Conditions Index for Nigeria. CBN Journal of Applied Statistics, 1, 35-53.
26
ORIGINAL_ARTICLE
The Lobbying, Bribery, and Compliance: An Evolutionary Model of Social Factors
Abstract Connecting to rule-makers in order to set favorable rules (lobbying) or paying government executives to bend the current rule (bribing) are the two main strategies for influencing government. This study in an evolutionary game model explain why bribing may become widespread while other states like compliance and cooperative lobbying are Pareto superior. The theoretical model is used to study the effect of social parameters on firm’s choice between lobbying and bribing. The results indicate that social disapproval of bribery has a negative impact on corruption. The effect, however, depends on the history of countries. Countries with a long history of corruption have much more difficult task in fight with corruption. Cooperation was the second social factor to be investigated. The effect of cooperation on lobbying is indirect through alleviating the difficulty and costs of linking to the government. Whenever and wherever linking is difficult, firms by cooperation, can make it less impeding.
https://ier.ut.ac.ir/article_67851_c4f4170f184fa9d76508b658477b1293.pdf
2018-10-01
956
989
10.22059/ier.2018.67851
Keywords: Lobbying
Bribery
Evolutionary Games
Replicator Dynamics
Cooperation. JEL Classification: D72
D73
C73
O57
Z13
Abbas
Khandan
khandan@unisi.it
1
Faculty of Economics, University of Siena, Siena, Italy
LEAD_AUTHOR
Banerjee, R. (2016). Corruption, Norm Violation and Decay in Social Capital. Journal of Public Economics, 137, 14-27.
1
Beckmann, K., & Gerrits, C. (2009). Lobbying and Corruption as Substitute Forms of Rent-seeking. In W. Schäfer, A. Schneider & T. Tobias (Eds.), Markets and Politics - Insights from a Political Economy Perspective. Marburg: Metropolis-Verl.
2
Bjørnskov, C. (2011). Combating Corruption: On the Interplay between Institutional Quality and Social Trust. Journal of Law and Economics, 54, 135-159.
3
Campos, N., & Giovannoni, F. (2008). Lobbying, Corruption and Other Banes. William Davidson Institute WP, 930, Retrieved from https://deepblue.lib.umich.edu/handle/2027.42/64386.
4
---------- (2007). Lobbying, Corruption and Political Influence. Public Choice, 131(1), 1-21.
5
Drutman, L. (2015). The Business of America is Lobbying: How Corporations Became Politicized and Politics Became More Corporate. New York: Oxford University Press.
6
Esteban, J., & Ray, D. (2001). Collective Action and the Group Size Paradox. American Political Science Review, 95(3), 663-672.
7
Harstad, B., & Svensson, J. (2011). Bribes, Lobbying, and Development. American Political Science Review, 105(1), 46-63.
8
Marwell, G., & Pamela, O. (1993). The Critical Mass in Collective Action: A Micro-social Theory. Cambridge: Cambridge University Press.
9
Narayan, D., & Pritchett, L. (1999). Cents and Sociability: Household Income and Social Capital in Rural Tanzania. Economic Development and Cultural Change, 47(4), 871-897.
10
Nooteboom, B. (2007). Social Capital, Institutions and Trust. Review of Social Economy, 65(1), 29-53.
11
Pamela, O., & Marwell, G. (1988). The Paradox of Group Size in Collective Action. A Theory of Critical Mass, II. American Sociological Review, 53(1), 1-8.
12
Robinson, L. J., Allan Schmid, A., & Siles, M. E. (2002). Is Social Capital Really Capital? Review of Social Economy, 60(1), 1-21.
13
Sandholm, W. H. (2010). Population Games and Evolutionary Dynamics. Cambridge: MIT Press.
14
Sandholm, W. H., Dokumaci, E., & Franchetti, F. (2012). Dynamo: Diagrams for Evolutionary Game Dynamics. Retrieved from http://www.ssc.wisc.edu/~whs/dynamo.
15
Schuler, T. (2007). Reflections on the Use of Social Capital. Review of Social Economy, 65(1), 11-28.
16
World Business Environment Survey. (2012). The World Bank. Retrieved from http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:20699364~pagePK:64214825~piPK:64214943~theSitePK:469382,00.html.
17
World Development Report. (1997). The World Bank. Retrieved from http://www.worldbank.org/en/publication/wdr/wdr-archive.
18
World Values Survey. (2005). Official Data File, 20090901; 2009. World Values Survey Association, Retrieved from
19
http://www.worldvaluessurvey.org/wvs.jsp.
20
Corruption Perception Index. (2012). Transparency International. Retrieved from http://www.transparency.org/cpi2012 .
21
Corruption Perception Index. (1999). Transparency International. Retrieved from http://www.transparency.org/cpi2012.
22
ORIGINAL_ARTICLE
GJR-Copula-CVaR Model for Portfolio Optimization: Evidence for Emerging Stock Markets
Abstract
T
his paper empirically examines the impact of dependence structure between the assets on the portfolio optimization, composed of Tehran Stock Exchange Price Index and Borsa Istanbul 100 Index. In this regard, the method of the Copula family functions is proposed as powerful and flexible tool to determine the structure of dependence. Finally, the impact of the dependence structure on the risk identification and the optimized portfolio selection, will be analyzed. The results show that the t-student copula function provides the best performance among other Copula functions. Also, empirical evidence suggests that the performance of the GJR-Copula-CVaR method is relatively more accurate and more flexible than other common methods of optimization.
https://ier.ut.ac.ir/article_67852_9d1b6b6a6612e835f7a8014edc48f610.pdf
2018-10-01
990
1015
10.22059/ier.2018.67852
Keywords: Portfolio Optimization
Conditional Value at Risk
Copula Functions
Dependence Structure. JEL Classification: C60
C61
G11
Moien
Nikusokhan
m.nikusokhan@mail.sbu.ac.ir
1
Department of Financial Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran
LEAD_AUTHOR
Ang, A., & Bekaert, G. (2002). International Asset Allocation with Regime Shifts. Review of Financial Studies, 15(4), 1137-1187.
1
Ang, A., & Chen, J. (2002). Asymmetric Correlations of Equity Portfolios. Journal of Financial Economics, 63(3), 443-494.
2
Beine, M. (2004). Conditional Covariance’s and Direct Central Bank Interventions in the Foreign Exchange Markets. Journal of Banking & Finance, 28(6), 1385-1411.
3
Boubaker, H., & Sghaier, N. (2013). Portfolio Optimization In The Presence Of Dependent Financial Returns with Long Memory: A Copula Based Approach. Journal of Banking & Finance, 37(2), 361-377.
4
Brooks, C., Burke, S., Heravi, S., & Persand, G. (2005). Autoregressive Conditional Kurtosis. Journal of Financial Econometrics, 3(3), 399-421.
5
Chen, Y. H., & Tu, A. (2013). Estimating Hedged Portfolio Value-at-Risk Using The Conditional Copula: An Illustration Of Model Risk. International Review of Economics & Finance, 27(C), 514-528.
6
Das, S., & Uppal, R. (2004). Systemic Risk and International Portfolio Choice. Journal of Finance, 59(6), 2809-2834.
7
Embrechts, P., McNeil, A., & Straumann, D. (2002). Correlation and Dependence in Risk Management: Properties and Pitfalls. In M. A. H. Dempster (Ed.), Risk Management: Value at Risk and Beyond (176-223). Cambridge: Cambridge University Press.
8
Engle, R. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987-1007.
9
Fantazzini, D. (2008). Dynamic Copula Modelling for Value at Risk. Frontiers in Finance and Economics, 5(2), 72-108.
10
Frank, M. (1979). On The Simultaneous Associativity of F(x, y) and x+y-F(x,y). Aequationes Mathematicae, 19(1), 194-226.
11
Glosten, L., Jagannathan, R., & Runkle, D. (1993). On The Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. The Journal of Finance, 48(5), 1779-1801.
12
Gumbel, E. (1960). Bivariate Exponential Distributions. Journal of American Statistical Association, 55(292), 698-707.
13
Hartmann, P., Straetmans, S., & DeVries, C. (2004). Asset Market Linkages in Crisis Periods. The Review of Economics and Statistics, 86(1), 313-326.
14
Harvey, C., & Siddique, A. (1999). Autoregressive Conditional Skewness. Journal of Financial and Quantitative Analysis, 34(4), 465-487.
15
He, X., & Gong, P. (2009). Measuring the Coupled Risks: A Copula-Based CVaR Model. Journal of Computational and Applied Mathematics, 223(2), 1066-1080.
16
Huang, J. J., Lee, K. J., Liang, H., & Lin, W. F. (2009). Estimating Value at Risk of Portfolio by Conditional Copula-GARCH Method. Insurance: Mathematics and Economics, 45(3), 315-324.
17
Mesfioui, M., & Quessy, J. F. (2005). Bounds on the Value-at-Risk for the Sum of Possibly Dependent Risks. Insurance: Mathematics and Economics, 37(1), 135-151.
18
Palaro, H., & Hotta, L. (2006). Using Conditional Copula to Estimate Value at Risk. Journal of Data Science, 4(1), 93-115.
19
Patton, A. (2006). Modelling Asymmetric Exchange Rate Dependence. International Economic Review, 47(2), 527-556.
20
---------- (2002). Applications of Copula Theory in Financial Econometrics (Unpublished Doctoral Dissertation), University of California, USA.
21
Poon, S. H., Rockinger, M., & Tawn, J. (2004). Modelling Extreme-Value Dependence in International Stock Markets. Statistica Sinica, 13(4), 929-953.
22
Sadique, S., & Silvapulle, P. (2001). Long-term Memory in Stock Market Returns: International Evidence. International Journal of Finance & Economics, 6(1), 59-67.
23
Schmidt, R. (2003). Dependencies of Extreme Events in Finance: Modelling, Statistics and Data Analysis (Unpublished Doctoral Dissertation), Ulm University, Germany.
24
Sklar, A. (1959). Fonctions de Répartition À N Dimensions Et Leurs Marges. Paris: Université Paris.
25
Song, P. K. (2000). Multivariate Dispersion Models Generated From Gaussian Copula. Scandinavian Journal of Statistics, 27(2), 305-320.
26
ORIGINAL_ARTICLE
The Effect of Unemployment on Health Capital
Abstract T his paper has considered the impact of unemployment on health capital in 136 countries during 2002–2010. The review of presented literature on health capital shows that the life expectancy has been considered as a proxy for health capital. Although, there are numerous studies that surveyed the mental and physical effects of unemployment, but there is no previous study in our knowledge that investigates the influence of unemployment on life expectancy in these countries. In addition, the effects of other macroeconomic factors including inflation, gross capital formation and development degree on life expectancy are analyzed as well. To do this, the data provided by World Bank is used and the presented model is estimated by panel data method. The results show that unemployment affects the life expectancy, negatively. Also, the effect of inflation on life expectancy is negative and statistically significant. However, gross capital formation is the main positive economic factors for improving longevity. The development degree of countries is positively related to the life expectancy. So, the average of life expectancy in developed countries is more than poor countries. Also, the urbanity is the main socio-environmental cause for life expectancy. Therefore, in terms of policy, it is recommended that the planning for creating the new job opportunities and enhancing the national incomes take in consider by policy makers in order to use of health capital benefits for economic development especially in developing countries.
https://ier.ut.ac.ir/article_67853_4aaccd0120836c397be6c2e6d6b25356.pdf
2018-10-01
1016
1033
10.22059/ier.2018.67853
Keywords: Economic Development
Life Expectancy
Panel data method
Unemployment
World Bank. JEL Classification: I19
O50
E17
Abdalali
Monsef
monsefali@yahoo.com
1
Department of Economics, Payame Noor University, Tehran, Iran
LEAD_AUTHOR
Abolfazl
Shahmohammadi Mehrjardi
shahamohamadi@stu.yazd.ac.ir
2
Department of Economics, Business School, Yazd University, Yazd, Iran
AUTHOR
Ahn, N., Garcia, J. R., & Jimeno, J. F. (2004). The Impact of Unemployment on Individual Well-Being in the EU. NEPRI Working Paper, 29, 1-19.
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4
Claussen, B., Bjorndal, A., & Hjort, P. (1993). Health and Re-employment in a Two Year Follow up Study of Long-term Unemployed. Journal of Epidemiology and Community Health, 47, 14–18.
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7
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8
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9
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62
ORIGINAL_ARTICLE
Monetary Policies, Exchange Rate Pass-through and Prices in Asian Economies: A Long and Short-run Analysis
Abstract
T
he financial crisis in 2007-2008 has turned into the most far-reaching international financial and economic crisis since the Great Depression. Indeed, the crisis-affected Asian countries experienced varying degrees of changes in the exchange rate and prices following an initial shock of sharp depreciation of their currencies in the second half of 1997. Moreover, questions connected with the exchange rate regime have been an important part of understanding macroeconomic policies and outcomes in Asia. Thus, the objective of this paper is to examine pass-through effects on domestic prices among the four selected Asian countries, Japan and S. Korea from the east and Iran and Turkey from the west, with special emphasis on an interaction between prices, monetary policies and exchange rate changes. In order to take into account dynamic effects between these variables, the structural vector auto-regression (SVAR) method is employed, by which the responses of such shocked variables are evaluated during 1970- 2015. The empirical results confirmed a dynamic relationship between exchange rate pass-through and other macro variables in the selected countries. Also, the results have shown that the pass-through shocks in the short-run are more effective in the countries which benefit from a managed floating exchange rate regime and inflation targeting policy.
https://ier.ut.ac.ir/article_67854_8455cabc706de44c5f2866673d76e582.pdf
2018-10-01
1034
1064
10.22059/ier.2018.67854
Keywords: Monetary Policies
Pass-through
Financial Crises
SVAR Regression
Exchange Rate. JEL Classification: C22
C32
F31
F40
Mehdi
Yazdani
ma_yazdani@sbu.ac.ir
1
Department of Economics, Faculty of Economics and Political Sciences, Shahid Beheshti University
LEAD_AUTHOR
An, L. (2006). Exchange Rate Pass-through: Evidence Based on Vector Auto-regression with Sign Restrictions. Munich Personal REPEC Archive, Retrieved from https://ideas.repec.org/p/fip/feddgw/70.html.
1
Baig, T., & Goldfajn, I. (1999). Financial Market Contagion in the Asian Crises. IMF Staff Papers, 46(2), 167-195.
2
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3
Batini, N., Harrision, R., & Millard, S. P. (2001). Monetary Policy Rules for an Open Economy. Journal of Economic Dynamics and Control, 27(11-12), 2059-2094.
4
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9
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10
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11
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12
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13
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14
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17
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18
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19
Eichengreen, B., & Rose, A. (1998). Staying Afloat When the Wind Shifts: External Factors and Emerging Market Banking Crises. NBER Working Paper, 6370, Retrieved from
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24
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25
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27
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https://www.federalreserve.gov/pubs/ifdp/2001/704/ifdp704r.pdf
29
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32
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33
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https://ideas.repec.org/p/nbr/nberwo/21646.html.
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https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp243.pdf?c6044208c4776b265f7c0ad5038de7e7.
37
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https://www.imf.org/external/pubs/ft/wp/2001/wp01170.pdf.
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40
https://www.imf.org/external/pubs/ft/wp/2011/wp1187.pdf .
41
Ito, T., & Sato, K. (2007). Exchange Rate Changes and Inflation in Post-Crisis Asian Economies: VAR Analysis of the Exchange Rate Pass-Through. JMCB, Retrieved from https://ideas.repec.org/p/nbr/nberwo/12395.html.
42
Jasová, M., Moessner, R., & Takáts, E. (2016). Exchange Rate Pass-Through: What Has Changed since the Crisis? BIS Working Paper, Retrieved from https://www.bis.org/publ/work583.pdf.
43
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https://www.imf.org/external/np/res/seminars/2004/60/pdf/ferret.pdf.
46
McCarthy, J. (2000). Pass-Through of Exchange Rates and Import Prices to Domestic Inflation in Some Industrialized Economies. Staff Reports, Retrieved from https://ideas.repec.org/p/fip/fednsr/111.html.
47
Menon, J. (1995). Exchange Rate Pass-Through. Journal of Economic Surveys, 9(2), 197-231.
48
Minella, A., Springer de Freitas, P., Goldfajn, I. & Muinhos, M. K. (2003). Inflation Targeting in Brazil, Constructing Credibility under Exchange Rate Volatility. Journal of International Money and Finance, 22, 1015-1040.
49
Mishkin, F. S. (2000). Inflation Targeting in Emerging Market Countries. NBER Working Paper Series, Retrieved from
50
https://econpapers.repec.org/paper/nbrnberwo/7618.htm.
51
Mohanty, M. S., & Klau, M. (2004). Monetary Policy Rules in Emerging Market Economies: Issues and Evidence. BIS Working Papers, Retrieved from https://www.bis.org/publ/work149.pdf.
52
Nogueira Junior, R. P. (2007). Inflation Targeting and the Role of Exchange Rate Pass-through. University of Kent, Discussion Paper, Retrieved from ftp://ftp.repec.org/opt/ReDIF/RePEc/ukc/ukcedp/0602.pdf.
53
Olivei, G. P. (2002). Exchange Rates and the Prices of Manufacturing Products Imported into the United States. New England Economic Review, 1, 3-18.
54
Osawa, N. (2006). Monetary Policy Responses to the Exchange Rate: Empirical Evidence from three East Asian Inflation-targeting Countries. Bank of Japan Working Paper Series, Retrieved from
55
https://www.boj.or.jp/en/research/wps_rev/wps_2006/data/wp06e14.pdf.
56
Otani, A., Shiratsuka, S., &. Shirota, T. (2005). Revisiting the Decline in the Exchange Rate Pass-Through: Further Evidence from Japan’s Import Prices. IMES Discussion Paper, Retrieved from
57
https://econpapers.repec.org/article/imeimemes/v_3a24_3ay_3a2006_3ai_3a1_3ap_3a61-75.htm.
58
Sahminan, S. (2002). Exchange Rate Pass-through into Import Prices: Empirical Evidences from Some Southeast Asian countries. The University of North Carolina at Chapel Hill, Working Paper, Retrieved from www.ibrarian.net/.../Exchange_Rate_Pass_Through_into_Import_Prices__Em.pdf?
59
Schmidt-Hebbel, K., & Werner, A. (2002). Inflation Targeting in Brazil, Chile and Mexico: Performance, Credibility and the Exchange Rate. Economia, 2(2), 31-89.
60
Senay, O. (2001). The Role of the Exchange Rate in Monetary Policy Rules. York: University of York.
61
Taylor, J. B. (2001). The Role of the Exchange Rate in Monetary Policy Rules. American Economic Review, 91(2), 263-267.
62
---------- (2000). Low Inflation, Pass-through and the Pricing Power of Firms. European Economic Review, 44(7), 1389-1408.
63
ORIGINAL_ARTICLE
An Empirical Insight of Examining Impact of Recent Demonetization on Monetary System: Evidence from India
Abstract D emonetization initiative by Govt. of India in Nov-Dec, 2016 aimed at addressing the issues like black money, hoarding and overall cleansing the monetary system. This paper in this regard attempts to empirically examine the impact of demonetization drive upon the monetary system by taking data of 180 days prior to Nov, 2016. The cointegration results exhibit show a long run cointegration between the money supply, demonetization dummy, cash in hand, notes in circulation and bank deposits. Furthermore, our Bayer-Hanck cointegration also confirms the cointegration among the variables. Our error correction mechanism analysis shows the long run relation between the variables. The variance decomposition analysis further states that effect of demonetization is widely visible upon the cash in hand followed by the notes in circulation. Despite the wider claims by the government regarding the positive impacts of demonetization drive, this initiative is fraught with several challenges and limitations. The implications of this initiative are discussed in this paper.
https://ier.ut.ac.ir/article_67855_48e328f193d061350145db94bdc0be99.pdf
2018-10-01
1065
1092
10.22059/ier.2018.67855
Keywords: Demonetization
Money Supply
ARDL
Macroeconomic variables
India. JEL Classification: E50
E52
E59
Narayan
Sethi
sethin@nitrkl.ac.in
1
Department of Humanities and Social Sciences, National Institute of Technology (NIT) Rourkela, Odisha, India
LEAD_AUTHOR
Padmaja
Bhujabal
515hs3006@nitrkl.ac.in
2
Department of Humanities and Social Sciences, National Institute of Technology (NIT) Rourkela, Odisha, India
AUTHOR
Devi
Prasad Dash
devi.dash@iitrpr.ac.in
3
Department of Humanities and Social Sciences, Indian Institute of Technology (IIT), Ropar, Punjab, India
AUTHOR
Sanhita
Sucharita
sanhita.sucharita@cuj.ac.in
4
Centre for Humanities and Social Sciences, School of Humanities and Social Sciences, Central University of Jharkhand, Ranchi, Jharkhand, India
AUTHOR
Bharadwaj, A. (2016). Demonetization and the Economy: Signs of Distress Everywhere. Business Standard, 1, 1-10.
1
Ghandy, K. (2016). Demonetization: One Step Forward, Two Steps Back. Economic and Political Weekly, 51(50), 28-30.
2
Jing, G. J. L. (2007). A Study of Dynamics of Gold Reserve after Demonetization. Studies of International Finance, Retrieved from http://en.cnki.com.cn/Article_en/CJFDTOTAL-GJJR200704011.htm.
3
Kumar, S. V., & Kumar, T. S. (2016). “Demonetization and Complete Financial Inclusion”. International Journal of Management Research and Reviews, 6(12), 1703-1707.
4
Lahiri, A. K. (2016). Demonetization, the Cash Shortage and the Black Money. Working Paper, Retrieved from
5
https://nipfp.org.in/media/medialibrary/2016/12/WP_2016_184.pdf.
6
Mali, V. (2016). Demonetization: A step towards modified India. International Journal of Commerce and Management Research, 2(12),35-36.
7
Marin, D. (2002). Trust versus illusion: What is driving demonetization in the former Soviet Union? Economics of Transition, 10(1), 173-200.
8
Pachare, S. M. (2016). Demonetization: Unpacking the Digital Wallets. We'Ken-International Journal of Basic and Applied Sciences, 1(4), 180-183.
9
Raychaudhuri, A. (2017). Demonetization in India: Some Unsolved Economic Puzzles. Trade and Development Review, 9(1-2), 74-85.
10
Tax Research Team. (2016). Demonetization: Impact on the Economy. Retrieved from https://ideas.repec.org/p/npf/wpaper/16-182.html.
11
Umamaheswari, D., & Suganthi, M. K. (2017). Demonetization-Cash Crunch or Cash Curse. International Journal of Latest Transactions in Engineering and Science, 2(2), 1-4.
12
ORIGINAL_ARTICLE
The Economic Efficiency Trend of Date Orchards in Saravan County
The purpose of this study is to evaluate the efficiency of date growers in Saravan County using non-parametric methods. The measurement of date farmers’ efficiency and the comparison of their performance to with one another can play an important role in improving their efficiency and productivity. One of the common methods to measure efficiency is data envelopment analysis (DEA). Despite its advantages, this method cannot measure efficiency in a sound way when few decision-making units (DMUs) are available. Therefore, DEA window analysis approach is used to ramp up the number of DMUs in order to make it possible to measure the efficiency of the farmers. This study used DEA window analysis approach to determine date growers’ efficiency in Saravan County over 2012-2016. The results show that the efficiency score of farmers is <1, which indicates their inefficiency so that means efficiency score was found to be 0.93, 0.92 and 0.95 per year in Zaboli, Sib and Suran districts, respectively. Technological change was one of the most influential factors in changing total productivity of agriculture. It is, therefore, suggested that modern technologies be adopted to enhance the efficiency of date production in the studied region.
https://ier.ut.ac.ir/article_67877_1e26691222345b20bdd08474c53f0773.pdf
2018-10-01
1093
1112
10.22059/ier.2018.67877
Keywords: Efficiency
Data Envelopment Analysis
DEA Window Analysis Approach
Date
Saravan. JEL Classification: Q10
Q13
N5
Ali
Sardar Shahraki
a.s.shahraki@eco.usb.ac.ir
1
Department of Agricultural Economics, University of Sistan and Baluchestan, Zahedan, Iran
LEAD_AUTHOR
Mohhamad Hoseyn
Karim
karimsistani482@gmail.com
2
Department of Economics, Kharazmi University, Tehran, Iran
AUTHOR
Afkhami Ardakani, M., Momeni, M., & Farahi, R. (2011). A Study of the Efficiency of Iranian Commercial Banks Using a Combined Window Approach and Malmquist Productivity Index. Two Manuscripts of Daneshvar Behavior/Management and Development, Shahed University, 18(2-47), 179-206.
1
AKamin, A., Bidogeza, J., Rene Minkoua, J., & Afari-sefa, V. (2017). Efficiency and Productivity Analysis of Vegetable Farming within Root and Tuber-Based Systems in the Humid Tropics of Cameroon. Journal of Integrative Agriculture, 16(8), 1865-1873.
2
Asmild, M., Paradi, J. C., Aggarwall, V., & Schaffnit, C. (2004). Combining DEA Window Analysis with the Malmquist Index Approach in a Study of the Canadian Banking Industry. Journal of Productivity Analysis, 21, 67-89.
3
Croppenstedt, A. (2005). Measuring, Technical Efficiency of Wheat Farmers in Egypt. ESA Working Paper, Retrieved from
4
https://pdfs.semanticscholar.org/4e81/39296b3881c9f37c63cf409a88bff0d0b128.pdf.
5
Dahmardeh, N., & Sardar Shahraki, A. (2015). Evaluation Factors Affecting of Risk Production in Sistan Grape Growers by Using Stochastic Frontier Approach. International Journal of Agricultural Management and Development, 5(1), 59-64.
6
Deputy of Statistics and Information Technology, Ministry of Agriculture. (2015). Agricultural Statistics of the Agricultural Crop Years of 2012-2013, Tehran, Ministry of Jihad-e Agriculture, Deputy of Planning and Economic, Retrieved from http://maj.ir/.
7
Huang, Z., Fu, Y., Liang, Q., Song, Y., & Xu, X. (2013). The efficiency of agricultural marketing cooperatives in China's Zhejiang province. Managerial and Decision Economics, 13(2), 108-127.
8
Karimi, M., & Jalili, M. (2017). Study of Agricultural Water Efficiency Indicators in Major Crop Products, Case Study: Mashhad Plain (Technical Note). Journal of Water and Sustainable Development, 4(1), 138-133.
9
Kumbhakar, S., & Lovell, C. A. K. (2000). Stochastic Frontier Analysis. Cambridge: Cambridge University Press.
10
Kumbhakar, S. (1993). Efficiency Estimation in a Profit Maximizing Model Using Flexible Production Function. Journal of Agricultural Economics, 10, 143-152.
11
Latruffe, L., Bravo-Ureta, B. E., Carpentier, A., Desjeux, Y., & Moreira, V. H. (2017). Subsidies and Technical Efficiency in Agriculture: Evidence from European Dairy Farms. American Journal of Agricultural Economics, 99(3), 783-799.
12
Mehrabhi Bashrabadi, H., & Pakravan, M. (2009). Calculation of efficiency and productivity to scale of sunflower producers in Khoy. Journal of Agricultural Economics and Development (Agriculture Sciences and Technology), 23(2), 95-102.
13
Mohammadpour Hengrvani, M., & Arsalan Bod, M. R. (2015). Investigating the Economic Efficiency of Water and Its Effective Factors on the Main Crops; a Case Study of Urmia. International Conference Centered on Agriculture, Retrieved from
14
https://www.civilica.com/Paper-ICDAT01-ICDAT01_147.html.
15
Mozafari, M. (2015). Economic Efficiency of Agricultural Cooperatives in Boein Zahra City and Prioritizing their Problems in Management Process and Marketing System. Rural Development Strategies, 2(4), 382-364.
16
Sardar Shahraki, A. (2016). Optimal Allocation of Water Resources if Hirmand by Application of Game Theory and Evaluation of Management Scenarios (Unpublished Doctoral Dissertation), Agricultural Economics, University of Sistan and Baluchestan, Iran.
17
Shahnavazi, A. (2017). Determination of the Performance of Arable Crops in Agricultural Sector of Iran. Iranian Journal of Agricultural Economics and Research, 2(2), 227-240.
18
Simelane, N. (2011). An Assessment of the Role of Co-operatives in Smallholder Dairy and Marketing in Swaziland. UPSpace Institutional Repository, Retrieved from http://hdl.handle.net/2263/25800.
19
Siriopoulos, C., & Tziogkidis, P. (2010). How Do Greek Banking Institutions React After Significant Events? A DEA Approach, Omega Journal. Special Issue in Empirical Research in the EU Banking Sector and the Financial Crisis, 38(5), 294-308.
20
Sokhanvar, M., Sadeghi, H., & Asari, A. (2011). Use of Window Data Envelopment Analysis to Analyze the Structure and Process Performance of Iranian Power Distribution Companies. Economic Growth and Development Research, 4, 182-146.
21
Speelman, S., D'Haese, M., Buysse, J., & D'haese, L. (2008). A Measure for the Efficiency of Water Use and its Determinants, Study at Small-Scale Irrigation Schemes in North-West Province. Agricultural Systems, 98(1), 31-39.
22
Tozer, P. (2010). Measuring the Efficiency of Wheat Production of Western Australian Growers. Agronomy Journal, 102(2), 642-648.
23
Villano, V., & Fleming, E. (2006). Technical Inefficiency and Production Risk in Rice Farming: Evidence from Central Luzon Philippines. Asian Economic Journal, 20(1), 29-46.
24
Wang, X., Sun, L. & Zhang, Y. (2012). The Empirical Study on Operating Efficiency of Agricultural Cooperatives in Langao, International Journal of Business and Management, 7(17), 60-74.
25
Yang, H. H., Chang, C. Y. (2008). Using DEA Window Analysis to Measure Efficiencies of Taiwan’s Integrated Telecommunication Firms. Telecommunications Policy, 33(1-2), 98-108.
26
Yilmaz, B. Yurduse, M. & Harmancioglu, N. (2009). The Assessment of Irrigation Efficiency in Buyuk Menderes Basin. Water Resource Management, 23, 1081-1095.
27
ORIGINAL_ARTICLE
Macroeconomic Shocks and Malaysian Tourism Industry: Evidence from a Structural VAR Model
Abstract
his study employs a structural vector autoregression (SVAR) model to investigate the macroeconomic shocks on Malaysian tourism industry, especially how the economy dynamically responds to oil price shocks, exchange rates, changes in price level, exports, economic growth and tourism income during the study time period from January 2001 to December 2012. The results indicate that oil price shocks, economic growth, exchange rate, and exports have a contemporaneous inverse impact on tourism revenues except for consumer price index which has a positive impact. This study added instant information to manage tourism industry in Malaysia. The findings of the study are useful to implement a number of corrective measures for the promotion of tourism in a country.
https://ier.ut.ac.ir/article_67878_818b30e2dda8ec49b8e3cad085f25069.pdf
2018-10-01
1113
1137
10.22059/ier.2018.67878
Keywords: Oil Price Shocks
Exchange rate
Inflation
Exports
economic growth
Tourism Income
SVAR Model
Malaysia. JEL Classification: C33
Z32
Ghulam
Ali
g.ali@uog.edu.pk
1
Department of Commerce, University of Gujrat, Gujrat, Pakistan
AUTHOR
Khalid
Zaman
dr.khalidzaman@uow.edu.pk
2
Department of Economics, University of Wah, Quaid Avenue, Wah Cantt, Pakistan
LEAD_AUTHOR
Talat
Islam
talat.islam@puhcbf.edu.pk
3
Institute of Business Administration, University of the Punjab, Lahore, Pakistan
AUTHOR
Becken, S. (2011). Oil, the Global Economy and Tourism. Tourism Review, 66(3), 65-72.
1
Becken, S., & Lennox, J. (2012). Implications of a Long-Term Increase in Oil Prices for Tourism. Tourism Management, 33(1), 133-142.
2
Beckmann, J. (2013). Nonlinear Adjustment, Purchasing Power Parity and the Role of Nominal Exchange Rates and Prices. The North American Journal of Economics and Finance, 24, 176-190.
3
Bilen, M., Yilanci, V., & Eryüzlü, H. (2017). Tourism Development and Economic Growth: a Panel Granger Causality Analysis in the Frequency Domain. Current Issues in Tourism, 20(1), 27-32.
4
Blake, A., Arbache, J. S., Sinclair, M. T., & Teles, V. (2008). Tourism and Poverty Relief. Annals of Tourism Research, 35(1), 107-126.
5
Brida, J. G., Cortes-Jimenez, I., & Pulina, M. (2016). Has the Tourism-led Growth Hypothesis been validated? A Literature Review. Current Issues in Tourism, 19(5), 394-430.
6
Brida, J. G., Lanzilotta, B., Pereyra, J. S., & Pizzolon, F. (2015). A Nonlinear Approach to the Tourism-led Growth Hypothesis: The Case of the MERCOSUR. Current Issues in Tourism, 18(7), 647-666.
7
Cárdenas-García, P. J., Sánchez-Rivero, M., & Pulido-Fernández, J. I. (2015). Does Tourism Growth Influence Economic Development? Journal of Travel Research, 54(2), 206-221.
8
Cetin, G., Alrawadieh, Z., Dincer, M. Z., Istanbullu Dincer, F., & Ioannides, D. (2017). Willingness to Pay for Tourist Tax in Destinations: Empirical Evidence from Istanbul. Economies, 5(2), 21-31.
9
Chatziantoniou, I., Filis, G., Eeckels, B., & Apostolakis, A. (2013). Oil Prices, Tourism Income and Economic Growth: A Structural VAR Approach for European Mediterranean Countries. Tourism Management, 36, 331-341.
10
Chen, C. M., Lin, Y. L., & Hsu, C. L. (2017). Does Air Pollution Drive Away Tourists? A Case Study of the Sun Moon Lake National Scenic Area, Taiwan. Transportation Research Part D: Transport and Environment, 53, 398-402.
11
Copeland, B. R. (1991). Tourism, Welfare and De-industrialization in a Small Open Economy. Economica, 58(232), 515-529.
12
Dritsakis, N. (2004). Tourism as a Long-run Economic Growth Factor: an Empirical Investigation for Greece Using Causality Analysis. Tourism Economics, 10(3), 305-316.
13
Eugenio-Martin, J. L., Martín Morales, N., & Scarpa, R. (2004). Tourism and Economic Growth in Latin American Countries: A Panel Data Approach. FEEM Working Paper, Retrived from http://ssrn.com/abstract=504482.
14
Frederick, M. (1993). Rural Tourism and Economic Development. Economic Development Quarterly, 7(2), 215-224.
15
Furceri, D., & Zdzienicka, A. (2011). The Real Effect of Financial Crises in the European Transition Economies. Economics of Transition, 19(1), 1-25.
16
Hall, S. G. (1991). The Effect of Varying Length VAR Models on the Maximum Likelihood Estimates of Cointegrating Vectors. Scottish Journal of Political Economy, 38(4), 317-323.
17
---------- (1989). Practitioners Corner: Maximum Likelihood Estimation of Cointegration Vectors: An Example of the Johansen Procedure. Oxford Bulletin of Economics and Statistics, 51(2), 213-218.
18
Hatemi-J, A. (2016). On the Tourism-led Growth Hypothesis in the UAE: a Bootstrap Approach with Leveraged Adjustments. Applied Economics Letters, 23(6), 424-427.
19
Hazari, B. R., & Sgro, P. M. (2004). Tourism and Growth in a Dynamic Model of Trade. In Tourism, Trade and National Welfare (185-195). Bingley, West Yorkshire: Emerald Group Publishing Limited.
20
Helpman, E., & Krugman, P. R. (1985). Market Structure and Foreign Trade: Increasing Returns, Imperfect Competition, and the International Economy. Cambridge: MIT press.
21
Ibrahim, M. H. (2006). Integration or Segmentation of the Malaysian Equity Market: An Analysis of Pre-and Post-capital Controls. Journal of the Asia Pacific Economy, 11(4), 424-443.
22
Jiang, Y., & Ritchie, B. W. (2017). Disaster Collaboration in Tourism: Motives, Impediments and Success Factors. Journal of Hospitality and Tourism Management, 31, 70-82.
23
Johansen, S. (1992). Testing Weak Exogeneity and the Order of Cointegration in UK Money Demand Data. Journal of Policy Modelling, 14(3), 313-334.
24
Joshi, O., Poudyal, N. C., & Larson, L. R. (2017). The Influence of Socio-political, Natural, and Cultural Factors on International Tourism Growth: a Cross-country Panel Analysis. Environment, Development and Sustainability, 19(3), 825-838.
25
Katircioglu, S. T. (2009). Revisiting the Tourism-led-growth Hypothesis for Turkey Using the Bounds Test and Johansen Approach for Cointegration. Tourism Management, 30(1), 17-20.
26
Khan, S. A. R., Qianli, D., SongBo, W., Zaman, K., & Zhang, Y. (2017). Travel and Tourism Competitiveness Index: The Impact of Air Transportation, Railways Transportation, Travel and Transport Services on International Inbound and Outbound Tourism. Journal of Air Transport Management, 58, 125-134.
27
Khoshnevis Yazdi, S., Homa Salehi, K., & Soheilzad, M. (2017). The Relationship between Tourism, Foreign Direct Investment and Economic Growth: Evidence from Iran. Current Issues in Tourism, 20(1), 15-26.
28
Khoshnevis Yazdi, S., & Khanalizadeh, B. (2017). Tourism Demand: a Panel Data Approach. Current Issues in Tourism, 20(8), 787-800.
29
Lean, H. H., & Smyth, R. (2009). Asian Financial Crisis, Avian Flu and Terrorist Threats: Are Shocks to Malaysian Tourist Arrivals Permanent or Transitory? Asia Pacific Journal of Tourism Research, 14(3), 301-321.
30
---------- (2008). Are Malaysia's Tourism Markets Converging? Evidence from Univariate and Panel Unit Root Tests with Structural Breaks. Tourism Economics, 14(1), 97-112.
31
Liu, A., & Liu, H. H. J. (2008). Tourism Employment Issues in Malaysia. Journal of Human Resources in Hospitality & Tourism, 7(2), 163-179.
32
Liu, A. (2006). Tourism in Rural Areas: Kedah, Malaysia. Tourism Management, 27(5), 878-889.
33
Malik, M. A. S., Shah, S. A., & Zaman, K. (2016). Tourism in Austria: Biodiversity, Environmental Sustainability, and Growth Issues. Environmental Science and Pollution Research, 23(23), 24178-24194.
34
McCombie, J, S, L., Thirlwall, A, P., & Thompson, P. (1994). Economic Growth and the Balance-of-payments Constraint.
35
New York: St. Martin's Press.
36
Ministry of Tourism and Culture of Malaysia. (2016). Malaysia Tourism Statistics in Brief. Retrieved from
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http://www.tourism.gov.my/statistics.
38
Nassani, A. A., Aldakhil, A. M., Abro, M. M. Q., & Zaman, K (2017a). Effective International Tourism Management: A Strategic Approach. Social Indicators Research, 137, 1207-1224.
39
Nassani, A. A., Zaman, K., Aldakhil, A. M., & Abro, M. M. Q. (2017b). War Economy and Pleasure: Assessing the Effects of Military Expenditure on Tourism Growth. Quality & Quantity, 51(4), 1733-1754.
40
Nicolau, J. L. (2008). Characterizing Tourist Sensitivity to Distance. Journal of Travel Research, 47(1), 43-52.
41
Poh-Poh, W. (1990). Coastal Resources Management: Tourism in Peninsular Malaysia. ASEAN Economic Bulletin, 7(2), 213-221.
42
Qureshi, M. I., Hassan, M. A., Hishan, S. S., Rasli, A. M., & Zaman, K. (2017). Dynamic Linkages between Sustainable Tourism, Energy, Health and Wealth: Evidence from Top 80 International Tourist Destination Cities in 37 Countries. Journal of Cleaner Production, 158, 143-155.
43
Sajjad, F., Noreen, U., & Zaman, K. (2014). Climate Change and Air Pollution Jointly Creating Nightmare for Tourism Industry. Environmental Science and Pollution Research, 21(21), 12403-12418.
44
Saha, S., Su, J. J., & Campbell, N. (2017). Does Political and Economic Freedom Matter for Inbound Tourism? A Cross-national Panel Data Estimation. Journal of Travel Research, 56(2), 221-234.
45
Salleh, N. H. M., Othman, R., & Ramachandran, S. (2007). Malaysia’s Tourism Demand from Selected Countries: The ARDL Approach to Cointegration. International Journal of Economics and Management, 1(3), 345-363.
46
Salleh, N. H. M., Siong-Hook, L., Ramachandran, S., Shuib, A., & Noor, Z. M. (2008). Asian Tourism Demand for Malaysia: A Bound Test Approach. Contemporary Management Research, 4(4), 351-368.
47
Shah, I. A., & Zaman, K. (2014). Exploring the Relationship between Tourism Development, Economic Growth and Exchange Rate in Oman. South Asian Journal of Tourism and Heritage, 7(1), 29-51.
48
Singh, A. (1997). Asia Pacific Tourism Industry: Current Trends and Future Outlook. Asia Pacific Journal of Tourism Research, 2(1), 89-99.
49
Szivas, E., & Riley, M. (1999). Tourism Employment during Economic Transition. Annals of Tourism Research, 26(4), 747-771.
50
Tang, C. F., & Tan, E. C. (2015). Does Tourism Effectively Stimulate Malaysia's Economic Growth? Tourism Management, 46, 158-163.
51
Tang, C. F. (2011). Old Wine in New Bottles: Are Malaysia's Tourism Markets Converging? Asia Pacific Journal of Tourism Research, 16(3), 263-272.
52
Tsui, K. W. H. (2017). Does a Low-cost Carrier Lead the Domestic Tourism Demand and Growth of New Zealand? Tourism Management, 60, 390-403.
53
Tsui, K. W. H., Yuen, A. C. L., & Fung, M. K. Y. (2018). Maintaining Competitiveness of Aviation Hub: Empirical Evidence of Visitors to China via Hong Kong by Air Transport. Current Issues in Tourism, 21(11), 1260-1284.
54
Wells, R. J. G. (1982). Tourism Planning in a Presently Developing Country: the Case of Malaysia. Tourism Management, 3(2), 98-107.
55
Yap, G. (2011). Modelling the Spillover Effects of Exchange Rates on Australia's Inbound Tourism Growth. SSRN, Retrived from
56
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1789645.
57
Yeoman, I., Lennon, J. J., Blake, A., Galt, M., Greenwood, C., & McMahon-Beattie, U. (2007). Oil Depletion: What Does This Mean for Scottish Tourism? Tourism Management, 28(5), 1354-1365.
58
Zain, Y. (2005). Malaysia Second Most Visited. New Straits Times, 22-23.
59
Zaman, K., Moemen, M. A. E., & Islam, T. (2017). Dynamic Linkages between Tourism Transportation Expenditures, Carbon Dioxide Emission, Energy Consumption and Growth Factors: Evidence from the Transition Economies. Current Issues in Tourism, 20(16), 1720-1735.
60
Zaman, K., Shahbaz, M., Loganathan, N., & Raza, S. A. (2016). Tourism Development, Energy Consumption and Environmental Kuznets Curve: Trivariate Analysis in the Panel of Developed and Developing Countries. Tourism Management, 54, 275-283.
61
Zhang, H. Q., & Kulendran, N. (2017). The Impact of Climate Variables on Seasonal Variation in Hong Kong Inbound Tourism Demand. Journal of Travel Research, 56(1), 94-107.
62
ORIGINAL_ARTICLE
The Impact of Food Stamp Program on Relative Food Consumption and Food Choices
I n this paper, the effects of the Food Stamp (FS) Program (now referred to as Supplemental Nutrition Assistance Program-SNAP) on individuals’ food choices are evaluated. In other words, I examine how households' food choice or relative food consumption is changed by FS participation. For this purpose six food groups are created using 2016 Consumer Expenditure Diary Survey (CEDS) results and Consumer Price Index (CPI). Five of these food groups are food consumed at home which are bakery products, dairy products, meat and meat products, vegetables, and others. Also food consumed away from home is included as sixth group. Multinomial probit and conditional logit models are applied to analyze the data set. The analyses results show that FS can change individuals' food choices by decreasing the price effect on food since FS is a kind of income transfer which subsequently affects participants' price sensitivity. In addition, the results show that FSusage may increase the relative meat consumption of households and food consumed away from home in comparison to other food groups.
https://ier.ut.ac.ir/article_67879_ea866ee426f9656297214b1ee44cd970.pdf
2018-10-01
1138
1148
10.22059/ier.2018.67879
Keywords: Consumption
Conditional Logit
Food Choices
Food Demand
Food Stamp Program
Multinomial Probit
SNAP. JEL Classification: D90
D91
E21
I38
Filiz
Guneysu Atasoy
filizatasoy@osmaniye.edu.tr
1
Department of Economics, Osmaniye Korkut Ata University, Osmaniye, Turkey
LEAD_AUTHOR
Basiotis, P., Brown, M., Johnson, S. R., & Morgan, K. J. (1983). Nutrient Availability, Food Costs, and Food Stamps. American Journal of Agricultural Economics, 65(4), 685-693.
1
Beatty, T. K., & Tuttle, C. J. (2014). Expenditure Response to Increases in In-kind Transfers: Evidence from the Supplemental Nutrition Assistance Program. American Journal of Agricultural Economics, 97(2), 390-404.
2
Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics: Methods and Applications. Cambridge: Cambridge University Press.
3
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