( )

1395/12/2 1395/3/27. 1392 1363. ( ) ( ). ( ) ( ).. 50. 48. : Email: h.faaljou@urmia.ac.ir Email: molabahrami.ahmad@gmail.com Email: hossienamiri@gmail.com ( ) 1396

90 24 / 102...(Demombynes and Ozler, 2005)..(1390 1390 )....(Blau and Blau, 1982) (Allen,.1996) (Bourguignon, Nunez and.sanchez, 2003; Neumayer, 2005; Choe, 2008)

103.(Cook and Zarkin, 1985; Cantor and Land, 1985) 1 (1987)...(Fallahi and Rodriguez, 2014).... (1390) (1390) (1392) (1391).. 1. Chiricos

90 24 / 104 ( ) ( ). 1392 1363..1.1-1..(Allen, 1996).....(Ibid.)..1-2.

105 2 (1985) 1 (1985) (1985). 3.... 4. (1985)......(Fallahi and Rodriguez, 2014).2. 1. Cook and Zarkin 2. Cantor and Land 3. Motivation Effects 4. Opportunity Effects

90 24 / 106.2-1 1 (2005).. 2 (2008).. 3 (2009).. 1/46 4 (2012).. (2014)....2-2 (1390)... 1. Demombynes and Ozler 2. Choe 3. Scorzafave and Soares 4. Chintarkarn and Herzer

107 (1390) 1385 1382.. (1391) 1385 1345.. (1392) ARDL 1390 1370...3 (1989) y t m. i s i. s i y t. s i = 12,,..., m y t i = i si = i s i. i y t 1. 1. Markov Chain s i

90 24 / 108 :. s i P( s = j s, s,..., s ) = P( s = j s = i) = P t 1 2 t 1 t t 1 ij i P ij (1) 1 m P = Pij i, j= 12,,..., m π i.. j (1) : m i = 12,,.., m m j = 1 i P ij = 1 (2) y t t (3) : π t = [ π1 π 2.. π m]. s (Brock, 2008) π = π = t+ s P πt t+ 1 Pπ t y t :(Hamilton, 1989; Brock, 2008) yt = µ s + ϕ y (4) t s t t 1 + εt s t ε t (4).S t =0/1 1 (4).. MS-AR(1). MS-AR(1) q (2008) (2014) : 1. Probability Transition Matrix

109 yt = µ s + α t sx t t + α sx t t + α sx t t + α sx t t + α sx t t + α sx t t + α sx t t + ε (5) 1 1 2 2 3 3 4 4 5 5 6 6 7 7 t. x 1t x 4 t x 3t x 2t y t (CPI) x 6t x 5t 1 α ist. i = 12,,..., 6 s s t =0. t = 1 : P00 1 P11 (6) P = P P 1 00 11 P 11 P 00 1 P 00 1 P 11.. 2.(Hamilton, 1994).4.1.. OX-Metrixs6.2

90 24 / 110 1. 1392 1363.. 3 100 80 60 40 1363-1392.1 20 0-20 -40 136313651367136913711373137513771379138113831385138713891391. : 1363-1392.2 14.6 12.6 10.6 8.6 6.6 4.6 2.6 0.6 1363 1365 1367 1369 1371 1373 1375 1377 1379 1381 1383 1385 1387 1389 1391. :

111 1392 1363 2. 1392 1363 2. 15 9 3. 1363-1392. 0.44 1363-1392.3 0.42 0.4 0.38 0.36 0.34 0.32 1363 1365 1367 1369 1371 1373 1375 1377 1379 1381 1383 1385 1387 1389 1391. :. 3 0/41 0/39 1380 1363 1383 1380.. 0/36.

90 24 / 112. 1. SHINT-t-test KSS 2... 3. 1 SHINT-t-test -3/71-3/16-2/58-2/71-2/75-2/72-6/045-2/60-2/72-6/24-2/81-3/03-2/75-3/27-3/61 KSS.1 KSS -2/64-2/76-3/05-2/69-3/66-3/51-2/59-2/69-3/07-2/81-3/21-4/33-3/64-2/78-3/11 ) ( 1. Kapetanios and Shin and Shell (KSS) Test 2. Exponential Smooth Transition Autoregressive Model (ESTAR). MATLAB.3

113 SHINT-t-test -4/55-2/82-3/86-4/28-2/70-2/78 KSS -3/42-3/82-2/76-3/87-3/00-3/32. -2/66 90 :. :.. 26/97. 99. 0/002.. OX.. 2

90 24 / 114.2-47/43-30/26-32/33 106/85 74/53 80/63. : 2. 3....3 µ 0 6/81 0/00 µ 1 9/51 ϕ 0/88 σ 0 0/082 0/00 σ 1 1/12 P 00 0/85 P 11 0/046 0/ 32 Nonlinear LR test 26/97. : 3. 3. 6/81 0/88 9/51

115.. 4. 0/05 0/85...4 1 0.8 0.6 0.4 0.2 0 1363 1365 1367 1369 1371 1373 1375 1377 1379 1381 1383 1385 1387 1389 1391. : 1374 1369 1376 1368 1363 ( ). ( ) 1392 (5) 4... 1

90 24 / 116 21/78. 10 5...4 t µ 0 3/01 6/25 0/00 µ 1 3/24 3/12 0/ 01 α 10 0/99 2/08 0/ 05 α 11 0/85 2/48 0/ 03 α 20 0/63 5/48 α 21 0/57 2/57 0/ 02 α 30 0/97 9/05 α 31 2/54 7/50 α 40 1/46 5/71 α 41 2/30 6/31 α 50 0/90 8/96 α 51 2/15 7/46 α 60-0/03-2/31 0/ 04 α 61-0/19-2/84 0/ 01 σ 0 3/53 3/52 σ 1 6/33 5/17 P 00 0/50 3/26 0/ 01 0/48 3/46 P 01 Log- likelihood -107/ 99 Nonlinear LR test 21/78 0/02. : 4.

117. 0/85 0/99.. 0/99. 0/85. 0/85. 0/14. 95.. 0/57 0/63 0/57 0/63. 0/06 4. 0/97 2/54.. 2/54 0/97

90 24 / 118.. 2/30 1/46. 1/46. 2/30. 0/84 4.. 2/15 0/9 2/15 0/9.. 1/25. 0/19 0/03 6/33 0/50. 3/53 5. 0/48 ( ) ( ).

119.5 1.20E+00 1.00E+00 8.00E-01 6.00E-01 4.00E-01 2.00E-01 0.00E+00 1363 1365 1367 1369 1371 1373 1375 1377 1379 1381 1383 1385 1387 1389 1391. : 1374 1372 1370 1368 1364 90 1392 1387 1384 1382 1378 1375 1371 1369 1363. 1391 1388 1386 1385 1381 1379 1377 1376 1373. 90.5 5 8/50 10 1 0/03 2 4/28 0/13 0/87 0/12. :. 5.

90 24 / 120. 6..6 100 80 60 40 20 0-20 -40-60 1364 1366 1368 1370 1372 1374 1376 1378 1380 1382 1384 1386 1388 1390 real fitted. :..5

121. 1392 1363..... ) ( ) (. ( ) 0/97 0/99 1/46 ). ( 2/15 2/30 2/54.

90 24 / 122. 50. 48. (1390) (1390). (1392).....

123 Ĥ.1.».(1384).2.(68)6.(1391).3.(29)8 ««( )».(1390).4.(3) 11. ( ).5 )».(1392).6.(6)2 «(1390 1370.(1390).7.(4)11 8. Allen, R. (1996). "Socioeconomic Conditions and Property Crime: a Comprehensive Review and Test of the Professional literature", American Journal of Economics and Sociology 55 (3). 9. Blau, J. R. and P. M. Blau (1982). "The Cost of Inequality: Metropolitan Structure and Violent Crime", American Sociological Review, 47 (1). 10. Bourguignon, F., J. Nunez and F. Sanchez (2003). "What Part of the Income Distribution Matters for Explaining Property Crime?", The Case of Colombia, Documento CEDE, 2003-2007. 11. Brock, C. (2008). Introductory Econometrics for Finance, Cambridge University Press. 12. Cantor, D. and K. C. Land (1985). "Unemployment and Crime Rates in the post-world War II United States: A Theoretical and Empirical Analysis", American Sociological Review, 50 (3). 13. Chintarkarn, P. and D. Herzer (2012). "More Inequality, More Crime? A Panel Cointegration Analysis for the United States", Economic Letters, 116. 14. Chiricos, T. (1987). Rates of Crime and Unemployment: an Analysis of Aggregate Research Evidence, Social Problems, 34(2). 15. Choe, J. (2008). "Income Inequality and Crime in the United States", Economic Letters, 101(1).

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