727 Views
38 Downloads |
The role of economic and political factors in budget forecasting errors: evidence from Turkey’s metropolitan municipalities for the period 2011-2022
Berat Kara*
Article | Year: 2025 | Pages: 273 - 307 | Volume: 49 | Issue: 2 Received: June 14, 2024 | Accepted: September 25, 2024 | Published online: June 7, 2025
|
FULL ARTICLE
FIGURES & DATA
REFERENCES
CROSSMARK POLICY
METRICS
LICENCING
PDF
|
Variable
|
Explanation
|
Acronym
|
|
Revenue forecasting error
|
Annual budget revenue forecasting absolute error
rate
|
REV
|
|
Expenditure forecasting error
|
Annual budget expenditure forecasting absolute error
rate
|
EXP
|
|
Inflation
|
Annual local inflation rate
|
INF
|
|
Unemployment
|
Annual local unemployment rate
|
UNP
|
|
Per capita GDP
|
Annual local per capita GDP
|
GDP
|
|
Export-to-import ratio
|
Local Export-to-import ratio: (Export / Import)
|
EIR
|
|
Population growth rate
|
Annual local population growth rate
|
PGR
|
|
Mayor’s political party
|
“1” if mayor’s political party is right-wing, “0”
otherwise
|
RLP
|
|
Mayor’s re-candidacy
|
“1” if mayor re-candidate for the next election, “0”
otherwise
|
REL
|
|
Municipality election periods
|
“1” if municipality elections were held that year, “0”
otherwise
|
ELC
|
|
Budget surplus
|
“1” if budget surplus, “0” otherwise
|
BDM
|
Source: TURKSTAT, Central Bank of the Republic of Turkey, Ministry of Treasury and Finance.
|
Variable
|
Mean
|
Std. dev.
|
Min.
|
Max.
|
Obs.
|
|
EXP
|
overall
|
0.1887
|
0.1870
|
0.0001
|
1.1190
|
N
|
180
|
|
between
|
|
0.0693
|
0.0699
|
0.3068
|
n
|
15
|
|
within
|
|
0.1745
|
-0.0971
|
1.0452
|
T
|
12
|
|
REV
|
overall
|
0.1707
|
0.1325
|
0.0021
|
0.7005
|
N
|
180
|
|
between
|
|
0.0673
|
0.0729
|
0.3074
|
n
|
15
|
|
within
|
|
0.1154
|
-0.0332
|
0.6644
|
T
|
12
|
|
BDM
|
overall
|
0.3555
|
0.4800
|
0
|
1
|
N
|
180
|
|
between
|
|
0.2076
|
0
|
0.75
|
n
|
15
|
|
within
|
|
0.4358
|
-0.3944
|
1.1888
|
T
|
12
|
|
INF
|
overall
|
18.4078
|
18.4690
|
5.43
|
73.3
|
N
|
180
|
|
between
|
|
0.4282
|
17.6466
|
19
|
n
|
15
|
|
within
|
|
18.4643
|
4.8787
|
73.1670
|
T
|
12
|
|
UNP
|
overall
|
10.3150
|
3.3335
|
3.6
|
23.4
|
N
|
180
|
|
between
|
|
2.8017
|
6.575
|
15.6083
|
n
|
15
|
|
within
|
|
1.9351
|
1.6066
|
18.1066
|
T
|
12
|
|
GDP
|
overall
|
10,464.98
|
4,376.786
|
3,043
|
20,883
|
N
|
180
|
|
between
|
|
4,301.055
|
3,901.333
|
17,653.08
|
n
|
15
|
|
within
|
|
1,339.401
|
5,983.394
|
13,952.64
|
T
|
12
|
|
EIR
|
overall
|
1.4758
|
1.6461
|
0.3546
|
10.4561
|
N
|
180
|
|
between
|
|
1.1814
|
0.6403
|
5.3017
|
n
|
15
|
|
within
|
|
1.1831
|
-2.6785
|
8.0300
|
T
|
12
|
|
PGR
|
overall
|
14.7243
|
11.1765
|
-23.24
|
39.09
|
N
|
180
|
|
between
|
|
8.6468
|
-2.1208
|
29.8908
|
n
|
15
|
|
within
|
|
7.3986
|
-13.464
|
48.866
|
T
|
12
|
|
RLP
|
overall
|
0.7444
|
0.4373
|
0
|
1
|
N
|
180
|
|
between
|
|
0.3542
|
0
|
1
|
n
|
15
|
|
within
|
|
0.2712
|
-0.0055
|
1.3277
|
T
|
12
|
|
REL
|
overall
|
0.5888
|
0.4934
|
0
|
1
|
N
|
180
|
|
between
|
|
0.3556
|
0
|
1
|
n
|
15
|
|
within
|
|
0.3532
|
-0.3277
|
1.3388
|
T
|
12
|
|
ELC
|
overall
|
0.1666
|
0.3737
|
0
|
1
|
N
|
180
|
|
between
|
|
0
|
0.1666
|
0.1666
|
n
|
15
|
|
within
|
|
0.37375
|
0
|
1
|
T
|
12
|
Note: For all variables: N=180, n=15, T=12. Source: Author’s calculations.
|
REV
|
EXP
|
BDM
|
INF
|
UNP
|
GDP
|
EIR
|
PGR
|
RLP
|
REL
|
ELC
|
|
REV
|
1
|
|
|
|
|
|
|
|
|
|
|
|
EXP
|
0.4953
|
1
|
|
|
|
|
|
|
|
|
|
|
BDM
|
-0.1505
|
0.0248
|
1
|
|
|
|
|
|
|
|
|
|
INF
|
0.1057
|
0.4426
|
0.0511
|
1
|
|
|
|
|
|
|
|
|
UNP
|
-0.2229
|
-0.0935
|
0.232
|
0.0323
|
1
|
|
|
|
|
|
|
|
GDP
|
0.0362
|
0.3941
|
0.1327
|
0.8105
|
0.0975
|
1
|
|
|
|
|
|
|
EIR
|
0.0329
|
0.0457
|
-0.1073
|
0.0038
|
-0.1091
|
-0.1018
|
1
|
|
|
|
|
|
PGR
|
0.0476
|
-0.0175
|
0.2151
|
-0.1706
|
0.134
|
-0.0091
|
-0.133
|
1
|
|
|
|
|
RLP
|
-0.0326
|
-0.1492
|
-0.0171
|
-0.1224
|
-0.1905
|
-0.3044
|
-0.1116
|
-0.1572
|
1
|
|
|
|
REL
|
0.0497
|
-0.0384
|
-0.0398
|
-0.0607
|
-0.0893
|
0.0539
|
-0.187
|
0.1035
|
-0.1789
|
1
|
|
|
ELC
|
0.1203
|
0.1016
|
0.0415
|
-0.2056
|
0.1235
|
-0.1068
|
0.0026
|
0.0228
|
-0.0114
|
-0.0808
|
1
|
Source: Author’s calculations.
|
Municipality
|
Category
|
MPE
|
MAPE
|
NoNEP
|
NoPEP
|
|
Istanbul
|
Revenue
|
11.17
|
14.42
|
4
|
8
|
|
Expenditure
|
5.78
|
13.21
|
6
|
6
|
|
Ankara
|
Revenue
|
-4.66
|
12.62
|
8
|
4
|
|
Expenditure
|
-1.97
|
16.88
|
11
|
1
|
|
Izmir
|
Revenue
|
-1.62
|
7.29
|
8
|
4
|
|
Expenditure
|
-7.92
|
11.92
|
11
|
1
|
|
Kocaeli
|
Revenue
|
-3.09
|
13.26
|
7
|
5
|
|
Expenditure
|
-6.72
|
20.91
|
9
|
3
|
|
Bursa
|
Revenue
|
-4.10
|
10.82
|
9
|
3
|
|
Expenditure
|
3.34
|
15.68
|
7
|
5
|
|
Antalya
|
Revenue
|
-12.89
|
30.74
|
9
|
3
|
|
Expenditure
|
-20.06
|
24.61
|
10
|
2
|
|
Konya
|
Revenue
|
-24.40
|
26.35
|
11
|
1
|
|
Expenditure
|
-12.54
|
29.20
|
11
|
1
|
|
Adana
|
Revenue
|
-21.00
|
23.72
|
11
|
1
|
|
Expenditure
|
-18.51
|
18.51
|
12
|
0
|
|
Tekirdag
|
Revenue
|
-7.030
|
20.03
|
7
|
5
|
|
Expenditure
|
-24.99
|
30.68
|
11
|
1
|
|
Gaziantep
|
Revenue
|
-9.11
|
14.14
|
9
|
3
|
|
Expenditure
|
-10.38
|
10.38
|
12
|
0
|
|
Kayseri
|
Revenue
|
2.30
|
20.68
|
8
|
4
|
|
Expenditure
|
-4.95
|
26.25
|
10
|
2
|
|
Sanliurfa
|
Revenue
|
-7.79
|
11.99
|
9
|
3
|
|
Expenditure
|
-15.87
|
15.87
|
12
|
0
|
|
Samsun
|
Revenue
|
-4.95
|
9.74
|
9
|
3
|
|
Expenditure
|
1.17
|
6.99
|
7
|
5
|
|
Ordu
|
Revenue
|
-9.86
|
17.60
|
11
|
1
|
|
Expenditure
|
3.23
|
18.91
|
7
|
5
|
|
Erzurum
|
Revenue
|
-20.52
|
22.64
|
10
|
2
|
|
Expenditure
|
-0.75
|
23.11
|
10
|
2
|
Note: NoNEP: number of negative error periods; NoPEP: number of positive error periods. Source: Author’s calculations based on the Ministry of Treasury and Finance data.
|
Coef.
|
Std. err
|
z
|
P > z
|
[95% Conf. Interval]
|
|
BDM
|
-0.0405
|
0.0198
|
-2.04
|
0.042**
|
-0.0794
|
-0.0015
|
|
INF
|
0.0021
|
0.0010
|
2.08
|
0.037**
|
0.0001
|
0.0040
|
|
UNP
|
-0.0100
|
0.0038
|
-2.62
|
0.009***
|
-0.0176
|
-0.0025
|
|
GDP
|
-0.0004
|
0.0004
|
-0.99
|
0.324
|
-0.0012
|
0.0004
|
|
EIR
|
0.0014
|
0.0068
|
0.21
|
0.831
|
-0.0119
|
0.0148
|
|
PGR
|
0.0018
|
0.0010
|
1.72
|
0.085*
|
-0.0002
|
0.0039
|
|
RLP
|
-0.0265
|
0.0294
|
-0.90
|
0.367
|
-0.0842
|
0.0311
|
|
REL
|
-0.0265
|
0.0228
|
-1.16
|
0.245
|
-0.0713
|
0.0182
|
|
ELC
|
0.0671
|
0.0241
|
2.78
|
0.006***
|
0.0197
|
0.1145
|
|
_cons
|
0.2659
|
0.0614
|
4.32
|
0.000
|
0.1453
|
0.3864
|
|
R-sq
|
within
|
0.1345
|
|
Obs. per group
|
min
|
12
|
|
between
|
0.0939
|
|
avg
|
12
|
|
overall
|
0.1179
|
|
max
|
12
|
|
Number of obs
|
180
|
|
Number of groups
|
15
|
|
Wald chi2(9)
|
25.14
|
|
Prob > chi2
|
0.0028
|
|
sigma_u
|
0.0594
|
|
sigma_e
|
0.1147
|
|
rho
|
0.2114
|
Source: Author’s calculations.
|
Coef.
|
Std. err
|
z
|
P > z
|
[95% Conf. Interval]
|
|
BDM
|
-0.0023
|
0.0269
|
-0.09
|
0.929
|
-0.0551
|
0.0503
|
|
INF
|
0.0046
|
0.0012
|
3.62
|
0.000***
|
0.0021
|
0.0071
|
|
UNP
|
-0.0089
|
0.0043
|
-2.05
|
0.040**
|
-0.0173
|
-0.0004
|
|
GDP
|
0.0001
|
0.0005
|
0.22
|
0.830
|
-0.0008
|
0.0011
|
|
EIR
|
0.0017
|
0.0084
|
0.20
|
0.839
|
-0.0147
|
0.0181
|
|
PGR
|
0.0008
|
0.0012
|
0.70
|
0.482
|
-0.0015
|
0.0033
|
|
RLP
|
-0.0489
|
0.0346
|
-1.41
|
0.158
|
-0.1167
|
0.0189
|
|
REL
|
-0.0221
|
0.0280
|
-0.79
|
0.429
|
-0.0770
|
0.0327
|
|
ELC
|
0.1060
|
0.0334
|
3.17
|
0.002***
|
0.0405
|
0.1715
|
|
_cons
|
0.2063
|
0.0717
|
2.88
|
0.004
|
0.0657
|
0.3469
|
|
R-sq
|
within
|
0.2848
|
|
Obs. per group
|
min
|
12
|
|
between
|
0.1755
|
|
avg
|
12
|
|
overall
|
0.2706
|
|
max
|
12
|
|
Number of obs.
|
180
|
|
Number of groups
|
15
|
|
Wald chi2(9)
|
64.53
|
|
Prob > chi2
|
0.0000
|
|
sigma_u
|
0.0332
|
|
sigma_e
|
0.1569
|
|
rho
|
0.0429
|
Source: Author’s calculations.
|
No
|
Hypothesis
|
Status
|
|
Revenue
|
Expenditure
|
|
1
|
Budgets
are forecasted with a high degree of inaccuracy.
|
Accept
|
Accept
|
|
2
|
Forecasting
errors are mostly in the negative direction.
|
Accept
|
Accept
|
|
3
|
A
budget surplus affects budget forecasting errors.
|
Accept
|
Reject
|
|
4
|
An
increase in inflation affects the forecasting error in a positive direction.
|
Accept
|
Accept
|
|
5
|
An
increase in the unemployment rate affects revenue forecasting errors negatively
and expenditure forecasting errors positively.
|
Accept
|
Accept
|
|
6
|
An
increase in per capita GDP increases errors in a positive direction.
|
Reject
|
Reject
|
|
7
|
As
the export-to-import ratio increases, forecasting errors also increase in a
positive direction.
|
Reject
|
Reject
|
|
8
|
As
the population growth rate increases, forecasting errors also increase in a
positive direction.
|
Accept
|
Reject
|
|
9
|
Mayor’s
political party affiliations affect the forecasting error.
|
Reject
|
Reject
|
|
10
|
Mayor’s
candidacy in the next elections affects revenue forecasting errors negatively
and expenditure forecasting errors positively.
|
Reject
|
Reject
|
|
11
|
Election
periods affect revenue forecasting errors negatively and expenditure
forecasting errors positively.
|
Accept
|
Accept
|
Source: Author’s calculations based on tables 4, 5 and 6 results.
|
(b)
|
(B)
|
(b-B)
|
sqrt(diag(V_b-V_B))
|
|
Fixed
|
Random
|
Difference
|
S.E.
|
|
BDM
|
-0.0426
|
-0.0405
|
-0.0021
|
0.0046
|
|
INF
|
0.0021
|
0.0021
|
0.0000
|
0.0004
|
|
UNP
|
-0.0107
|
-0.0100
|
-0.0006
|
0.0028
|
|
GDP
|
-0.0004
|
-0.0004
|
-0.0000
|
0.0002
|
|
EIR
|
0.0029
|
0.0014
|
0.0015
|
0.0034
|
|
PGR
|
0.0018
|
0.0018
|
-0.0000
|
0.0006
|
|
RLP
|
-0.0316
|
-0.0265
|
-0.0051
|
0.0177
|
|
REL
|
-0.0484
|
-0.0265
|
-0.0218
|
0.0112
|
|
ELC
|
0.0652
|
0.0671
|
-0.0018
|
0.0037
|
|
chi2(8)
|
7.8000
|
|
Prob>chi2
|
0.4531
|
|
(b)
|
(B)
|
(b-B)
|
sqrt(diag(V_b-V_B))
|
|
Fixed
|
Random
|
Difference
|
S.E.
|
|
BDM
|
-0.0184
|
-0.0023
|
-0.0161
|
0.0091
|
|
INF
|
0.0050
|
0.0046
|
0.0003
|
0.0008
|
|
UNP
|
-0.0062
|
-0.0089
|
0.0026
|
0.0050
|
|
GDP
|
-0.0001
|
0.0001
|
-0.0002
|
0.0004
|
|
EIR
|
-0.0010
|
0.0017
|
-0.0027
|
0.0066
|
|
PGR
|
-0.0002
|
0.0008
|
-0.0011
|
0.0011
|
|
RLP
|
-0.0625
|
-0.0489
|
-0.0135
|
0.0330
|
|
REL
|
-0.0621
|
-0.0221
|
-0.0400
|
0.0218
|
|
ELC
|
0.1001
|
0.1060
|
-0.0058
|
0.0067
|
|
chi2(8)
|
13.5000
|
|
Prob>chi2
|
0.0957
|
|
Models
|
|
Revenue
|
Expenditure
|
|
Var
|
sd = sqrt(Var)
|
|
Var
|
sd = sqrt(Var)
|
|
REV
|
0.0175
|
0.1325
|
EXP
|
0.0349
|
0.1870
|
|
e
|
0.0131
|
0.1147
|
e
|
0.0246
|
0.1569
|
|
u
|
0.0035
|
0.0594
|
u
|
0.0011
|
0.0332
|
|
chibar2(01)
|
18.8800
|
chibar2(01)
|
3.2800
|
|
Prob > chibar2
|
0.0000
|
Prob > chibar2
|
0.0352
|
|
Models
|
|
Revenue
|
Expenditure
|
|
F (1, 14)
|
0.463
|
2.032
|
|
Prob > F
|
0.507
|
0.176
|
|
Models
|
|
Revenue
|
Expenditure
|
|
Pr
|
0.177
|
0.112
|
|
Average absolute value of the off-diagonal elements
|
0.306
|
0.418
|
Afonso, A. and Silva, J., 2012. The Fiscal Forecasting Track Record of the European Commission and Portugal. I SEG Economics Working Paper, No. 37/2012/DE/UECE [ CrossRef]
Agostini, S. J., 1991. Searching for a Better Forecast: San Francisco’s Revenue Forecasting Model. Government Finance Review, 7, pp. 13-16.
Aizenman, J. and Hausmann, R., 2000. The Impact of Inflation on Budgetary Discipline. Journal of Development Economics, 63(2), pp. 425-449 [ CrossRef]
Alesina, A. and Paradisi, M., 2017. Political Budget Cycles: Evidence from Italian Cities. Economics & Politics, 29(2), pp. 157-177 [ CrossRef]
Allan, C. M., 1965. Fiscal Marksmanship, 1951-1963. Oxford Economic Papers, 17(2), pp. 317-327 [ CrossRef]
Asher, M. G., 1978. Accuracy of Budgetary Forecasts of Central Government, 1967-68 to 1975-76. Economic and Political Weekly, 13(8), pp. 423-432.
Auld, D. A., 1970. Fiscal Marksmanship in Canada. The Canadian Journal of Economics, 3(3), pp. 507-511 [ CrossRef]
Beckett-Camarata, J., 2006. Revenue Forecasting Accuracy in Ohio Local Governments. Journal of Public Budgeting, Accounting & Financial Management, 18(1), pp. 77-99 [ CrossRef]
Bee, A. and Moulton, S., 2015. Political Budget Cycles in U.S. Municipalities. Economics of Governance, 16(4), pp. 379-403 [ CrossRef]
Benito, B., Guillamon, M.-D. and Bastida, F., 2015. Budget Forecast Deviations in Municipal Governments: Determinants and Implications. Australian Accounting Review, 25(1), pp. 45-70 [ CrossRef]
Bohn, F., 2010. Disinformation and Political Budget Cycles.
Bohn, F., 2011. Political Budget Cycles: Can Disinformation Explain Country Group Differences.
Boukari, M. and Veiga, F. J., 2018. Disentangling Political and Institutional Determinants of Budget Forecast Errors: A Comparative Approach. Journal of Comparative Economics, 46(4), pp. 1030-1045 [ CrossRef]
Boylan, R. T., 2008. Political Distortions in State Forecasts. Public Choice, 136(3), pp. 411-427 [ CrossRef]
Bretschneider, S. and Gorr, W. L., 1992. Economic, Organizational, and Political Influences on Biases in Forecasting State Sales Tax Receipts. International Journal of Forecasting, 7(4), pp. 457-466 [ CrossRef]
Bretschneider, S. [et al.], 1989. Political and Organizational Influences on the Accuracy of Forecasting State Government Revenues. International Journal of Forecasting, 5(3), pp. 307-319 [ CrossRef]
Brogan, M., 2012. The Politics of Budgeting: Evaluating the Effects of the Political Election Cycle on State-Level Budget Forecast Errors. Public Administration Quarterly, 36(1), pp. 84-115.
Brouthers, L. E., 1986. Parties, Ideology and Elections: The Politics of Federal Revenues and Expenditures Forecasting. International Journal of Public Administration, 8(3), pp. 289-314 [ CrossRef]
Buettner, T. and Kauder, B., 2015. Political Biases Despite External Expert Participation: An Empirical Analysis of Tax Revenue Forecasts in Germany. Public Choice, 164(3), pp. 287-307 [ CrossRef]
Calabrese, T. and Williams, D., 2019. Bias Associated with Centrally Budgeted Expenditure Forecasts. In: D. Williams and T. Calabrese. The Palgrave Handbook of Government Budget Forecasting. Cham: Palgrave MacMillan, pp. 201-215 [ CrossRef]
Couture, J. and Imbeau, L., 2009. Do Governments Manipulate Their Revenue Forecasts? Budget Speech and Budget Outcomes in the Canadian Provinces. In: L. M. Imbeau. Do They Walk Like They Talk? Speech and Action in Policy Processes. New York: Springer, pp. 155-166[ CrossRef]
Davis, J. M., 1980. Fiscal Marksmanship in the United Kingdom, 1951-78. The Manchester School, 48(2), pp. 187-202 [ CrossRef]
Deus, J. D. and de Mendonça, H., 2017. Fiscal Forecasting Performance in an Emerging Economy: An Empirical Assessment of Brazil. Economic Systems, 41(3), pp. 408-419 [ CrossRef]
Deus, J. D. and de Mendonça, H. F., 2015. Empirical Evidence on Fiscal Forecasting in Eurozone Countries. Journal of Economic Studies, 42(5), pp. 838-860 [ CrossRef]
Fedotov, D. Y., 2017. Forecasting of Regional Economic Development and Budget Based on the Example of Irkutsk Oblast. Studies on Russian Economic Development, 28(4), pp. 416-422 [ CrossRef]
Feenberg, D. [et al.], 1989. Testing The Rationality of State Revenue Forecasts. The Review of Economics and Statistics, 71(2), pp. 300-308 [ CrossRef]
Forrester, J. P., 1991. Budgetary Constraints and Municipal Revenue Forecasting. Policy Sciences, 24(4), pp. 333-356 [ CrossRef]
Fullerton, T., 1989. A Composite Approach to Forecasting State Government Revenues: Case Study of the Idaho Sales Tax. International Journal of Forecasting, 5(3), pp. 373-380 [ CrossRef]
Gentry, W. M., 1989. Do State Revenue Forecasters Utilize Available Information? National Tax Journal, 42(4), pp. 429-439 [ CrossRef]
Geys, B., Goeminne, S. and Smolders, C., 2008. Political Fragmentation and Projected Tax Revenues: Evidence from Flemish Municipalities. International Tax and Public Finance, 15(3), pp. 297-315 [ CrossRef]
Grizzle, G. and Klay, W. E., 1994. Forecasting State Sales Tax Revenues: Comparing the Accuracy of Different Methods. State & Local Government Review, 26(3), pp. 142-152.
Haan, J. D., and Mink, M., 2005. Has the Stability and Growth Pact Impeded Political Budget Cycles in the European Union? CESifo Working Paper Series, No. 1532 [ CrossRef]
Heinemann, F., 2006. Planning or Propaganda? An Evaluation of Germany’s Medium-term Budgetary Planning. FinanzArchiv / Public Finance Analysis, 62(4), pp. 551-578 [ CrossRef]
Holm-Hadulla, F., Hauptmeier, S. and Rother, P., 2012. The Impact of Expenditure Rules on Budgetary Discipline over the Cycle. Applied Economics, 44(25), pp. 3287-3296 [ CrossRef]
Islam, A. R., 1999. Forecast Performances of Provincial Government Revenue Estimates in Canada. Simon Fraser University, Doctoral Thesis.
Jones, V. D., Bretschneider, S. and Gorr, W., 1997. Organizational Pressures on Forecast Evaluation: Managerial, Political, and Procedural Influences. Journal of Forecasting, 16(4), pp. 241-254 [ CrossRef]
Kara, B., 2024a. The Performance of Medium-Term Budgeting in Türkiye: An Analysis of Budget Forecasts. Yönetim ve Ekonomi Dergisi, 31(4), pp. 659-676 [ CrossRef]
Kara, B., 2024b. Türkiye'de Kamu Bütçesi Tahminlerinin Gerçekliği. Ankara: Gazi Kitabevi.
Kauder, B., Potrafke, N. and Schinke, C., 2017. Manipulating Fiscal Forecasts: Evidence from the German States. CESifo Working Paper, No. 6310 [ CrossRef]
Khan, A., 2019. Fundamentals of Public Budgeting and Finance. Palgrave Macmillan [ CrossRef]
Khan, T., Hussain, A. and Malik, Z. K., 2018. Fiscal Marksmanship in Pakistan: With Special Focus on Province Khyber Pakhtunkhwa. Pakistan Journal of Economic Studies, 1(1), pp. 21-43.
Krause, G. A. and Corder, J., 2007. Explaining Bureaucratic Optimism: Theory and Evidence from U.S. Executive Agency Macroeconomic Forecasts. The American Political Science Review, 101(1), pp. 129-142 [ CrossRef]
Krol, R., 2013. Evaluating State Revenue Forecasting under a Flexible Loss Function. International Journal of Forecasting, 29(2), pp. 282-289 [ CrossRef]
Lago-Peñas, I. and Lago-Peñas, S., 2008. Explaining Budgetary Indiscipline: Evidence from Spanish Municipalities. Public Finance and Management, 8(1), pp. 1-43 [ CrossRef]
Lalvani, M., 1999. Elections and Macropolicy Signals: Political Budget Cycle Hypothesis. Economic and Political Weekly, pp. 2676-2681.
Larkey, P. and Smith, R., 1989. Bias in the Formulation of Local Government Budget Problems. Policy Sciences, (22), pp. 123-166 [ CrossRef]
Lazar, C. and Andrei, J., 2006. The Budget - an Instrument for Forecasting. Multiyear Budgeting. Economic Insights – Trends and Challenges, 3, pp. 41-46.
Leal, T. [et al.], 2008. Fiscal Forecasting: Lessons from the Literature and Challenges. Fiscal Studies, 29(3), pp. 347-386 [ CrossRef]
Lee, T.-H. and Kwak, S., 2020. Revenue Volatility and Forecast Errors: Evidence From Korean Local Governments. Local Government Studies, 46(1), pp. 1-16 [ CrossRef]
Mayper, A. G., Granof, M. and Giroux, G., 1991. An Analysis of Municipal Budget Variances. Accounting, Auditing & Accountability Journal, 4(1), pp. 29-50 [ CrossRef]
McCollough, J. and Frank, H., 1992. Incentives for Forecasting Reform Among Local Finance Officers. Public Budgeting and Financial Management, 4(2), pp. 407-429.
Merola, R. and Pérez, J., 2013. Fiscal Forecast Errors: Governments versus Independent Agencies? European Journal of Political Economy, 32, pp. 285-299 [ CrossRef]
Mink, M. and de Haan, J., 2006. Are There Political Budget Cycles in the Euro Area? E uropean Union Politics, 7(2), pp. 191-211 [ CrossRef]
Oliver, X. and Villalonga, J., 2018. The Determinants of Regional Budget Forecast Errors in Federal Economies: Spain 1995-2013. Hacienda Pública Española / Review of Public Economics, 226(3), pp. 85-121 [ CrossRef]
Penner, R. G., 2001. Errors in Budget Forecasting. Washington D.C.: The Urban Institute.
Pina, Á. M. and Venes, N., 2011. The Political Economy of EDP Fiscal Forecasts: An Empirical Assessment. European Journal of Political Economy, 27(3), pp. 534-546 [ CrossRef]
Reddick, C. G., 2008. Evaluating Revenue Forecasting in City Governments: A Survey of Texas Finance Directors. In: J. Sun and T. Lynch. Government Budget Forecasting Theory and Practice. New York: Taylor & Francis Group, pp. 305-324 [ CrossRef]
Ríos, A.‐M. [et al.], 2018. The Influence of Transparency on Budget Forecast Deviations in Municipal Governments. Journal of Forecasting, 37(4), pp. 457-474 [ CrossRef]
Schroeder, L., 2007. Forecasting Local Revenues and Expenditures. In: A. Shah. Local Budgeting. Washington: The World Bank, pp. 53-79 [ CrossRef]
Sedmihradská, L. and Čabla, A., 2013. Budget Accuracy in Czech Municipalities and the Determinants of Tax Revenue Forecasting Errors. Ekonomická revue - Central European Review of Economic Issues, 16(4), pp. 197-206 [ CrossRef]
Stevenson, W. J., 2012. Operations Management. New York: The McGraw-Hill Irwin.
Strauch, R., Hallerberg, M. and Hagen, J., 2004. Budgetary Forecasts in Europe - the Track Record of Stability and Convergence Programmes. European Central Bank Working Papers, No. 307 [ CrossRef]
Sturm, J.-E., Jong-A-Pin, R. and de Haan, J., 2012. Using Real-Time Data to Test for Political Budget Cycles. KOF Working papers, No 12-313 [ CrossRef]
Voorhees, W., 2006. Consistent Underestimation Bias, the Asymmetrical Loss Function, and Homogeneous Sources of Bias in State Revenue Forecasts. Journal of Public Budgeting, Accounting & Financial Management, 18(1), pp. 61-76 [ CrossRef]
Williams, D. W. and Calabrese, T., 2016. The Status of Budget Forecasting. Journal of Public and Nonprofit Affairs, 2(2), pp. 127-160 [ CrossRef]
Willoughby, K. and Guo, H., 2008. The State of the Art: Revenue Forecasting in US State Governments. In: J. Sun and T. Lynch. Government Budget Forecasting: Theory and Practice. Taylor & Francis Group, pp. 27-42 [ CrossRef]
Zakaria, M. and Ali, S., 2010. Fiscal Marksmanship in Pakistan. The Lahore Journal of Economics, 15(2), pp. 113-133 [ CrossRef]
|
|
June, 2025 II/2025
|