Silviase/jgov_v1.4_full_ann huggingface dataset:
[Figure 13 about here.]

[Figure 14 about here.]

[Figure 15 about here.]

EIRFs to demand shock at 1990:Q1 and 2000:Q1 are shown in Figure 16 and 17. The responses in Figure 17 are more volatile rather than 16 because the stabilizing effect of monetary policy is lost under the liquidity trap. These results are consistent with Section 4.2, Woodford (2003). Following Eggertsson and Woodford (2003), this problem is able to be solved by adopting the flexible 2% – 3% targeted rate of inflation based on GDP deflator.

[Figure 16 about here.]

[Figure 17 about here.]

EIRFs to supply shock at 1990:Q1 and 2000:Q1 are shown in Figure 18 and 19. The responses in Figure 19 are more volatile rather than 18 because the stabilizing effect of monetary policy is lost under the liquidity trap. These results are consistent with Section 4.2, Woodford (2003).

[Figure 18 about here.]

[Figure 19 about here.]

In practice, the Hodrick and Prescott (1997) filter is often used to estimate the natural output of the Japanese economy. However, whether the HP filter and the magic number, which are suggested in Hodrick and Prescott (1997), are appropriate for estimation of Japanese natural output is an open question. Urasawa (2008) uses the Baxter and King (1999) filter to provide the stylized facts of Japanese business cycles. Our method is an alternative to these filters, and it is “structural” estimation of time-varying economic trends. In Figure 20, we compare our annualized estimates of output gap with estimates of the HP filter and the CF filter. In the upper panel of Figure 20, we show our estimate (the black line) and the estimate of the HP filter (the blue line). From 1985 to the mid-1990s the black line is different from the blue one. The blue line indicates that the output gap is negative in the late 1980s and positive in the early 1990s. In the late 1980s, Japanese economy was in the “bubble” economy, and in the early 1990s, was in the “Heisei” recession. The output gap based on the HP filter is not consistent with these facts, and the one based on our method is consistent with them. Before the mid-1990s, our method is better than the HP filter. The black line coincides with the blue one from the mid-1990s to the 2000s. In the lower panel of Figure 20, we show our estimate (the black line) and the estimate of the CF filter (the green line). The green line is much smoother rather than the black line, and the black one coincides with the green one from the late 1990s to the 2000s. We conclude that our estimate of output gap relatively coincides with the estimates, which are calculated by the HP/CF filters, although our method is totally different from the filters.

[Figure 20 about here.]
way is ambiguous because parameters in DSGE models are time-varying. If we calculate impulse response function at time t, the results of them may be meaningless because the parameters may have changed at time $ t+1 $ . Canova and Gambetti (2006) proposes the use of generalized impulse response functions in time-varying structural vector autoregressions. However, in time-varying analysis of DSGE models, it is an open question. We assume that the timings of when the economy is trapped in a liquidity trap and its subsequent escaped from it are given. The endogenous timings are our future work.

## A Data Source

We use quarterly macroeconomic data on the Japanese economy from 1981:Q1 to 2007:Q4.

- Uncollateralized overnight call rate, averaged over three months (Bank of Japan): uncollateralized overnight call rate, monthly average (July 1985-December 2007) and collateralized overnight call rate, monthly average (January 1981 - July 1985) are linked at July 1985.

http://www.boj.or.jp/en/theme/research/stat/market/index.htm

• Seasonally-adjusted real/nominal GDP (Cabinet Office): quarterly estimates of GDP, chained, (1994:Q1-2006:Q3, Reference-year = 2000) and quarterly estimates of GDP, fixed-based, (1981:Q1-1994:Q1, Base-year = 1995) are linked at 1994:Q1.

http://www.esri.cao.go.jp/en/sna/menu.html

http://www.esri.cao.go.jp/en/sna/qe081-2/gdemenuea.html

http://www.esri.cao.go.jp/en/sna/qe052-2/gdemenuebr.html

- Seasonally-adjusted GDP deflator (Cabinet Office): the deflator is calculated from seasonally-adjusted real/nominal GDP.

• Seasonally-adjusted labor force population, averaged over three months (Ministry of Internal Affairs and Communications): January 1981 - December 2007

http://www.stat.go.jp/english/data/roudou/lngindex.htm

## B Simulation Setting

The discount factor, $ \beta $ , is calibrated to be 0.99. We use uniform distributions for initial prior distributions of states, time-varying parameters, and parameters: $ unifom(-1,1) $ for states, $ unifom(0,1) $ for time-varying parameters, and $ unifom(0,0.2) $ for parameters. The number of particle is 10,000 at time t. Thus, we generate 270,000 random variables at time t. In Eq. (22), we set C to zero.

## References

Adam, K. and R. M. Billi, “Optimal monetary policy under commitment with a zero bound on nominal interest rates,” Journal of Money, Credit and Banking, 2006, 38 (7), 1877–1906.
Alspach, D. L. and H. W. Sorenson, “Nonlinear Bayesian estimation using Gaussian sum approximation,” IEEE Transactions on Automatic Control, 1972, 17, 439–448.

Amisano, G. and O. Tristani, “Euro Area Inflation Persistence in an Estimated Nonlinear DSGE Model,” 2007. ECB Working Paper No. 754.

An, S. and F. Schorfheide, “Bayesian analysis of DSGE models,” Econometric Reviews, 2007, 26(2-4), 113–172.

Baba, Naohiko, Shinichi Nishioka, Nobuyuki Oda, Masaaki Shirakawa, Kazuo Ueda, and Hiroshi Ugai, “Japan’s Deflation, Problems in the Financial System, and Monetary Policy,” Monetary and Economic Studies, 2005, 23 (1), 47–111.

Bakhshi, Hasan, Hashmat Khan, and Barbara Rudolf, “The Phillips curve under state-dependent pricing,” Journal of Monetary Economics, November 2007, 54 (8), 2321–2345.

Baxter, M. and R. G. King, “Measuring Business Cycles: Approximate Band-Pass Filters For Economic Time Series,” The Review of Economics and Statistics, November 1999, 81 (4), 575–593.

Bjornland, H. C., K. Leitemo, and J. Maih, “Estimating the natural rates in a simple New Keynesian framework,” January 2008, (2007/10).

Braun, R. A. and E. Shioji, “Monetary policy and economic activity in Japan, Korea, and the United States (Regime shifts in macroeconomy and monetary policy),” Seoul Journal of Economics, 2006, 19(1), 111–146.

Braun, R. A. and Y. Waki, “Monetary Policy During Japan’s Lost Decade,” Japanese Economic Review, 2006, 57 (2), 324–344.

Caballero, Ricardo J., Takeo Hoshi, and Anil K. Kashyap, “Zombie Lending and Depressed Restructuring in Japan,” NBER Working Papers 12129, National Bureau of Economic Research, Inc April 2006.

Calvo, G. M., “Staggered prices in a utility-maximizing framework,” Journal of Monetary Economics, 1983, 12 (3), 383–398.

Canova, F., Methods for Applied Macroeconomic Research, New Jersey: Princeton University Press, 2007.

and L. Gambetti, “Structural Changes in the U.S. Economy: Bad Luck or Bad Policy?,” CEPR Discussion Paper, 2006. No. 5457.

Christiano, L. J., M. Eichenbaum, and C. Evans, “Nominal rigidities and the dynamic effects of a shock to monetary policy,” Journal of Political Economy, 2005, 113 (1), 1–45.

Christiano, Lawrence J. and Terry J. Fitzgerald, “The Band Pass Filter,” International Economic Review, 05 2003, 44 (2), 435–465.
Clarida, R., J. Gali, and M. Gertler, “The Science of Monetary Policy: A New Keynesian Perspective,” Journal of Economic Literature, 1999, 37 (4), 1661–1707.

– , – , and – , “Monetary Policy Rules And Macroeconomic Stability: Evidence And Some Theory,” The Quarterly Journal of Economics, 2000, 115 (1), 147–180.

Dejong, D. N. and C. Dave, Structural Macroeconometrics, New Jersey: Princeton University Press, 2007.

Del Negro, M., F. Schorfheide, F. Smets, and R. Wouters, “On the Fit of New Keynesian Models,” Journal of Business & Economic Statistics, 2007, 25 (21), 123–143.

Doan, T., R. B. Litterman, and C. A. Sims, “Forecasting and Conditional Projection Using Realistic Prior Distributions,” Econometric Reviews, 1984, 3 (1), 1–100.

Dotsey, Michael, Robert G. King, and Alexander L. Wolman, “State-Dependent Pricing And The General Equilibrium Dynamics Of Money And Output,” The Quarterly Journal of Economics, May 1999, 114 (2), 655–690.

Doucet, A., N. de Freitas, and N. Gordon, eds, Sequential Monte Carlo Methods in Practice, New York: Springer-Verlag, 2001.

Eggertsson, G. and M. Woodford, “The Zero Interest-Rate Bound and Optimal Monetary Policy,” Brookings Papers on Economic Activity, 2003, pp. 139–211.

Eggertsson, Gauti B., “The liquidity trap,” in Steven N. Durlauf and Lawrence E. Blume, eds., The New Palgrave Dictionary of Economics (Second Edition), Palgrave Macmillan, 2008.

Eggertsson, Gauti B. and Benjamin Pugsley, “The Mistake of 1937: A General Equilibrium Analysis,” Monetary and Economic Studies, December 2006, 24 (S1), 151–190.

Fernandez-Villaverde, J. and J. F. Rubio-Ramirez, “Estimating dynamic equilibrium economies: linear versus nonlinear likelihood,” Journal of Applied Econometrics, 2005, 20 (7), 891–910.

— and _, “Estimating Macroeconomic Models: A Likelihood Approach,” Review of Economic Studies, 2007. Forthcoming.

— and _, “How Structural Are Structural Parameters?,” NBER Macroeconomics Annual 2007, 2007.

Fout, H., “Can New Keynesian Models of the Business Cycle Explain U.S. Wage Behavior?,” Manuscript, 2005.

Fuchi, Hitoshi, Ichiro Muto, and Hiroshi Ugai, “A Historical Evaluation of Financial Accelerator Effects in Japan’s Economy,” Bank of Japan Working Paper Series No. 05-E-8 2005.

Fujiwara, Ippei, “Is there a direct effect of money?: Money’s role in an estimated monetary business cycle model of the Japanese economy,” Japan and the World Economy, August 2007, 19 (3), 329–337.
Gali, J., “New Perspectives on Monetary Policy, Inflation, and the Business Cycle,” NBER Working Papers 8767, National Bureau of Economic Research, Inc February 2002.

___, Monetary Policy, Inflation, and the Business Cycle: An Introduction to the New Keynesian Framework, New Jersey: Princeton University Press, 2008.

Gertler, Mark and John Leahy, “A Phillips Curve with an Ss Foundation,” NBER Working Papers 11971, National Bureau of Economic Research, Inc January 2006.

Gordon, N., D. Salmond, and A. Smith, “Novel approach to nonlinear/non-Gaussian Bayesian state estimation,” IEEE Proceedings-F, 1993, 140, 107–113.

Hatano, Toshiya, “Zaisei Akaji no Makuro kouka: Kalman filter ni yoru Chuuritsu Meidai no Kenshou (in Japanese),” Financial Review, 2004, 2004 (74), 65–91.

Hayashi, F., “No growth without structural reform (in Japanese),” in K. Iwata and T. Miyagawa, eds., Ushinawareta Jyuunennno Sinin Ha Nanika, Tokyo: Toyokeizaisinposha, 2003, pp. 39–61.

and E. C. Prescott, “The 1990s in Japan: A Lost Decade,” Review of Economic Dynamics, 2002, 5 (1), 206–235.

Henderson, Dale W. and Warwick J. McKibbin, “A comparison of some basic monetary policy regimes for open economies: implications of different degrees of instrument adjustment and wage persistence,” Carnegie-Rochester Conference Series on Public Policy, December 1993, 39, 221–317.

Hirose, Y. and S. Naganuma, “Structural Estimation of the Output Gap: A Bayesian DSGE Approach for the U.S. Economy,” 2007. Bank of Japan Working Paper Series No. 07-E-24.

Hodrick, R. J. and E. C. Prescott, “Postwar U.S. Business Cycles: An Empirical Investigation,” Journal of Money, Credit, and Banking, 1997, 29, 1–16.

Hosono, K. and M. Sakuragawa, “Soft Budget Problems in the Japanese Banking System,” Technical Report, mimeo 2004.

Hürseler, M. and H. R. Künsch, “Approximating and maximizing the likelihood for a general state-space model,” in A. Doucet, N. de Freitas, and Gordon N., eds., Sequential Monte Carlo Methods in Practice, New York: Springer-Verlag, 2001, pp. 159–175.

Ichiue, Hibiki, Takushi Kurozumi, and Takeki Sunakawa, “Inflation Dynamics and Labor Adjustments in Japan: A Bayesian DSGE Approach,” 2008. Bank of Japan Working Paper Series 08-E-9.

Iiboshi, H., S. Nishiyama, and T. Watanabe, “An Estimated Dynamic Stochastic General Equilibrium Model of the Japanese Economy: A Bayesian Analysis,” Mimeo, 2005.

Jazwinski, A. H., Stochastic Processes and Filtering Theory, New York: Academic Press, 1970.

Jung, Taehun, Yuki Teranishi, and Tsutomu Watanabe, “Optimal Monetary Policy at the Zero-Interest-Rate Bound,” Journal of Money, Credit and Banking, 2005, 37 (5), 813–836.
Justiniano, A. and G. E. Primiceri, “The Time-Varying Volatility of Macroeconomic Fluctuations,” American Economic Review, 2008, 98 (3), 604–641.

Kalman, R. E., “A new approach to linear filtering and prediction problems,” Transactions of the ASME–Journal of Basic Engineering (Series D), 1960, 82, 35–45.

Kato, Ryo, Lectures on Modern Macroeconomics, Tokyo: Toyokeizai, 2006. (in Japanese).

Kimball, M. S., “The Quantitative Analytics of the Basic Neomonetarist Model,” Journal of Money, Credit and Banking, 1995, 27 (4), 1241–77.

Kitagawa, Genshiro., “Non-Gaussian State Space Modeling of Nonstationary Time Series,” Journal of the American Statistical Association, 1987, 82, 1032–1041.

Kitagawa, Genshiro, “Monte Carlo filter and smoother for non-Gaussian nonlinear state space models,” Journal of Computational and Graphical Statistics, 1996, 5 (1), 1–25.

– , “A self-organizing state-space model,” Journal of the American Statistical Association, 1998, 93 (443), 1203–1215.

Krugman, P., “It’s Baaack: Japan’s Slump and the Return of the Liquidity Trap,” Brookings Papers on Economic Activity, 1998, 2, 137–187.

Kuttner, K. N. and A. S. Posen, “The Great Recession: Lessons for Macroeconomic Policy from Japan,” Brookings Papers on Economic Activity, 2001, 2001 (2), 93–160.

— and _, “Fiscal Policy Effectiveness in Japan,” Journal of the Japanese and International Economies, 2002, 16 (4), 536–558.

Laubach, T. and J. C. Williams, “Measuring the Natural Rate of Interest,” The Review of Economics and Statistics, November 2003, 85 (4), 1063–1070.

Levin, A. T., A. Onatski, J. C. Williams, and N. Williams, “Monetary policy under uncertainty in micro-founded macroeconometric models,” in M. Gertler and K. Rogoff, eds., NBER Macroeconomics Annual 2005, MIT Press, 2005, pp. 229–287.

Liu, J. and M. West, “Combined parameter and state estimation in simulation-based filtering,” in A. Doucet, N. de Freitas, and N. Gordon, eds., Sequential Monte Carlo Methods in Practice, New York: Springer-Verlag, 2001, pp. 197–223.

Lubik, Thomas A. and Frank Schorfheide, “Computing sunspot equilibria in linear rational expectations models,” Journal of Economic Dynamics and Control, November 2003, 28 (2), 273–285.

McCallum, B. T. and E. Nelson, “An Optimizing IS-LM Specification for Monetary Policy and Business Cycle Analysis,” Journal of Money, Credit and Banking, 1999, 31 (3), 296–316.

McCallum, Bennett T, “Theoretical Analysis Regarding a Zero Lower Bound on Nominal Interest Rates,” Journal of Money, Credit and Banking, November 2000, 32 (4), 870–904.
Miyao, R., Makuro kinyuseisaku no jikeiretu bunnseki: Seisaku kouka no riron to jissho (in Japanese), Tokyo: Nihon Keizai Shinbun, 2006.

Nakajima, Tomoyuki, “Liquidity trap and optimal monetary policy in open economies,” Journal of the Japanese and International Economies, March 2008, 22 (1), 1–33.

Primiceri, G., “Time Varying Structural Vector Autoregressions and Monetary Policy,” Review of Economic Studies, 2005, 72 (3), 821–852.

Ristic, B., S. Arulampalam, and N. Gordon, Beyond the Kalman Filter: Particle Filters for Tracking Applications, Artech House Publishers, 2004.

Roberts, J. M., “New Keynesian Economics and the Phillips Curve,” Journal of Money, Credit and Banking, November 1995, 27 (4), 975–84.

Sekine, Toshitaka, Keiichiro Kobayashi, and Yumi Saita, “Forbearance Lending: The Case of Japanese Firms,” Monetary and Economic Studies, August 2003, 21 (2), 69–92.

Sims, C. A., “Solving Linear Rational Expectations Models,” Computational Economics, 2002, 20 (1-2), 1–20.

Smets, F. and R. Wouters, “An Estimated Stochastic Dynamic General Equilibrium Model of the Euro Area,” Journal of European Economic Association, 2003, 1 (5), 1123–1175.

Smets, Frank and Rafael Wouters, “Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach,” American Economic Review, June 2007, 97 (3), 586–606.

Sugo, T. and K. Ueda, “Estimating a Dynamic Stochastic General Equilibrium Model for Japan,” Journal of the Japanese and International Economics, 2008. forthcoming.

Svensson, L. E. O., “The Zero Bound in an Open Economy: A Foolproof Way of Escaping from a Liquidity Trap,” Monetary and Economic Studies, February 2001, 19 (S1), 277–312.

Taylor, J. B., “Discretion vs. Policy Rules in Practice,” Carnegie-Rochester Conference Series on Public Policy, 1993, 39, 195–214.

Trabandt, M., “Sticky Information vs. Sticky Prices: A Horse Race in a DSGE Framework,” Manuscript, 2006. Social Science Research Network Working Paper, No. 455140.

Trehan, Bharat and Tao Wu, “Time-varying equilibrium real rates and monetary policy analysis,” Journal of Economic Dynamics and Control, May 2007, 31 (5), 1584–1609.

Ugai, Hiroshi, “Effects of the Quantitative Easing Policy: A Survey of Empirical Analyses,” Monetary and Economic Studies, 2007, 25 (1), 1–48.

Urasawa, S., “Business Cycle Fluctuations in Japanese Macroeconomic Time Series,” Journal of the Asia Pacific Economy, 2008. forthcoming.

Walsh, C., Monetary Theory and Policy, Cambridge: MIT Press, 2003.
West, M., P. J. Harrison, and H. S. Migon, “Dynamic generalized linear models and Bayesian forecasting (with discussion),” Journal of the American Statistical Association, 1985, 80, 73–97.

Woodford, M., Interest and Prices: Foundations of a Theory of Monetary Policy, Princeton: Princeton University Press, 2003.

Yano, K., “A Self-organizing state space model and simplex initial distribution search,” Computational Statistics, 2008, 23, 197–216.

___, “Time-varying structural vector autoregressions based on self-organizing state space modeling: theory and application,” Manuscript, 2008.

___, “Time-varying analysis of dynamic stochastic general equilibrium models based on sequential Monte Carlo methods,” Manuscript, 2009.

— and N. Yoshino, “Japanese Monetary Policy Reaction Function and Time-Varying Structural Vector Autoregressions: A Monte Carlo Particle Filtering Approach,” FSA Research Review, 2007, 2007, 103–140.

Yun, T., “Nominal price rigidity, money supply endogeneity, and business cycles,” Journal of Monetary Economics, 1996, 37 (2-3), 345–370.
## List of Figures

1 Time-varying trends and targets 26
2 Endogenous variables 27
3 Time-varying parameters 28
4 Time-varying parameters of the Taylor rule 29
5 Stochastic Volatilities in the System Equation 30
6 Stochastic Volatilities in the Measurement Equation 31
7 Impulse response function: monetary shock (1985) 32
8 Impulse response function: monetary shock (1990) 33
9 Impulse response function: monetary shock (1995) 34
10 Impulse response function: monetary shock (2007) 35
11 Impulse response function: technology shock (1985) 36
12 Impulse response function: technology shock (1990) 37
13 Impulse response function: technology shock (1995) 38
14 Impulse response function: technology shock (2000) 39
15 Impulse response function: technology shock (2005) 40
16 Impulse response function: demand shock (1990) 41
17 Impulse response function: demand shock (2005) 42
18 Impulse response function: supply shock (1990) 43
19 Impulse response function: supply shock (2005) 44
20 Output gap: Comparing filtering methods 45
Figure 1: Time-varying trends and targets
Figure 2: Endogenous variables
Figure 3: Time-varying parameters
Figure 4: Time-varying parameters of the Taylor rule
Figure 5: Stochastic Volatilities in the System Equation
Figure 6: Stochastic Volatilities in the Measurement Equation
Figure 7: Impulse response function: monetary shock (1985)
Figure 8: Impulse response function: monetary shock (1990)
Figure 9: Impulse response function: monetary shock (1995)
Figure 10: Impulse response function: monetary shock (2007)
Figure 11: Impulse response function: technology shock (1985)
Figure 12: Impulse response function: technology shock (1990)
Figure 13: Impulse response function: technology shock (1995)
Figure 14: Impulse response function: technology shock (2000)
Figure 15: Impulse response function: technology shock (2005)
Figure 16: Impulse response function: demand shock (1990)
Figure 17: Impulse response function: demand shock (2005)
Figure 18: Impulse response function: supply shock (1990)
Figure 19: Impulse response function: supply shock (2005)
Figure 20: Output gap: Comparing filtering methods
## List of Tables

1 Log-likelihood of model ___ 47
Model	Log-likelihood	$ |\xi_{s}| $
Standard Model	-695.80	0.4837
Model without inflation indexation	-698.57	0.3476
Model without habit formation	-699.92	0.3885

Table 1: Log-likelihood of model
# 昭和55年度政府関係機関補正予算(機第1号)

(昭和55年度政府関係機関補正予算参照書添付)

第 94 回 国 会 ( 常 会 ) 提 出
## 総目録

## 昭和55年度政府関係機関補正予算(機第1号)

予算総則補正

甲号收入支出予算補正

(添付)

昭和55年度政府関係機関補正

予算(機第1号)参照書

昭和55年度政府関係機関収入支出予算補正予定計算書等
# 昭和55年度政府関係機関補正予算(機第1号)目録

予算総則補正

1

甲号收入支出予算補正 ___ 3

日本 専 売 公 社 3

日本国有鉄道 3

住宅金融公庫

4

環境衛生金融公庫4
(添付)

# 昭和55年度政府関係機関補正予算(機第1号)参照書目録

# 昭和55年度政府関係機関収入支出予算補正予定計算

日本 専 売 公 社
7
甲号収入支出予算補正予定計算書
7
日本 国有 鉄 道
23
甲号収入支出予算補正予定計算書
23
損 益 勘 定
23
資 本 勘 定
27
住 宅 金 融 公 庫
39
甲号収入支出予算補正予定計算書
39
環 境 衛 生 金 融 公 庫
53
甲号収入支出予算補正予定計算書
53
昭和55年度政府関係機関補正予算(機第1号)
# 昭和55年度政府関係機関補正予算

予算総則補正

第1条 次に掲げる各政府関係機関の昭和55年度収入支出予算補正は、「甲号収入支出予算補正」に掲げるとおりとする。

日 本 専 壳 公 社

日本国有鉄道

住宅金融公庫

環境衛生金融公庫

第2条 昭和55年度政府関係機関予算総則第4条の日本専売公社の借入金の限度額の規定中「長期借入金の限度額は73,000,000千円、」を削り、短期借入金の限度額「810,000,000千円」を「515,000,000千円」に改める。

第3条 昭和55年度政府関係機関予算総則第8条第1項に定める日本専売公社がその職員に対して支給する基準内給与の額「93,907,074千円」を「94,701,554千円」に、基準外給与の額「58,595,417千円」を「59,301,605千円」に、給与の総額「152,502,491千円」を「154,003,159千円」に改める。

第4条 昭和55年度政府関係機関予算総則第12条第1項の日本国有鉄道の借入金等の限度額の表中

長期借入金及び鉄道債券

政府からの長期借入金、政府引受債及び政府保証債

ロイ以外のもの

を

長期借入金及び鉄道債券

政府からの長期借入金、政府引受債及び政府保証債

ロイ以外のもの

に改める。

1,251,000,000千円

691,500,000

1,367,000,000 千円

724,500,000
第5条 昭和55年度政府関係機関予算総則第16条第1項に定める日本国有鉄道がその職員に対して支給する基準内給与の額「1,047,360,040千円」を「1,066,295,783千円」に、基準外給与の額「584,184,063千円」を「595,594,266千円」に、給与の総額「1,631,544,103千円」を「1,661,890,049千円」に改める。
第6条 昭和55年度政府関係機関予算総則第29条第1項の各公庫の借入金等の限度額の表中
住 宅 金 融 公 庫	政府からの借入金	3,161,300,000千円
住宅金融公庫財形住宅債券
イ政府引受債	900,000
ロイ以外のもの	49,221,000
住宅金融公庫宅地債券	2,000,000
を
住 宅 金 融 公 庫	政府からの借入金	3,101,700,000千円
住宅金融公庫財形住宅債券
イ政府引受債	900,000
ロイ以外のもの	40,783,000
住宅金融公庫宅地債券	2,000,000
に、
環境衛生金融公庫	借入金	276,700,000千円
を
環境衛生金融公庫	借入金	260,700,000千円
に改める。
甲号 收入支出予算補正
政府関係機関	項	補 正 額
追 加 額(千円)	修正減少額(千円)	差 引 額(千円)
日本専売公社収入	たばこ事業収入	34,372,969	△	272
塩事業収入	841,406	0
収入補正額	35,214,375	△	272
支 出	給与其他諸費	2,020,969	0
たばこ事業費	33,460,053	△	27,966,686
塩事業費	9,112,111	△	219,777
共通費	76,804	0
支払利子	1,000,000	0
予備費	0	△	3,500,000
支出補正額	45,669,937	△	31,686,463
日本国有鉄道損益勘定収入	運輸収入	0	△	53,841,000
資本勘定より受入	149,000,000	0
収入補正額	149,000,000	△	53,841,000
支 出	給与其他諸費	37,809,568	0
営業費	83,909,532	△	3,469,425
保 守 費	2,645,165	△	21,832,142
4 甲号 收入支出予算補正

政府關係機關	項目	補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 補 賬 1,127,6301,127,6301,349,147