News-driven international business cycles

Michiru Sakane Kosaka

doi:10.1515/bejm-2012-0115

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News-driven international business cycles

Michiru Sakane Kosaka

Published/Copyright: May 25, 2013

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From the journal The B.E. Journal of Macroeconomics Volume 13 Issue 1

Abstract

This paper examines the international transmission effects of news about the total factor productivity (TFP) in the US on the Canadian and Japanese economies. I develop and estimate a two-country real business cycle model to generate booms in Canadian and Japanese variables in response to news about future US TFP. I find that international macroeconomic comovements can be generated by news about the future TFP in the US. Unlike previous studies, I show that the response of the Canadian or Japanese TFP to a US news shock is important for generating the boom observed in the empirical analysis. The estimated preference parameters indicate that eliminating the wealth effect on hours worked is important. I also show that a low elasticity of substitution between domestically and foreign-produced intermediate goods can also help explain the domestic boom created by a news shock.

Keywords: international business cycles; news; total factor productivity

Corresponding author: Michiru Sakane Kosaka, Faculty of Liberal Arts, Sophia University, 7-1, Kioi-cho, Chiyoda-Ku, Tokyo, 102-8554 Japan, e-mail: michiru-s@sophia.ac.jp

¹
Other variables are transformed to per capita values in the same way.
²
Rhys Mendes (Bank of Canada) kindly provided me the dataset for the Canadian economy.
³
I calculate the measure of hours worked as follows. First, I multiply the Canadian population series by the participation rate series. I then multiply the resulting series by the employment rate calculated using the unemployment rate to get the employment series. Then I multiply this by the average hours worked series to obtain the total hours worked.
⁴
See Appendix A for the explanation of data in detail.
⁵
See page 143 of Lütkepohl (2005) for details.
⁶
See Hansen (2005) for an explanation of the derivation of the Wold representation in the case of the VECM.
⁷
The use of SP is motivated also by the dynamic correlation between TFP and SP as shown in Figure 5. This result shows that SP leads TFP, which motivates us to use SP as a signal of future TFP.
⁸
If I set κ=0, these preferences become inconsistent with steady-state growth. Therefore, when I solve the model for the case of GHH preferences, I use κ=0.001, which is a small value.
⁹
This value is taken from Raffo (2006).
¹⁰
This value is calculated using the data on the share of real imports in real GDP.
¹¹
For the two-country model of the US and Japan, it is difficult to generate a positive investment response with ϕ=5.
¹²
I compared the RIRSC from 4 to 20 lags.
¹³
Rhys Mendes (Bank of Canada) kindly provided the dataset.

Appendix

Data sources

A.1 US data

For the US working age population, I use the data from the US Government Printing Office. The original data are taken from the Department of Commerce (Bureau of Census).
Real GDP is obtained from the Bureau of Economic Analysis (hereinafter BEA) NIPA Table (Series ID: GDPC1).
Real personal consumption expenditure is obtained from the St. Louis Fed FRED database (Series ID: PCECC96).
Real fixed private investment is obtained from the St. Louis Fed FRED database (Series ID: FPIC1).
Real capital services index is obtained from the Bureau of Labor Statistics (hereinafter BLS).
Total non-farm employment is obtained from BLS (Series ID: CES0000000001).
For nominal stock price, I use the Standard & Poor’s 500 composite stock prices index downloaded from the Global Financial Database.
Deflator (price index of business sector) is obtained from BEA (from Price Indexes for Gross Value Added by Sector).
Hours index of the non-farm business sector is obtained from BLS (Series ID: PRS85006033).
The series of real exports of goods and services is obtained from the St. Louis Fed FRED database.
The series of real imports of goods and services is obtained from the St. Louis Fed FRED database.

A.2 Canadian data

For the Canadian population (15 years old and over), I use the data from the CANSIM database, Statistics Canada (Series ID: V2091030).
Real GDP is obtained from the CANSIM database (Series ID: V1992067).
Real consumption is obtained from the CANSIM database (Series ID: V1992044).
Real investment in non-residential structures and equipment is obtained from Statistics Canada.
To calculate hours worked, using the population data above, I multiplied the series by the participation rate obtained from the Bank of Canada and then multiplied the result by the employment rate, which I calculated using data of the unemployment rate, to get the employment data. Then I multiplied by the series of average hours worked to obtain total hours worked.¹³
Real exports is obtained from the CANSIM database (Series ID: V1992060).
Real imports is obtained from the CANSIM database (Series ID: V1992063).
Canadian terms of trade is calculated as the import deflator divided by the export deflator obtained from the sourceOECD database.

A.3 Japanese data

For the Japanese working population (15 years old and over), I obtain the series from the Statistics Bureau, Ministry of Internal Affairs and Communications.
For the following variables, before 1994Q1, I use the series from 68SNA. In order to connect the series from 68SNA and 93SNA, I obtain the growth rates of the series from 93SNA and calculate the series from 1994Q1 using these. Before 1994Q1, I use the series from 68SNA.
- Real GDP is obtained from SNA Statistics, Cabinet Office.
- Real private consumption is obtained from SNA Statistics, Cabinet Office.
- Real private non-residential investment is obtained from SNA Statistics, Cabinet Office.
- Real net exports is obtained from SNA Statistics, Cabinet Office.
- Real exports is obtained from SNA Statistics, Cabinet Office.
- Real imports is obtained from SNA Statistics, Cabinet Office.
Real capital stock is obtained from SNA Statistics, Cabinet Office. I obtain the series called “Gross capital stock series by industry, including the construction in progress.” In order to connect the series from 68SNA and 93SNA, I obtain the growth rates of the series from 93SNA and calculate the series from 1980Q2 using these. Before 1980Q2, I use the series from 68SNA.
Hours is the aggregate weekly hours worked obtained and calculated using the data from Roudouryoku Chosa (Annual Report on the Labour Force Survey), Statistics Bureau, and the data series in accompanying data set of Braun et al. (2006).
Japanese terms of trade is calculated as the import deflator divided by the export deflator obtained from the sourceOECD database.

B Short-run and long-run identification

B.1 Short-run identification

In this section, I explain the short-run identification. In (3), I impose an impact restriction on the impact matrix, Γ₀. The restriction is

Γ₀(1, 2)=0.

In practice, I find Γ₀ by implementing Cholesky decomposition of the variance-covariance matrix of the reduced residuals,

B.2 Long-run identification

B.1.2 Identification of the permanent shock

Here, I follow King et al. (1991), which generalizes the long-run identification method introduced by Blanchard and Quah (1989). This method allows both variables, i.e., TFP^US and SP, to be affected by a productivity shock in the long-run. In other words, this method allows two variables to be cointegrated. Suppose that the reduced-form representation of the model is

where and u_t=[u_1tu_2t]′. The structural-form representation is

In the long-run identification a lá King et al. (1991), there are two identifying restrictions:

and

where A is the matrix of long-run multipliers. A is decomposed into two parts, so that Assuming that is a 2×1 matrix of full rank, I obtain the multipliers on the permanent shock, using the procedure as follows.

From (33), (34), and (35), I obtain

Suppose that D satisfies and can be obtained from Therefore, using (35), (36), and (37),

Assuming that has unit variance, I obtain

where Σ_u is the variance of the reduced residuals. Π is a lower triangular square root of (38). Therefore, I conduct Cholesky decomposition of DΣ_uD′ to obtain Π.

Assuming that where H and J are 2×1 matrices, the first column of the structural dynamic multipliers, can be written as C(L)H. From (35) and the assumption of unit variance for the structural errors,

Since the first row of is G=Π^–¹D, we obtain

H′=GΣ_u.

Therefore, the dynamic multipliers of the permanent shock, is given by C(L)Σ_uG′.

B.2.2 Identification of the transitory shock

In this section, I explain the identification of the transitory shock using the long-run identification following Fisher, Fackler, and Orden (1995). The first row of is G, which is calculated in Section B.2.1. In order to identify the transitory shock, the second row of M, needs to be known. In order to derive M, I solve the following equation:

[0 1]=[MΣ_uG′ MΣ_uM′].

This paper is based on a chapter of my dissertation at Duke University. I would like to thank my dissertation supervisor, Craig Burnside for his guidance and many discussions. I would also like to thank Pietro Peretto, Barbara Rossi and Juan Rubio-Ramirez for useful comments and suggestions. I am also grateful to Francesco Bianchi, Jeremy Chiu, Ippei Fujiwara, Charles Yuji Horioka, Cosmin Ilut, Nobuhiro Kiyotaki, Jouchi Nakajima, Masao Ogaki, Eiji Ogawa, Keisuke Otsu, Roberto Pancrazi, Etsuro Shioji, Alexandra Tabova, Marija Vukotic, Tsutomu Watanabe as well as seminar participants at Duke University, Hitotsubashi University, the 11th Macro Conference at Osaka University and Japanese Economic Association Spring Meeting in 2010 for helpful comments.

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Published Online: 2013-05-25

Published in Print: 2013-01-01

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https://doi.org/10.1515/bejm-2012-0115

Keywords for this article

international business cycles; news; total factor productivity