Silviase/jgov_v1.4_full_ann huggingface dataset:
variance. This standardization is somewhat restrictive because $ F(\cdot) $ could include the location parameter. However, the standardization allows us to estimate (11) as a linear function if $ \delta $ is fixed in (12). While Auerbach and Gorodnichenko (2013) parameterize $ \delta $ at a single value in their analysis, we allow for more flexible parameterizations of $ \delta $ by a grid search. In other words, we parameterize $ \delta $ over a range of $ \delta \in (0, \Delta) $ and run linear regressions for each grid constructed from an interval of $ (0, \Delta) $ . We then search for the best $ \delta $ that minimizes the sum of squared residuals (SSR). We repeat this procedure for delay parameter d for the range of $ d \in \{0, 1, \ldots, 12\} $ to choose a pair of $ (\delta, d) $ that minimizes the SSR. $ ^{26} $

### 4.2 Results from the STAR model

Table 5 reports the results of the grid search of $ (\delta,d) $ as a preparatory analysis. In the table, we do not report our choice of d, because d=1 minimizes the SSR for all k. On the other hand, the chosen $ \delta $ varies between 0.43 and 9.10, depending on $ k^{.27} $

Figure 4 plots the IRs under the high- and low-inflation regimes. As in the previous section, we report the IRs for (a) DCs with FER, (b) LDCs with FER, (c) DCs with USD, and (d) LDCs with USD.

Panels (a) and (b) of Figure 4 show that the effect of commodity price shocks is transitory even if we allow for the possibility that IRs depend on the past inflation rate. In both panels, the slopes of the IR function are modestly steep for the first year and become nearly flat afterward. The IRs are similar between the two regimes and not substantially different from the case of the linear model in Figure 3. Hence, the risks of a second-round effect would be low even under the two regimes.

By contrast, when we remove the effect of exchange rate variations on the price responses, the IRs differ substantially from the linear model, as shown in panels (c) and (d) of Figure 4. In response to a commodity price shock, the CPIs under the high-inflation regime increase sharply until period 8 and decline somewhat afterward. The CPIs under the low-inflation regime slowly increase and continue to increase even in the second year. The IRs for the second year imply that
the effect of commodity price shocks on inflation may not be transitory.

Table 6 computes the four country groups' VIRs under the linear model (panel (a)) and the two inflation regimes (panels (b) and (c)). Comparisons across the three panels suggest that the effects of commodity price shock remain transitory in the country groups with FER but may be persistent in the country groups with the currencies pegged to the U.S. dollar. For DCs with FER, $ VIR_{2} $ under the two regime is similar in magnitude to $ VIR_{2} $ in the linear model. This is also the case for LDCs with FER, although $ VIR_{2} $ under the high-inflation regime is slightly larger than $ VIR_{2} $ in the linear model. Turning to country groups with currencies pegged to the U.S. dollar, $ VIR_{1} $ and $ VIR_{2} $ contrasts sharply with their linear model. $ VIR_{2} $ in these country groups is higher than the linear model confirming the shapes of the IRs presented in Figure 4.

We can test whether the price responses in a particular forecast horizon are statistically the same between the high- and low-inflation regimes. If we take an example of DCs with FER, the null hypothesis is that $ \gamma_{k}^{H} + \omega_{IT}\gamma_{IT,k}^{H} + \omega_{TO}\gamma_{TO,k}^{H} = \gamma_{k}^{L} + \omega_{IT}\gamma_{IT,k}^{L} + \omega_{TO}\gamma_{TO,k}^{L} $ . Assuming that $ \omega_{IT} $ and $ \omega_{TO} $ are fixed, we test the null hypothesis using the Wald test. Similarly, if we test whether the price responses in LDCs with USD are the same across the two regimes, the null hypothesis is defined as $ H_{0} $ : $ \gamma_{k}^{H} + \gamma_{USD,k}^{H} + \gamma_{LDC,k}^{H} + \omega_{IT}\gamma_{IT,k}^{H} + \omega_{TO}\gamma_{TO,k}^{H} = \gamma_{k}^{L} + \gamma_{USD,k}^{L} + \gamma_{LDC,k}^{L} + \omega_{IT}\gamma_{IT,k}^{L} + \omega_{TO}\gamma_{TO,k}^{L} $ . This hypothesis testing can be made under different values of k.

Not surprisingly, as indicated in Table 7, we cannot reject the null hypothesis for countries with FER at the conventional significance level for any $ k = 0, 1, \ldots, 24 $ . This suggests that the regime-dependent IRs of the CPIs may not be present in the DCs and LDCs with FER. For DCs with USD, the test statistics reject the null hypothesis in 11 forecast horizons at the 10 percent significance level (for k = 0 and 5 - 14). For LDCs with USD, we reject the null hypothesis in 13 forecast horizons at the 10 percent significance level (for k = 4 - 15). Hence, at least during the first year after the shock, the IRs in the country group of DCs and LDCs with USD are statistically different between the two regimes.

## 5 Conclusion

Using local projections, we estimated the IRs of the CPIs to a commodity price shock and explored the implications for inflation. We found that, in most countries, the effect of commodity price
shocks on inflation is transitory, suggesting the low risks of a second-round effect of commodity price shocks on inflation. This finding is robust even when we control for exchange rate variations, adoption of IT, the degree of economic development, and the degree of trade openness. The result is also robust even if we use other general commodity price indexes and change the method of identifying commodity price shocks. Based on the STAR model that uses the past inflation rate as the transition variable, we found that commodity price shocks may have non-transitory effects on inflation in countries with exchange rates pegged to the U.S. dollar. Nevertheless, commodity price shocks continue to have transitory effects in the countries with exchange rate flexibility.

Our study focused on the responses of the CPIs and inflation. For a deeper understanding of the impact of commodity price shocks on economies as a whole, an investigation of the actual policy responses of central banks and the effects on the real economy and longer-term inflation expectations would be worthwhile. It is also important to identify demand and supply shocks in the commodity markets.

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Table 2: Comparisons of estimation methods

Estimation methods	$ VIR_{1} $	$ VIR_{2} $
Local projections	0.531	0.134
Romer and Romer's method
$ q = 12, r = 6 $	0.477	0.040
$ q = 12, r = 12 $	0.555	0.049
$ q = 12, r = 18 $	0.632	0.136

Notes: The table presents $ VIR_{1} $ and $ VIR_{2} $ under local projections and the standard approach by Romer and Romer's method. $ VIR_{1} $ and $ VIR_{2} $ are defined by (5) and (6), respectively. Romer and Romer's method uses (7) and specifies the lag length for the commodity price shock $ (r) $ is set 6, 12, and 18.
Notes: Estimated from (8). The numbers in parentheses are heteroskedasticity- and autocorrelation-consistent standard errors. Each of the dummy variables is defined as a variable that takes one (a) if a country's currency is pegged to the U.S. dollar and zero otherwise; (b) if a country adopts inflation targeting in its monetary policy and zero otherwise; (c) if a country is less developed and zero otherwise; and (d) if the degree of trade openness is above the median and zero otherwise.

Table 3: Estimates of γUSD,k, γIT,k, γLDC,k, and γTO,k based on the local linear projection with dummy variables
Table 4: Variability of inflation responses: Differences across specifications

Commodity price indexes	$ VIR_{1} $	$ VIR_{2} $
(a) Alternative commodity price indexes
WB (non-energy)	0.490	0.131
WB (energy)	0.376	0.162
IMF (all)	0.422	0.130
IMF (non-fuel)	0.421	0.153
IMF (fuel)	0.372	0.160
(b) Alternative forecasting equations
Five ERs	0.467	0.116
S&P GSCI	0.448	0.170
Demand shock by Kilian (2009)	0.222	0.176

Notes: The table presents $ VIR_{1} $ and $ VIR_{2} $ for several specifications. $ VIR_{1} $ and $ VIR_{2} $ are defined by (5) and (6), respectively. Panel (a) reports $ VR_{Is} $ using alternative commodity price indexes, where WB and IMF denote the data source of commodity price indexes. WB (non-energy) and WB (energy) means that the $ VR_{Is} $ are calculated using the WB's non-energy commodity price index and energy commodity price index, respectively. IMF (all), IMF (non-fuel), and IMF (fuel) refer to the case where the All Commodity Price Index, Non-fuel Commodity Price Index, and Fuel Commodity Price Indexes are respectively used. In panel (b), "Five ERs" corresponds to the case where the nominal exchange rates of the Chilean peso and South African rand are newly included in (4) in estimating the commodity price shock. "S&P GSCI" refers to the case where commodity price shocks are estimated using (10). "Demand shock by Kilian (2009)" reports the $ VR_{Is} $ when we replace the estimated commodity price shock by the global demand shock estimated by Kilian (2009).
Table 5: Results of a grid search for the STAR models

k	q	$ \delta $
0	12	0.436
1	12	1.536
2	12	1.920
3	12	2.838
4	11	4.974
5	11	5.470
6	10	6.824
7	10	6.376
8	9	9.602
9	7	9.616
10	7	7.870
11	7	8.200
12	6	8.844
13	7	8.364
14	6	8.446
15	7	7.152
16	6	6.804
17	6	6.202
18	7	6.004
19	7	6.304
20	7	6.934
21	6	8.242
22	6	7.914
23	6	8.432
24	6	9.200

Notes: The parameter $ \delta $ in the transition function (12) is chosen from grid search over (0,10). The value of $ \delta $ minimizes the sum of squared residuals in (11) for each grid. We also choose the delay parameter d to minimize the sum of squared residuals, but d = 1 is chosen for all forecast horizons. The lag lengths of q in (12) are selected by the BIC for $ 6 \leq q \leq 12 $ to avoid the corner solution for $ \delta $ .
Table 6: Variability of inflation responses: Differences across country groups

Country group	$ VIR_{1} $	$ VIR_{2} $
(a) Linear models with dummies
DCs with FER	0.332	0.062
LDCs with FER	0.427	0.124
DCs with USD	0.614	0.138
LDCs with USD	0.644	0.198
(b) STAR models: High-inflation regime
DCs with FER	0.391	0.107
LDCs with FER	0.389	0.225
DCs with USD	1.105	0.200
LDCs with USD	0.914	0.285
(c) STAR models: Low-inflation regime
DCs with FER	0.269	0.105
LDCs with FER	0.536	0.129
DCs with USD	0.366	0.258
LDCs with USD	0.385	0.248

Notes: The commodity price index is the WB's non-energy commodity price index. $ VIR_{1} $ and $ VIR_{2} $ are defined by (5) and (6), respectively. DCs (LDCs) with FER stand for (less) developed countries with flexible exchange rates. DCs (LDCs) with USD refer to (less) developed countries with exchange rates pegged to the U.S. dollar.
Table 7: Tests for the equality of impulse responses between the high- and low-inflation regimes

k	DCs with FER	LDCs with FER	DCs with USD	LDCs with USD
0	0.743	0.117	0.029	0.448
1	0.869	0.074	0.259	0.561
2	0.412	0.108	0.436	0.293
3	0.772	0.410	0.525	0.057
4	0.435	0.106	0.116	0.003
5	0.159	0.052	0.039	0.001
6	0.149	0.070	0.020	0.000
7	0.315	0.274	0.005	0.000
8	0.401	0.620	0.001	0.000
9	0.550	0.694	0.005	0.001
10	0.929	0.730	0.019	0.003
11	0.918	0.908	0.027	0.006
12	0.797	0.924	0.071	0.020
13	0.858	0.953	0.071	0.029
14	0.803	0.834	0.094	0.056
15	0.441	0.658	0.147	0.072
16	0.418	0.556	0.217	0.161
17	0.502	0.606	0.326	0.270
18	0.575	0.861	0.398	0.221
19	0.384	0.764	0.598	0.329
20	0.323	0.886	0.873	0.441
21	0.317	0.983	0.985	0.451
22	0.346	0.993	0.983	0.533
23	0.348	0.912	0.923	0.537
24	0.344	0.911	0.879	0.584

Notes: The p-values of the Wald tests whose null hypothesis is that country group’s price response for forecast horizon k differs between the high- and low-inflation regimes. The test statistics are defined for each forecast horizon k. See the footnote of Table 6 for abbreviations in the table.
Figure 1: Commodity price inflation and the estimated commodity price shocks

(a)

(b)

Notes: Panel (a) shows commodity price inflation and its forecasts based on (4). Panel (b) plots the residuals in (4).
Figure 2: Impulse responses of the CPI: Benchmark regressions

(a)

(b)

Notes: The panels plot impulse responses of the CPI to a 10 percent increase in commodity price shocks. The impulse responses are estimated from (2). The unit of the responses is percent. Panel (a) shows the CPI responses estimated from the 144-country panel. Panel (b) shows those estimated from the longer sample period. The shaded areas represent the 95 percent confidence intervals.
Figure 3: Impulse responses of the CPI with interaction terms

(c) DCs with USD

(d) LDCs with USD

Notes: The panels plot impulse responses of the CPI to a 10 percent increase in commodity price shocks. The impulse responses are estimated from (8). The unit of the responses is percent. Panels (a) and (b) show the responses of the CPI in developed countries with flexible exchange rates (DCs with FER) and less developed countries with flexible exchange rates (LDCs with FER), respectively. Panels (c) and (d) correspond to developed countries with exchange rates pegged to the U.S. dollar (DCs with USD) and less developed countries with exchange rates pegged to the U.S. dollar (LDCs with USD), respectively. The shaded areas represent the 95 percent confidence intervals.
Figure 4: Impulse responses of the CPI with interaction terms: High- and low-inflation regimes

(a) DCs with FER

(b) LDCs with FER

(c) DCs with USD

(d) LDCs with USD

Notes: The panels plot impulse responses of the CPI to a 10 percent increase in commodity price shocks under the high- and low-inflation regimes. The impulse responses are estimated from (11). The unit of the responses is percent. Panels (a) and (b) show the responses of the CPI in developed countries with flexible exchange rates (DCs with FER) and less developed countries with flexible exchange rates (LDCs with FER), respectively. Panels (c) and (d) correspond to developed countries with exchange rates pegged to the U.S. dollar (DCs with USD) and less developed countries with exchange rates pegged to the U.S. dollar (LDCs with USD), respectively.
Chapter 6

Interdependent Development of Eastern Asian Economies

- Cross Country Comparison of Household Electric and Electronic Industry - 1\. Introduction

For more than 30 years, the East Asian countries (EA which includes Japan, the Asian NIEs and ASEAN) has been enjoying high economic growth compared with the other economies. Although the scale of the EA economies combined is still about 60% of the other two main areas, NAFTA (U.S., Canada and Mexico) and EC, the trade share of the EA in the world trade had increased from 13.4% in 1980 to 18.0% in 1990.

These results, together with dynamic movements in the trade and foreign direct investments in the region in the latter half of the 1980's may suggest a glorious future development in this region.

The purpose of this chapter is to analyze the interdependent economic growth of EA through trade and foreign direct investment by an application of the “Flying-Wild-Geese” economic development theory.

EA composes of many countries and areas, which differ in the stage of economic development with each other and are characterized by different endowments of factors such as land, capital, labour, etc. For the analytical purpose, we deal with the household electric and electronic industry (HEEI) of Japan, Korea and Thailand, in which rapid movements have been observed both in trade and investment. This industry also has interesting economic characteristics such as economies of scale, learning-by-doing processes discussed later in this chapter.

Section 2 deals with an overview of interdependent economic developments between Japan, the NIEs and ASEAN and implications of the “Flying-Wild-Geese” economic development to the interdependence of this area’s economies.

Section 3 focuses on some microeconomic fact findings of trade and foreign direct investment in HEEI. Trade statistics should ideally be examined in physical rather than value terms in order to avoid the influences of fluctuations in exchange rates. However, the commodity composition of HEEI is so much diverse that it is difficult to cover all commodities in one quantitative unit. We thus utilized value figures in the US dollar. After reviewing recent developments in trade and investments in this area, we point out characteristic aspects of these developments.

### 2. Interdependent Economic Development

Overview of interdependent economic development between Japan, the NIEs and ASEAN

We go back to the 1970's to reflect on the economic relations between Japan, the NIEs and ASEAN. In order to avoid increases in production costs brought about by wage hikes, Japanese manufacturing companies started to transfer their production bases of labor intensive industries to foreign countries with lower labor costs. On the other hand, the NIEs required capital and technology to promote their export oriented industrialization. The transfer of production bases of Japanese manufacturers to the NIEs (South Korea and Taiwan in particular) was accelerated to a considerable pace as the result of these push and
Table:1 Export Amount to Japan from Korea and Thailand by Manufactured Product

(Korea → Japan)

[Amount]

(US$1,000 \%)

Year	(1)Total export	(2)Manufactured Product	(3)Chemical Product	(4)Machine Product	(5)Others	(2) / (1)
1979	3,359,416	2,421,249	186,207	288,766	1,946,276	72.1
1980	2,996,272	2,220,843	236,378	359,768	1,624,697	74.1
1981	3,388,915	2,512,721	265,210	378,982	1,868,529	74.1
1982	3,253,813	2,402,470	231,814	303,111	1,867,545	73.8
1983	3,364,954	2,197,564	232,979	338,460	1,626,125	65.3
1984	4,212,740	2,772,321	255,784	487,660	2,028,877	65.8
1985	4,091,949	2,634,955	202,255	498,813	1,933,887	64.4
1986	5,291,970	3,674,411	323,655	664,737	2,686,019	69.4
1987	8,075,471	5,990,003	376,809	1,074,981	4,538,213	74.2
1988	11,810,619	9,340,395	490,525	1,775,251	7,074,619	79.1
1989	12,994,156	10,549,596	520,227	2,133,749	7,895,620	81.2
1990	11,706,656	9,437,065	570,056	2,103,021	6,763,988	80.6

(Thailand → Japan)

[Amount]

(US$1,000 %)

Year	(1)Total export	(2)Manufactured Product	(3)Chemical Product	(4)Machine Product	(5)Others	(2) / (1)
1979	1,169,396	271,181	27,918	5,003	238,260	23.2
1980	1,119,485	275,374	30,714	4,399	240,261	24.6
1981	1,061,093	233,839	36,776	4,469	192,594	22.0
1982	1,040,695	250,802	37,463	5,809	207,530	24.1
1983	1,018,657	219,595	22,211	14,527	182,857	21.6
1984	1,039,647	247,453	33,148	41,475	172,830	23.8
1985	1,026,908	284,917	37,460	60,866	186,591	27.7
1986	1,390,904	367,939	47,949	84,286	235,704	26.5
1987	1,795,996	528,620	49,024	116,276	363,320	29.4
1988	2,751,427	897,060	61,583	184,463	651,014	32.6
1989	3,582,607	1,503,799	91,695	396,414	1,015,690	42.0
1990	4,147,212	1,993,426	107,507	682,842	1,203,077	48.1

“Japanese import of manufactured product in 1991” JETRO & Overseas Economic Information Center