These graphs show –for a naked eye- a similar cycle over time, besides differences on timing and amplitude, there is a similar number of boom and boost processes. A first question to analyze is the existence of cointegration between those series, and if the answer is affirmative to evaluate the dependency of some from one.

Cointegration concept comes from econometrics and it is related to time series which evolve over time in a similar pattern, cointegration does not imply causality between the variables analyzed, it implies a similar concept of parallel lines in geometry.

On the other side dependency theory in economics, implies the existence of a center and a periphery, equivalent to solar system in astronomy, the planets does not have exact orbits, but they are not comets drifting on the outer space. The dependency concept tells of an economy that is the center for periphery economies, which essentially are tied up to the evolution of the center.

This note founds that Chile and Peru manufacturing sector are periphery economic activities to the USA industrial sector on the long run, obviously in the short run a periphery sector may drift away, but sooner or later return to its orbit. A simulation is run from August 2006 up to December 2008, when Peru has a new presidential tenure in July 28^{th} 2006, which shows a Peru industrial sector drifting-up, and then a forecast is made to evaluate how much economic contraction will face Peru to be back on track.

The monthly series are taken from International Monetary Fund’s International Financial Statistics Compact Disk disseminated March 2009. Only series from Chile and Peru are chosen from South America due lack of availability for other countries, only USA series will be taken from 24 advanced economies (IMF lingo), as center country. The series start in January 1979 because Peru is the constraint, until December 2008 because is most recent available data from USA. The codes are […66EY.ZF…] for Chile and Peru, and […66…ZF…] for USA; which is available **here**.

A first step is taking logarithm of each series, then to evaluate the existence of unit root for each one, which is accepted. A cointegration test between Chile, Peru and USA is rejected with zero lags, but a loop from 1 to 60 lags is run to find 12 lags (a calendar year) as appropriate to accept cointegration between the analyzed variables. The evaluation is constraint until July 2006, since Peru has a new presidential tenure since August 2006.

Two approaches were tried to model the dependency of Peru and Chile to USA. The unsuccessful one was an Error Correction Model (ECM) which shows a strong equilibrium relationship between Peru and Chile, but the equilibrium error component was nil for Chile equation and strong for Peru; but in any circumstance was possible to reject a null hypothesis for USA inclusion in the equilibrium relationship or in the equilibrium error.

The second and successful approach was a Vector Autoregressive (VAR) model with USA as exogenous component. The lag period was 12, since this length was found as adequate on the cointegration analysis described above. The computer code in RATS is **here**, and the output results are **here**.

The first null hypothesis tested for no relation between Chile and Peru is rejected for each equation and therefore the naked eye observation was correct. A second set of null hypothesis for each USA coefficient being zero in both equations is carried out, to find them rejected and to conclude that USA industrial sector has influence on Chile and Peru manufacturing sector, in the impact multipliers. A third set of null hypothesis for equilibrium multipliers of USA being zero is carried out, to find them accepted; this unexpected outcome is interpreted as neutrality on the long run of the level of USA industrial activity for Chile and Peru manufacturing sectors, only if USA variable become constant which never have been observed. A fourth set of null hypothesis for equilibrium unitary elasticity between Chile and Peru is carried out, to find them rejected; this outcome allows concluding that USA individual shocks in Chile and Peru become permanent for the level of manufacturing activity.

Those four sets of null hypothesis are re-run with a Seemly Unrelated Regression (SUR) model with similar specification in order to evaluate cross equation restrictions. The previous four null hypotheses are evaluated and similar conclusions are obtained. The fifth null hypothesis of USA impact coefficients with similar values vis-à-vis for Chile and Peru is carried out, to find them rejected; this outcome allows concluding differentiated short run impact between Chile and Peru, nevertheless for both countries the equilibrium multiplier for USA is zero. Finally, a sixth null hypothesis for null intercept in Chile and Peru is carried out and accepted; this outcome allows to conclude that long run average level of manufacturing in Chile and Peru is the long run multiplier of Peru and Chile –respectively- multiply by the average level of industrial activity in USA.

The third and sixth null hypotheses are imposed in a new estimation on the SUR model in order to produce a forecast outside the boundaries of the estimation, which are January 1979 and July 2006. The forecast values are plotted in graphs for Chile and Peru showing an interesting feature which differentiates Chile and Peru; while Chile forecast values are close to observed values, Peru forecast values are systematically below observed values.

**A first econometric conclusion is the existence of cointegration between Chile, Peru, and USA. A second econometric conclusion is the dependency of Chile and Peru with USA which is coherent with the economic dependency theory. A third conclusion is Chile manufacturing sector is on track, and its contraction will mirror USA industrial contraction. A fourth conclusion is Peru manufacturing sector is off track, and its contraction will be more than proportional to USA industrial contraction.**