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University of Birmingham, England and IMF Institute, respectively. We are grateful for helpful comments from Temisan Agbeyegbe, Marco Barassi, Paul Cashin, Eric Clifton, Andrew Feltenstein, John Fender, Martin Kaufman, Christopher Rowe, Sunil Sharma, and Robert Taylor, none of whom are responsible for remaining errors.
Systematic interventions can be deduced by traders in bond and currency markets, and affect expectations and interest rate differentials as well as exchange rates.
Calvo and others (1995) state that the real exchange rate is probably the most popular real target in developing countries, a common rationale being to avoid loss of competitiveness.
In a recent study, Wickham (2002) finds that daily nominal exchange returns for some developed and developing countries may be classified as white noise but not independent and identically distributed (iid) processes. While the analysis does not detect intervention in the data, he cautions that the results do not imply the absence of foreign market intervention or no use of monetary policy instruments to influence the exchange rate.
Evidence in favor of PPP has also been found using non-stationary panel techniques, which increase the span of the data while minimizing the effects of potential structural breaks (Frankel and Rose (1996), O’Connell (1997), and Papell (1998)). However, these methods are also subject to the “near-unit-root bias,” which favors finding PPP. For a confidence- interval-based method to overcome the power issue, see Cashin and McDermott (2001).
Cheung and Lai (1993) also find stationarity but using fractional integration models.
Calvo and others (1995) argue that real exchange rate targeting leads to some combination of higher inflation and higher domestic real exchange rates, while Goldfajn and Valdés (1996) find that appreciations are more likely to be undone by changes in the nominal exchange exchange rates as opposed to changes in inflation differentials.
All parameters of interest are actually identifiable in the general framework (there are seven reduced form parameters) but not in the linear approximation here which has five reduced form parameters.
In the literature on interventions (sterilized and non-sterilized), the exchange rate is affected through a portfolio balance effect, noise trading due to asymmetric information, and a signaling effect about the stance of monetary policy.
The countries are: United States, United Kingdom, Germany, Japan, France, Canada, Italy, Australia, Belgium, New Zealand, Spain, Israel, South Africa, Korea, Thailand, Malaysia, Indonesia, India, Philippines, Argentina, Brazil, Chile, Colombia, Costa Rica, Mexico, Paraguay, and Uruguay.
For a detailed methodology, see Zanello and Desruelle (1997); also, see Lafrance and St. Amant (1999).
One way of doing this is to model the intervention bands as regimes in a Markov-switching model or in a smooth transition regression (see Hamilton (1989), Tong (1990), and Granger and Teräsvirta (1993)).
We can think of this process as generating close approximations for estimates of a nonlinear model, whose likelihood function has been partitioned into a function of the values of the other set (in our case, the intervention bandwidth).
We estimate our regressions on a sample of 240 observations, generated from a random walk with starting value of ln(100) and iid errors (5000 iterations). The simulated distributions for each t-statistic are evaluated on a partial grid ranging from –3 to 0. Joint F-tests are also tabulated.
Our regimes are classified as discrete shifts – policy intervention in monthly data is probably less affected by time aggregation and nonsynchronous adjustment by agents, factors which favor smooth rather than discrete adjustment (Teräsvirta (1994)).
The maximum value of 1.00 was based on the number of observations in progressively larger bands across all countries in the sample.
The lag q is the largest integer not exceeding
We do not include a trend variable; it is not consistent with long-run PPP and, with a few exceptions, is not supported by the initial unit root tests. When included, the rationale is to control for the Balassa-Samuelson effect (see Cashin and McDermott, 2001).
Using the daily overnight Eurocurrency rate, Baillie and Osterberg (2000) find limited evidence of a significant impact of intervention on the conditional mean of deviations from uncovered interest rate parity (UIP). However, Eichenbaum and Evans (1995) show monetary policy leads to persistent departures from UIP.
The half-live is the length of time it takes for a unit impulse to dissipate by half. It is calculated using HL = abs (log(0.5)/log(β)), where β is the autoregressive parameter (Cashin and McDermott, 2001). For a half-life larger than three years, the point estimates of β need to be less than 0.02 in absolute value.
Estimates of β3 are generally stable for different lag lengths but β3 appears concave with respect to κ (bandwidth), tending to decrease (not reject unit root) as κ increases.
For example, the contention that the U.S. targets the REER may seem unwarranted (see Eichenbaum and Evans (1995) who find that in the US monetary policy leads to persistent changes in exchange rates, nominal and real). For a preliminary comparison, we ran our regressions using the nominal effective exchange rate as the dependent variable and found the coefficients β4 and β5 even more statistically significant.
Obstfeld and Rogoff (2000) argue that transaction costs may account for most current puzzles in international macroeconomics.