Defending Against Speculative Attacks

While virtually all currency crisismodels recognise that the fate of a currency peg depends on how tenaciously policy makers defend it, they seldom model how this is done. We incorporate themechanics of speculation and the interest rate defence against it in the model ofMorris and Shin (American Economic Review 88, 1998). Our model captures that the interest rate defence reduces speculators’ profits and thus postpones the crisis. It predicts that well before the fall of a currency interest rates are increased to offset the buildup of exchange market pressure, and this then unravels in a sharp depreciation. This pattern is at odds with predictions of standard models, but we show that it fits well with reality.Exchange Market Pressure, Currency Crisis, Interest Rate Defence, Global Game

Automatic classification of long-term ambulatory ECG records according to type of ischemic heart disease

<p>Abstract</p> <p>Background</p> <p>Elevated transient ischemic ST segment episodes in the ambulatory electrocardiographic (AECG) records appear generally in patients with transmural ischemia (e. g. Prinzmetal's angina) while depressed ischemic episodes appear in patients with subendocardial ischemia (e. g. unstable or stable angina). Huge amount of AECG data necessitates automatic methods for analysis. We present an algorithm which determines type of transient ischemic episodes in the leads of records (elevations/depressions) and classifies AECG records according to type of ischemic heart disease (<it>Prinzmetal's angina</it>; <it>coronary artery diseases excluding patients with Prinzmetal's angina</it>; <it>other heart diseases</it>).</p> <p>Methods</p> <p>The algorithm was developed using 24-hour AECG records of the Long Term ST Database (LTST DB). The algorithm robustly generates ST segment level function in each AECG lead of the records, and tracks time varying non-ischemic ST segment changes such as slow drifts and axis shifts to construct the ST segment reference function. The ST segment reference function is then subtracted from the ST segment level function to obtain the ST segment deviation function. Using the third statistical moment of the histogram of the ST segment deviation function, the algorithm determines deflections of leads according to type of ischemic episodes present (elevations, depressions), and then classifies records according to type of ischemic heart disease.</p> <p>Results</p> <p>Using 74 records of the LTST DB (containing elevated or depressed ischemic episodes, mixed ischemic episodes, or no episodes), the algorithm correctly determined deflections of the majority of the leads of the records and correctly classified majority of the records with Prinzmetal's angina into the <it>Prinzmetal's angina </it>category (7 out of 8); majority of the records with other coronary artery diseases into the <it>coronary artery diseases excluding patients with Prinzmetal's angina </it>category (47 out of 55); and correctly classified one out of 11 records with other heart diseases into the <it>other heart diseases </it>category.</p> <p>Conclusions</p> <p>The developed algorithm is suitable for processing long AECG data, efficient, and correctly classified the majority of records of the LTST DB according to type of transient ischemic heart disease.</p