On the Existence and Uniqueness of the Maximum Likelihood Estimators of Normal and Lognormal Population Parameters with Grouped Data

Lognormal distribution has abundant applications in various fields. In literature, most inferences on the two parameters of the lognormal distribution are based on Type-I censored sample data. However, exact measurements are not always attainable especially when the observation is below or above the detection limits, and only the numbers of measurements falling into predetermined intervals can be recorded instead. This is the so-called grouped data. In this paper, we will show the existence and uniqueness of the maximum likelihood estimators of the two parameters of the underlying lognormal distribution with Type-I censored data and grouped data. The proof was first established under the case of normal distribution and extended to the lognormal distribution through invariance property. The results are applied to estimate the median and mean of the lognormal population.

Exchange rate overvaluation under hyperinflation: The case of Peru

This dissertation examines the behavior of the exchange rate under two different scenarios. The first one is characterized by, relatively, low inflation or a situation where prices adjust sluggishly. The second is a high inflation economy where prices respond very rapidly even to unanticipated shocks. In the first one, following a monetary expansion, the exchange rate overshoots, i.e. the nominal exchange rate depreciates at a faster pace than the price level. Under high levels of inflation, prices change faster than the exchange rate so the exchange rate undershoots its long run equilibrium value.^ The standard work in this area, Dornbusch (1976), explains the overshooting process in the context of perfect capital mobility and sluggish adjustment in the goods market. A monetary expansion will make the exchange rate increase beyond its long run equilibrium value. This dissertation expands on Dornbusch's model and provides an analysis of the exchange rate under conditions of currency substitution and price flexibility, characteristics of the Peruvian economy during the hyper inflation process that took place at the end of the 1980's. The results of the modified Dornbusch model reveal that, given a monetary expansion, the change in the price level will be larger than the change in the exchange rate if prices react more than proportionally to the monetary shock.^ We will expect this over-reaction in circumstances of high inflation when the velocity of money is increasing very rapidly. Increasing velocity of money, gives rise to a higher relative price variability which in turn contributes to the appearance of new financial (and also non-financial) instruments that report a higher return than the exchange rate, causing people to switch their demand for foreign exchange to this new assets. In the context of currency substitution, economic agents hoard and use foreign exchange as a store of value. The big decline in output originated by hyper inflation induces people to sell this hoarded money to finance current expenses, increasing the supply of foreign exchange in the market. Both, the decrease in demand and the increase in supply reduce the price of foreign exchange i.e. the real exchange rate. The findings mentioned above are tested using Peruvian data for the period January 1985-July 1990, the results of the econometric estimation confirm our findings in the theoretical model. ^

Improving the predictability of time series by chaotic parameter estimation

Limited literature regarding parameter estimation of dynamic systems has been identified as the central-most reason for not having parametric bounds in chaotic time series. However, literature suggests that a chaotic system displays a sensitive dependence on initial conditions, and our study reveals that the behavior of chaotic system: is also sensitive to changes in parameter values. Therefore, parameter estimation technique could make it possible to establish parametric bounds on a nonlinear dynamic system underlying a given time series, which in turn can improve predictability. By extracting the relationship between parametric bounds and predictability, we implemented chaos-based models for improving prediction in time series. ^ This study describes work done to establish bounds on a set of unknown parameters. Our research results reveal that by establishing parametric bounds, it is possible to improve the predictability of any time series, although the dynamics or the mathematical model of that series is not known apriori. In our attempt to improve the predictability of various time series, we have established the bounds for a set of unknown parameters. These are: (i) the embedding dimension to unfold a set of observation in the phase space, (ii) the time delay to use for a series, (iii) the number of neighborhood points to use for avoiding detection of false neighborhood and, (iv) the local polynomial to build numerical interpolation functions from one region to another. Using these bounds, we are able to get better predictability in chaotic time series than previously reported. In addition, the developments of this dissertation can establish a theoretical framework to investigate predictability in time series from the system-dynamics point of view. ^ In closing, our procedure significantly reduces the computer resource usage, as the search method is refined and efficient. Finally, the uniqueness of our method lies in its ability to extract chaotic dynamics inherent in non-linear time series by observing its values. ^