Markovian Processes, Two-Sided Autoregressions and Finite-Sample Inference for Stationary and Nonstationary Autoregressive Processes.

In this paper, we develop finite-sample inference procedures for stationary and nonstationary autoregressive (AR) models. The method is based on special properties of Markov processes and a split-sample technique. The results on Markovian processes (intercalary independence and truncation) only require the existence of conditional densities. They are proved for possibly nonstationary and/or non-Gaussian multivariate Markov processes. In the context of a linear regression model with AR(1) errors, we show how these results can be used to simplify the distributional properties of the model by conditioning a subset of the data on the remaining observations. This transformation leads to a new model which has the form of a two-sided autoregression to which standard classical linear regression inference techniques can be applied. We show how to derive tests and confidence sets for the mean and/or autoregressive parameters of the model. We also develop a test on the order of an autoregression. We show that a combination of subsample-based inferences can improve the performance of the procedure. An application to U.S. domestic investment data illustrates the method.

Simulation-Based Finite-Sample Tests for Heteroskedasticity and ARCH Effects.

A wide range of tests for heteroskedasticity have been proposed in the econometric and statistics literature. Although a few exact homoskedasticity tests are available, the commonly employed procedures are quite generally based on asymptotic approximations which may not provide good size control in finite samples. There has been a number of recent studies that seek to improve the reliability of common heteroskedasticity tests using Edgeworth, Bartlett, jackknife and bootstrap methods. Yet the latter remain approximate. In this paper, we describe a solution to the problem of controlling the size of homoskedasticity tests in linear regression contexts. We study procedures based on the standard test statistics [e.g., the Goldfeld-Quandt, Glejser, Bartlett, Cochran, Hartley, Breusch-Pagan-Godfrey, White and Szroeter criteria] as well as tests for autoregressive conditional heteroskedasticity (ARCH-type models). We also suggest several extensions of the existing procedures (sup-type of combined test statistics) to allow for unknown breakpoints in the error variance. We exploit the technique of Monte Carlo tests to obtain provably exact p-values, for both the standard and the new tests suggested. We show that the MC test procedure conveniently solves the intractable null distribution problem, in particular those raised by the sup-type and combined test statistics as well as (when relevant) unidentified nuisance parameter problems under the null hypothesis. The method proposed works in exactly the same way with both Gaussian and non-Gaussian disturbance distributions [such as heavy-tailed or stable distributions]. The performance of the procedures is examined by simulation. The Monte Carlo experiments conducted focus on : (1) ARCH, GARCH, and ARCH-in-mean alternatives; (2) the case where the variance increases monotonically with : (i) one exogenous variable, and (ii) the mean of the dependent variable; (3) grouped heteroskedasticity; (4) breaks in variance at unknown points. We find that the proposed tests achieve perfect size control and have good power.

Exact Nonparametric Two-Sample Homogeneity Tests for Possibly Discrete Distributions.

In this paper, we study several tests for the equality of two unknown distributions. Two are based on empirical distribution functions, three others on nonparametric probability density estimates, and the last ones on differences between sample moments. We suggest controlling the size of such tests (under nonparametric assumptions) by using permutational versions of the tests jointly with the method of Monte Carlo tests properly adjusted to deal with discrete distributions. We also propose a combined test procedure, whose level is again perfectly controlled through the Monte Carlo test technique and has better power properties than the individual tests that are combined. Finally, in a simulation experiment, we show that the technique suggested provides perfect control of test size and that the new tests proposed can yield sizeable power improvements.

Finite-Sample Inference Methods for Simultaneous Equations and Models with Unobserved and Generated Regressors

We propose finite sample tests and confidence sets for models with unobserved and generated regressors as well as various models estimated by instrumental variables methods. The validity of the procedures is unaffected by the presence of identification problems or \"weak instruments\", so no detection of such problems is required. We study two distinct approaches for various models considered by Pagan (1984). The first one is an instrument substitution method which generalizes an approach proposed by Anderson and Rubin (1949) and Fuller (1987) for different (although related) problems, while the second one is based on splitting the sample. The instrument substitution method uses the instruments directly, instead of generated regressors, in order to test hypotheses about the \"structural parameters\" of interest and build confidence sets. The second approach relies on \"generated regressors\", which allows a gain in degrees of freedom, and a sample split technique. For inference about general possibly nonlinear transformations of model parameters, projection techniques are proposed. A distributional theory is obtained under the assumptions of Gaussian errors and strictly exogenous regressors. We show that the various tests and confidence sets proposed are (locally) \"asymptotically valid\" under much weaker assumptions. The properties of the tests proposed are examined in simulation experiments. In general, they outperform the usual asymptotic inference methods in terms of both reliability and power. Finally, the techniques suggested are applied to a model of Tobin’s q and to a model of academic performance.

Finite-Sample Diagnostics for Multivariate Regressions with Applications to Linear Asset Pricing Models

In this paper, we propose several finite-sample specification tests for multivariate linear regressions (MLR) with applications to asset pricing models. We focus on departures from the assumption of i.i.d. errors assumption, at univariate and multivariate levels, with Gaussian and non-Gaussian (including Student t) errors. The univariate tests studied extend existing exact procedures by allowing for unspecified parameters in the error distributions (e.g., the degrees of freedom in the case of the Student t distribution). The multivariate tests are based on properly standardized multivariate residuals to ensure invariance to MLR coefficients and error covariances. We consider tests for serial correlation, tests for multivariate GARCH and sign-type tests against general dependencies and asymmetries. The procedures proposed provide exact versions of those applied in Shanken (1990) which consist in combining univariate specification tests. Specifically, we combine tests across equations using the MC test procedure to avoid Bonferroni-type bounds. Since non-Gaussian based tests are not pivotal, we apply the “maximized MC” (MMC) test method [Dufour (2002)], where the MC p-value for the tested hypothesis (which depends on nuisance parameters) is maximized (with respect to these nuisance parameters) to control the test’s significance level. The tests proposed are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995. Our empirical results reveal the following. Whereas univariate exact tests indicate significant serial correlation, asymmetries and GARCH in some equations, such effects are much less prevalent once error cross-equation covariances are accounted for. In addition, significant departures from the i.i.d. hypothesis are less evident once we allow for non-Gaussian errors.

Monte Carlo Tests with Nuisance Parameters: A General Approach to Finite-Sample Inference and Nonstandard Asymptotics

The technique of Monte Carlo (MC) tests [Dwass (1957), Barnard (1963)] provides an attractive method of building exact tests from statistics whose finite sample distribution is intractable but can be simulated (provided it does not involve nuisance parameters). We extend this method in two ways: first, by allowing for MC tests based on exchangeable possibly discrete test statistics; second, by generalizing the method to statistics whose null distributions involve nuisance parameters (maximized MC tests, MMC). Simplified asymptotically justified versions of the MMC method are also proposed and it is shown that they provide a simple way of improving standard asymptotics and dealing with nonstandard asymptotics (e.g., unit root asymptotics). Parametric bootstrap tests may be interpreted as a simplified version of the MMC method (without the general validity properties of the latter).

Estimating Nonlinear DSGE Models by the Simulated Method of Moments

This paper studies the application of the simulated method of moments (SMM) for the estimation of nonlinear dynamic stochastic general equilibrium (DSGE) models. Monte Carlo analysis is employed to examine the small-sample properties of SMM in specifications with different curvature. Results show that SMM is computationally efficient and delivers accurate estimates, even when the simulated series are relatively short. However, asymptotic standard errors tend to overstate the actual variability of the estimates and, consequently, statistical inference is conservative. A simple strategy to incorporate priors in a method of moments context is proposed. An empirical application to the macroeconomic effects of rare events indicates that negatively skewed productivity shocks induce agents to accumulate additional capital and can endogenously generate asymmetric business cycles.

Whither RDS? An investigation of respondent driven sampling as a method of recruiting mainstream marijuana users.

Background: An important challenge in conducting social research of specific relevance to harm reduction programs is locating hidden populations of consumers of substances like cannabis who typically report few adverse or unwanted consequences of their use. Much of the deviant, pathologized perception of drug users is historically derived from, and empirically supported, by a research emphasis on gaining ready access to users in drug treatment or in prison populations with higher incidence of problems of dependence and misuse. Because they are less visible, responsible recreational users of illicit drugs have been more difficult to study. Methods: This article investigates Respondent Driven Sampling (RDS) as a method of recruiting experienced marijuana users representative of users in the general population. Based on sampling conducted in a multi-city study (Halifax, Montreal, Toronto, and Vancouver), and compared to samples gathered using other research methods, we assess the strengths and weaknesses of RDS recruitment as a means of gaining access to illicit substance users who experience few harmful consequences of their use. Demographic characteristics of the sample in Toronto are compared with those of users in a recent household survey and a pilot study of Toronto where the latter utilized nonrandom self-selection of respondents. Results: A modified approach to RDS was necessary to attain the target sample size in all four cities (i.e., 40 'users' from each site). The final sample in Toronto was largely similar, however, to marijuana users in a random household survey that was carried out in the same city. Whereas well-educated, married, whites and females in the survey were all somewhat overrepresented, the two samples, overall, were more alike than different with respect to economic status and employment. Furthermore, comparison with a self-selected sample suggests that (even modified) RDS recruitment is a cost-effective way of gathering respondents who are more representative of users in the general population than nonrandom methods of recruitment ordinarily produce. Conclusions: Research on marijuana use, and other forms of drug use hidden in the general population of adults, is important for informing and extending harm reduction beyond its current emphasis on 'at-risk' populations. Expanding harm reduction in a normalizing context, through innovative research on users often overlooked, further challenges assumptions about reducing harm through prohibition of drug use and urges consideration of alternative policies such as decriminalization and legal regulation.

Mental health treatment seeking by military members with posttraumatic stress disorder : findings on rates, characteristics, and predictors from a nationally representative Canadian military sample

Objective: The goal of this study was to identify rates, characteristies, and predictors of mental health treatment seeking by military members with posttraumatic stress disorder (PTSD). Method: Our sample was drawn from the 2002 Canadian Community Health Survey-Canadian Forces Supplement (CCHS-CF) dataset. The CCHS-CF is the first epidemiologic survey of PTSD and other mental health conditions in the Canadian military and includes 8441 nationally representative Canadian Forces (CF) members. Of those, 549 who met the criteria for lifetime PTSD were included in our analyses. To identify treatment rates and characteristics, we examined frequency of treatment contact by professional and facility type. To identify predictors of treatment seeking, we conducted a binary logistic regression with lifetime treatment seeking as the outcome variable. Results: About two-thirds of those with PTSD consulted with a professional regarding mental health problems. The most frequently consulted professionals, during both the last year and lifetime, included social workers and counsellors, medical doctors and general practitioners, and psychiatrists. Consultations during the last year most often took place in a CF facility. Treatment seeking was predicted by cumulative lifetime trauma exposure, index traumatic event type, PTSD symptom interference, and comorbid major depressive disorder. Those with comorbid depression were 3.75 times more likely to have sought treatment than those without. Conclusions: Although a significant portion of military members with PTSD sought mental health treatment, 1 in 3 never did. Trauma-related and illness and (or) need factors predicted treatment seeking. Of all the predictors of treatment seeking, comorbid depression most increased the likelihood of seeking treatment.

Preparation, Characterisation And Microwave Dielectric Properties Of Anbn-1o3n (N=5,6,8) Type Perovskite Compounds

The present study on the preparation , characterization and microwave dielectric properties of AnBn-1O3n (N=5,6,8) type perovskite compounds. The explored ceramics show dielectric constant between 11 and 54,quality factor in the range 2400 to 88900 GHz and Tf in the range -73 to +231ppm/0C.Most of the investigated cation deficient hexagonal perovskites show intermediate dielectric constant with high quality factors. This study gives a general introduction about material, scientific and technological aspects of DRs.Three important ,€r ,Q and Tf, used for the DR characterization are described. The relationship of the above parameters with the fundamental material characteristics is discussed. Different modes are excited when a DR is excited with suitable microwave spectrum of frequencies .A description of analytical determination of frequencies and construction of mode charts used for sample design and mode identification are also discussed. In this study several ceramics are developed for DR purposes, very little attention has been paid to grow the single crystals. It might be due to the fact that the difficulties and time involved in the growth of single crystals, big enough to function as microwave resonators make them expensive .However single crystals of these materials may have very high Q values. It is also possible that a better understanding of the dielectric properties in relation to the structure can be arrived using single crystals. Hence one of the future directions of dielectric resonator research should be to grow good quality single crystals of the above materials.

Preparation and Properties of Iodine-Doped Radiopaque Natural Rubber

Polymers exhibit low electron density and they are radiolucent. Polymers can be made radiopaque by different techniques. We report a method for the preparation of radiopaque material from natural rubber (NR). NR in its latex form was iodinated. Iodinated natural rubber (INR) was characterized by using UV, thermo gravimetric analysis (TGA), and X-ray images. INR was compounded at high and low temperatures and its physical properties were measured. The low temperature cured samples show good radiopacity and conductivity. The optical density of low temperature cured samples was measured.

Preparation and Characterisation of Spinel Ferrites - Their Incorporation in Rubber Matrix and Evaluation of Properties

The objectives of the proposed work are preparation of ceramic nickel zinc ferrite belonging to the series Ni1-XZnXFe2O4 with x varying from 0 to 1in steps of 0.2, structrural, magnetic and electrical characterization of Ni1-XZnXFe2O4, preparation and evaluation of Cure characteristics of Rubber Ferrite Composites (RFCs), magnetic characterization of RFCs using vibrating sample magnetometer (VSM), electrical characterization of RFCs and estimation of magnetostriction constant form HL parameters. The study deals with the structural and magnetic properties of ceramic fillers, variation of coercivity with composition and the variation of magnetization for different filler loadings are compared and correlated. The dielectric properties of ceramic Ni1-XZnXFe2O4 and rubber ferrite composites containing Ni1-XZnXFe2O4 were evaluated and the ac electrical conductivity (ac) of ceramic as well as composite samples can be calculated by using a simple relationship of the form ac = 2f tan 0r, with the data available from dielectric measurements. The results suggest that the ac electrical conductivity is directly proportional to the frequency

A new method for load matching in multimode microwave feating applicators based on the use of dielectric layer superposition

Usage of a dielectric multilayer around a dielectric Sample is studied as a means for improving the efficiency in multimode microwave- heating cavities. The results show that by using additional dielectric constant layers the appearance of undesired reflections at the sample-air interface is avoided and higher power -absorption rates within the sample and high -efficiency designs are obtained

Optimization of Preparation Techniques and Dielectric Study of Polyanilines in the Microwave & High Frequency Field

The thesis deals with the preparation and dielectric characterization of Poly aniline and its analogues in ISM band frequency of 2-4 GHz that includes part of the microwave region (300 MHz to 300 GHz) of the electromagnetic spectrum and an initial dielectric study in the high frequency [O.05MHz-13 MHz]. PolyaniIine has been synthesized by an in situ doping reaction under different temperature and in the presence of inorganic dopants such as HCl H2S04, HN03, HCl04 and organic dopants such as camphorsulphonic acid [CSA], toluenesulphonic acid {TSA) and naphthalenesulphonic acid [NSA]. The variation in dielectric properties with change in reaction temperature, dopants and frequency has been studied. The effect of codopants and microemulsions on the dielectric properties has also been studied in the ISM band. The ISM band of frequencies (2-4 GHz) is of great utility in Industrial, Scientific and Medical (ISM) applications. Microwave heating is a very efficient method of heating dielectric materials and is extensively used in industrial as well as household heating applications.

Photothermal and photoacoustic investigations on certain polymers and semi conducting materials

Material synthesizing and characterization has been one of the major areas of scientific research for the past few decades. Various techniques have been suggested for the preparation and characterization of thin films and bulk samples according to the industrial and scientific applications. Material characterization implies the determination of the electrical, magnetic, optical or thermal properties of the material under study. Though it is possible to study all these properties of a material, we concentrate on the thermal and optical properties of certain polymers. The thermal properties are detennined using photothermal beam deflection technique and the optical properties are obtained from various spectroscopic analyses. In addition, thermal properties of a class of semiconducting compounds, copper delafossites, arc determined by photoacoustic technique.Photothermal technique is one of the most powerful tools for non-destructive characterization of materials. This forms a broad class of technique, which includes laser calorimetry, pyroelectric technique, photoacollstics, photothermal radiometric technique, photothermal beam deflection technique etc. However, the choice of a suitable technique depends upon the nature of sample and its environment, purpose of measurement, nature of light source used etc. The polynler samples under the present investigation are thermally thin and optically transparent at the excitation (pump beam) wavelength. Photothermal beam deflection technique is advantageous in that it can be used for the detennination of thermal diffusivity of samples irrespective of them being thermally thick or thennally thin and optically opaque or optically transparent. Hence of all the abovementioned techniques, photothemlal beam deflection technique is employed for the successful determination of thermal diffusivity of these polymer samples. However, the semi conducting samples studied are themlally thick and optically opaque and therefore, a much simpler photoacoustic technique is used for the thermal characterization.The production of polymer thin film samples has gained considerable attention for the past few years. Different techniques like plasma polymerization, electron bombardment, ultra violet irradiation and thermal evaporation can be used for the preparation of polymer thin films from their respective monomers. Among these, plasma polymerization or glow discharge polymerization has been widely lIsed for polymer thin fi Im preparation. At the earlier stages of the discovery, the plasma polymerization technique was not treated as a standard method for preparation of polymers. This method gained importance only when they were used to make special coatings on metals and began to be recognized as a technique for synthesizing polymers. Thc well-recognized concept of conventional polymerization is based on molecular processcs by which thc size of the molecule increases and rearrangemcnt of atoms within a molecule seldom occurs. However, polymer formation in plasma is recognized as an atomic process in contrast to the above molecular process. These films are pinhole free, highly branched and cross linked, heat resistant, exceptionally dielectric etc. The optical properties like the direct and indirect bandgaps, refractive indices etc of certain plasma polymerized thin films prepared are determined from the UV -VIS-NIR absorption and transmission spectra. The possible linkage in the formation of the polymers is suggested by comparing the FTIR spectra of the monomer and the polymer. The thermal diffusivity has been measured using the photothermal beam deflection technique as stated earlier. This technique measures the refractive index gradient established in the sample surface and in the adjacent coupling medium, by passing another optical beam (probe beam) through this region and hence the name probe beam deflection. The deflection is detected using a position sensitive detector and its output is fed to a lock-in-amplifIer from which the amplitude and phase of the deflection can be directly obtained. The amplitude and phase of the deflection signal is suitably analyzed for determining the thermal diffusivity.Another class of compounds under the present investigation is copper delafossites. These samples in the form of pellets are thermally thick and optically opaque. Thermal diffusivity of such semiconductors is investigated using the photoacoustic technique, which measures the pressure change using an elcctret microphone. The output of the microphone is fed to a lock-in-amplificr to obtain the amplitude and phase from which the thermal properties are obtained. The variation in thermal diffusivity with composition is studied.