Iowa Individual Income Tax Annual Statistical Report 2003

Iowa Individual Income Tax Statistical Report 2003

Iowa Individual Income Tax Annual Statistical Report 2004

Iowa Individual Income Tax Statistical Report 2004

Iowa Individual Income Tax Annual Statistical Report 2005

Iowa Individual Income Tax Statistical Report 2005

A-1 Monthly Public Assistance Statistical Report Family Investment Program, September 2007

A-1 - Monthly Public Assistance Statistical Report Family Investment Program - September 2007

A-1 - Monthly Public Assistance Statistical Report Family Investment Program - October 2007

A-1 - Monthly Public Assistance Statistical Report Family Investment Program - October 2007

A-7 Statistical Report On FIP Applications And Cases Discontinued, April 2004

Monthly statistical report on FIP by the Iowa Department of Human Services

A-7 Statistical Report On FIP Applications And Cases Discontinued, December 2004

Monthly statistical report on FIP by the Iowa Department of Human Services

A-1 Monthly Public Assistance Statistical Report Family Investment Program, November 2007

A-1 - Monthly Public Assistance Statistical Report Family Investment Program - Nov 2007

Bringing game theory to hypothesis testing: Establishing finite sample bounds on inference

Small sample properties are of fundamental interest when only limited data is avail-able. Exact inference is limited by constraints imposed by speci.c nonrandomizedtests and of course also by lack of more data. These e¤ects can be separated as we propose to evaluate a test by comparing its type II error to the minimal type II error among all tests for the given sample. Game theory is used to establish this minimal type II error, the associated randomized test is characterized as part of a Nash equilibrium of a .ctitious game against nature.We use this method to investigate sequential tests for the di¤erence between twomeans when outcomes are constrained to belong to a given bounded set. Tests ofinequality and of noninferiority are included. We .nd that inference in terms oftype II error based on a balanced sample cannot be improved by sequential sampling or even by observing counter factual evidence providing there is a reasonable gap between the hypotheses.

Experiencing simulated outcomes

Whereas much literature has documented difficulties in making probabilistic inferences, it hasalso emphasized the importance of task characteristics in determining judgmental accuracy.Noting that people exhibit remarkable efficiency in encoding frequency information sequentially,we construct tasks that exploit this ability by requiring people to experience the outcomes ofsequentially simulated data. We report two experiments. The first involved seven well-knownprobabilistic inference tasks. Participants differed in statistical sophistication and answered withand without experience obtained through sequentially simulated outcomes in a design thatpermitted both between- and within-subject analyses. The second experiment involvedinterpreting the outcomes of a regression analysis when making inferences for investmentdecisions. In both experiments, even the statistically naïve make accurate probabilistic inferencesafter experiencing sequentially simulated outcomes and many prefer this presentation format. Weconclude by discussing theoretical and practical implications.

Further evidence on the statistical properties of real GNP

The well-known lack of power of unit root tests has often been attributed to the shortlength of macroeconomic variables and also to DGP s that depart from the I(1)-I(0)alternatives. This paper shows that by using long spans of annual real GNP and GNPper capita (133 years) high power can be achieved, leading to the rejection of both theunit root and the trend-stationary hypothesis. This suggests that possibly neither modelprovides a good characterization of these data. Next, more flexible representations areconsidered, namely, processes containing structural breaks (SB) and fractional ordersof integration (FI). Economic justification for the presence of these features in GNP isprovided. It is shown that the latter models (FI and SB) are in general preferred to theARIMA (I(1) or I(0)) ones. As a novelty in this literature, new techniques are appliedto discriminate between FI and SB models. It turns out that the FI specification ispreferred, implying that GNP and GNP per capita are non-stationary, highly persistentbut mean-reverting series. Finally, it is shown that the results are robust when breaksin the deterministic component are allowed for in the FI model. Some macroeconomicimplications of these findings are also discussed.

A-1 Monthly Public Assistance Statistical Report Family Investment Program, January 2008

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

A-1 Monthly Public Assistance Statistical Report Family Investment Program, Februray 2008

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

A-1 Monthly Public Assistance Statistical Report Family Investment Program, March 2008

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

A-1 Monthly Public Assistance Statistical Report Family Investment Program, April 2008

A-1 - Monthly Public Assistance Statistical Report Family Investment Program