Bibliographic record
Abstract
This dissertation consists of three independent essays in applied econometrics. Essay One: Unemployment and Criminality: Using Markov-Switching Models to Identify a Link. This paper studies the existence of a relationship between the unemployment rate and crime in the U.S. Using Markov Switching Autoregressive models the behaviour of four crime variables and unemployment rate during the period of study is investigated and different regimes for each variable are determined. Using some non-parametric measures such as the Concordance Index (Harding and Pagan 2002) and Independence of Chronologies (Bodman and Crosby 2005), among others, the independency of cycles of unemployment rate and crime variables is tested. The results of this phase show that there is no relationship between the unemployment rate and burglary and motor. However, for larceny and robbery the results are mixed. At the second phase, Markov Switching Vector Autoregressive models are also used to determine the states for both unemployment rate and each of the crime variable simultaneously. The results of this stage show that the effect of unemployment rate on larceny and motor depends on the state of the variables. For larceny this effect is either positive or null, and for motor it fluctuates between negative, null, and positive. Also the result shows that regardless of the state of the variables, the effect of unemployment on burglary and robbery is negative and null, respectively. Essay Two: Convergence in the Canadian Provinces: Evidence using Unemployment Rate. Quarterly time series data from Canada and the Canadian provinces for the period 1976:1--2005:3 are examined to determine if the unemployment rates in the Canadian provinces are converging to the national rate of unemployment. The paper checks for existence of stochastic convergence using recent unit root statistics, see Perron and Rodriguez (2003) and Rodriguez (2007); furthermore, it verifies the existence of beta-convergence using recently proposed methods by Vogelsang (1998), Perron and Yabu (2005, 2006), and Bai and Perron (1998, 2003). Results from different unit root tests, without and with structural breaks, confirm that stochastic convergence holds in all provinces except British Columbia. Next, the existence of beta-convergence is tested using different approaches to estimate the trend function. The results show that deterministic convergence holds and the unemployment rates of the Canadian provinces are converging to the unemployment rate of Canada. This conclusion is stronger when multiple breaks are allowed in the trend function, that is, using the approach of Bai and Perron (1998, 2003). Essay Three: Persistence in Unemployment: Evidence from Canada and the Canadian Provinces. This paper addresses the persistence of unemployment in Canada. To test this issue, we use quarterly unemployment rate data from Canada and the Canadian provinces from 1976--2005. First, we utilize different unit root tests, without and with structural breaks, and confidence intervals for the autoregressive coefficients, to test the null hypothesis of unit root. The results of these tests show that the unemployment rates in Canada and the Canadian provinces, except Nova Scotia and Quebec, do not have a unit root and are therefore, not persistent. Next, we check for stability of the persistence property of the data, using the Bai and Perron approach (1998, 2003), which allows for multiple breaks in data. The results from this section show that the persistence is not constant and is regime dependent. In addition, except for the unemployment rate in British Columbia, which is persistent in all regimes, the other series are not persistent in most regimes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".