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Adil, Muhammad (2018) Projecting Pakistan Population with a Bayesian Hierarchical approach. [Ph.D. thesis]

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Abstract (english)

UN has generated projections for demographic components for all countries of the world deterministically before revising the methodology and recently started to generate probabilistic projections at national level , but regional/sub national level trajectories are yet to be generated. In this thesis, probabilistic projections for total fertility rate (TFR) , life expectancy at birth for males and females and population totals at national as well as regional level have been generated based on Bayesian Hierarchical modeling approach. The trajectories were also generated for variable number of countries and it was observed that continent based trajectories were enough to model the future pattern instead of going for entire globe data. This in result saves time and decreases the influence of developed world demographic pattern on developing world and vise versa. TFR results were compared based on different values of $\mu$, (i.e., values less than and more than 2.1, which is the ultimate level of replacement) and with the trajectories generated based on Bayesian Hierarchical modeling approach for phase III. For values greater than 2.0, the trajectories were taking long time to converge to replacement level than for smaller values. On the other hand trajectories of Life Expectancy have revealed significant gap between male and female projections at national as well as regional level. It was also observed that Balochistan region has lower life expectancy at birth than the rest of the regions of Pakistan. The trajectories for Population totals were generated based on the probabilistic TFR and life expectancy trajectories. The results revealed a total population of over 207 million at national level which nearly coincides with the recently released provisional figures of census. Furthermore, the results are significantly different from previously adopted deterministic results and could be a good substitute over classical deterministic approaches.

Abstract (italian)

Le proiezioni demografiche delle Nazioni Unite per tutti i paesi del mondo sono sempre state ottenute attraverso un approccio puramente deterministico, e solo recentemente approcci probabilistici sono stati considerati per i livelli nazionali, mentre le traiettorie regionali o sub nazionali non sono ancora state considerate. In questa tesi, le proiezioni probabilistiche del tasso di fecondità, dell'aspettativa di vita alla nascita per maschi e per femmine e il totale delle popolazioni sia a livello nazionale che a livello regionale sono state generate attraverso l'uso di modelli Bayesiani gerarchici.
Le traiettorie sono state sviluppate per diverse variabili numeriche specifiche di ogni paese dalle quali è emerso che le informazioni relative al continente risultano più efficienti rispetto a quelle globali per modellare l'andamento futuro dei fenomeni di interesse. Questo approccio permette sia di accelerare la procedura di stima del modello, sia di ridurre l'impatto legato alla variabilità degli eventi osservati su tutto il globo. I risultati per il tasso di fecondità globale sono stati confrontati rispetto a differenti livelli di $\mu$ (e quindi minori o maggiori di 2.1, che è l'ultimo valore considerato) e rispetto a diverse traiettorie generate dal modello gerarchico in fase III. Per valori maggiori di 2.1, le traiettorie hanno richiesto un tempo computazionale molto lungo per convergere al livello prestabilito rispetto a valori più piccoli. D'altro canto, le traiettorie per l'aspettativa di vita hanno rivelato un divario significativo tra i maschi e le femmine sia a livello nazionale che a livello regionale. E' stato osservato inoltre che nella regione del Balochistan l'aspettativa di vita alla nascita è pari almeno a quella del resto del paese. Le traiettorie per il totale delle popolazioni sono state generate basandosi sul tasso di fecondità globale e sulle aspettative di vita generate dal modello Bayesiano. I risultati hanno mostrato una popolazione totale di 207 milioni di persone per il livello nazionale, livello che coincide approssimativamente con i recenti e provvisori dati del censimento. I risultati ottenuti sono inoltre significativamente differenti da quelli fino ad ora ottenuti, ed evidenziano la qualità del nuovo approccio proposto come alternativa ai metodi deterministici fino ad ora utilizzati.

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EPrint type:Ph.D. thesis
Tutor:Mazzuco, Stefano
Ph.D. course:Ciclo 30 > Corsi 30 > SCIENZE STATISTICHE
Data di deposito della tesi:16 January 2018
Anno di Pubblicazione:15 January 2018
Key Words:Pakistan Population, Bayesian Hierarchical, model, TFR,Life Expectancy at birth
Settori scientifico-disciplinari MIUR:Area 13 - Scienze economiche e statistiche > SECS-S/01 Statistica
Struttura di riferimento:Dipartimenti > Dipartimento di Scienze Statistiche
Codice ID:11034
Depositato il:08 Nov 2018 09:41
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