Crowding-in or Crowding-out? Modelling the Relationship between Public and Private Fixed Capital Formation Using Co-integration Analysis: The Case of Pakistan 1964-2000

This paper uses the Co-integrating VAR’s [Johansen (1988); Ericsson, et al. (1998)] to examine the relationship between economic growth, public investment, and private investment in the presence of unit roots. Exogeneity is not implicitly assumed but explicitly tested for, and evidence of co-integration and feedback between public and private investment leads to a model in the form of a parsimonious VAR. The analysis is conducted using 37 years of annual data for Pakistan. The analysis suggests that public investment has a positive impact on private investment, and that economic growth drives both private and public investment as predicted by the accelerator-based models.

Crowding-in or Crowding-out? Modelling the Relationship between Public and Private Fixed Capital Formation Using Co-integration Analysis: The Case of Pakistan 1964-2000

This paper uses the Co-integrating VAR’s [Johansen (1988); Ericsson, et al. (1998)] to examine the relationship between economic growth, public investment, and private investment in the presence of unit roots. Exogeneity is not implicitly assumed but explicitly tested for, and evidence of co-integration and feedback between public and private investment leads to a model in the form of a parsimonious VAR. The analysis is conducted using 37 years of annual data for Pakistan. The analysis suggests that public investment has a positive impact on private investment, and that economic growth drives both private and public investment as predicted by the accelerator-based models

Some Thoughts on the Whole Works-in-Progress Thing

Panel: Works in Progres

Study Of Phenothiazine On p53 Core Domain Mutant Y220C: Finding The Anti-tumor Activity Of Phenothiazine

The tumor suppressor protein p53 is a transcription factor that plays a key role in the prevention of cancer development. The p53 cancer mutation Y220C induces formation of a cavity on the protein's surface that can accommodate stabilizing small molecules. We have attempted with the help of virtual screening and molecular docking approach using Lamarckian Genetic Algorithm to elucidate the extent of specificity of p53 cancer mutation Y220C towards different class of Phenothiazines (an anti-cancer agent). 

The 393 residue p53 tumor suppressor protein exists in a dynamic equilibrium to form homotetramers. Each chain comprises several functional domains. The N terminal part of the protein consists of the trans-activation domain (residues 1–63) followed by a proline rich region (64– 92). The central (core) domain (p53 core domain) is responsible for binding. The C terminal part of p53 contains the tetramerization domain (residues 326–355) and the negative regulatory domain at the extreme C terminus (363–393), which contains phosphorylation and acetylation sites and regulates the DNA binding activity of p53.

The docking result of the study of 2,000 Phenothiazines demonstrated that the binding energies were in the range of -10.54 kcal/mol to -1.14 kcal/mol, with 8 molecules showing hydrogen bonds with the active site residues (Lys 164). All the selected 2000 inhibitors were selected on the basis of the structural specificity to the enzyme towards its substrate and inhibitors. Our research provides a blueprint for the design of more potent and specific drugs that rescue p53-Y220C