Saudi Sectorial Stochastic Dynamic Recursive Computable General Equilibrium (CGE) Model

The Gini-HDI Model aims to estimate the factors determining the Gini and HDI Indices and their dimensions and linking them to other economic models to estimate the impact of any future policy if the Gini Coefficient and the Human Development Index.

The Model was built using Factor Analysis (Explanatory Factor Analysis, Confirmatory Factor Analysis) and Structural Equation Analysis.

As the usual key economic indicators provide macro information, social indicators such as Gini and HDI provide insights in the society which could not be clear within the macro variables.

Nested Modelling: If Model A is nested in Model B, then the parameters of Model A are a subset of the parameters of Model B.

In the case of a Nested Macro Econometric Model, any previous policy, investment plan, social regulation, ..etc which was applied previously would be nested out of the historical data and then the historical data of selected key economic indicators would be resolved again using the model. The difference between the actual data and the revised estimated historical data without the policy which was applied already would represent the historical impact on each of the variables.

SNMEM could be linked to any economic and/or social model which Advanced Analytics has built and also to any index such as the Gini and HDI. Its methodology is statistical and it would pertain economic theory while linking to the appropriate model to answer the question needed about the historical impact of the policy under study.

The Model measures:

• Inertia: Tendency of current employment state to persist over time.
• Mobility Rate.
• Reliability: Probability a worker move from inactivity to employment before being unemployed.
• Activity Rate: Proportion of employed and unemployed.
• Entrant Rate: Measures the proportion of worker entering the labor market.
• Exit Rate: Proportion of worker exiting the labor market.
• Unemployment Rate: Proportion of unemployed worker.
• Give-up Rate: Probability for the unemployed that a transition to inactivity occurs before a transition to employment.
• Net Outflow: Difference between proportion of the workers who leave employment for inactivity and inactive worker who succeed to get a job.

The Saudi S-VAR is Vector Auto-Regressive Model of Major Macroeconomic Indicators, Augmented with a Structural Macro-econometric Model of 8 blocks and 14 Sectors (86 activities as per ISIC4) of the economy. The Model contains three main blocks, which are: a Nominal Block, a Real Block, and an Equilibrium Block. The Model includes indices such as: Oil GDP, Non-Oil GDP , Inflation, Unemployment, Money Supply, Government Expenditure, Government Revenue, Private Investment, Exports, Imports, World GDP, World Price Level, Oil Prices, Oil Production, FedFund rate, SIABOR, REER).  It is able to measure Short Term Economic Policy Impacts, sensitivity of inflation and growth to changes in major international indicators, and perform short term prediction of GDP growth, inflation and Saudi unemployment.

The MIMIC Model is a Structural Equation Model which takes into account the determinants of Shadow Economy (Informal Economy) represented in the Shadow Economy’s causes and indicators. The Model determines the size of the shadow economy as a percentage of the Nominal Gross Domestic Product (GDP) and provides Med/Long term forecasts of the size of the shadow economy of Saudi Arabia. It could disaggregate the shadow economy into two parts, One is accounted for in the GDP and the other is not accounted for depending on the method of estimating the GDP. The Cause  and Indicator Variables of this MIMIC Model are as follows:

T/Y : Total Private Sector Tax (T) / Total Nominal GDP (Y)

F: Financial Sector Development Index = Value Added of Financial Sector / Value Added of Energy and Utility Sector.

S/LF: Self Employment(S)/Total Labour Force (LF)

U: Saudi Unemployment Rate

C/M2: Currency Demand (C) / Broad Money including timed deposits (M2)

R/W: Total

The Gambit Toolkit is a generic game theoretic model for solving extensive and normal form game theoretic models. analyzing general games in extensive or strategy form and computing Nash equilibria and other solution concepts in games.

Gambit is for finite games only. Because of the mathematical structure of finite games, it is possible to write many general- purpose routines for analyzing these games. Thus, Gambit can be used in a wide variety of applications of game theory. However, games that are not finite, that is, games in which players may choose from a continuum of actions, or in which players may have a continuum of types, do not admit the same general-purpose methods.

Gambit is for noncooperative game theory only. Gambit focuses on the branch of game theory in which the rules of the game are written down explicitly, and in which players choose their actions independently. Gambit’s analytical tools center primarily around Nash equilibrium, and related concepts of bounded rationality such as quantal response equilibrium. Gambit does not at this time provide any representations of, or methods for, analyzing games written in cooperative form. (It should be noted that some problems in cooperative game theory do not suffer from the computational complexity that the Nash equilibrium problem does, and thus cooperative concepts could be an interesting future direction of development.

Analyzing large games may become infeasible surprisingly quickly. While the specific formal complexity classes of computing Nash equilibria and related concepts are still an area of active research, it is clear that, in the typical case, the amount of time required to compute equilibria increases rapidly in the size of the game. In other words, it is quite easy to write down games which will take Gambit an unacceptably long amount time to compute the equilibria of. There are two ways to deal with this problem in practice. One way is to better identify good heuristic approaches for guiding the equilibrium computation process. Another way is to take advantage of known features of the game to guide the process. Both of these approaches are now becoming areas of active interest. While it will certainly not be possible to analyze every game that one would like to, it is hoped that Gambit will both contribute to these two areas of research, as well as make the resulting methods available to both students and practitioners.