Analytical framework: Economic models as a tool to understand markets

Introduction: Why We Need Economic Models

The economy is a vast and multifaceted system that is difficult to fully understand. To address this challenge, analysts and economists have developed simplified frameworks that break down complex processes into manageable components. These schematizations not only facilitate analysis but also allow for more informed predictions about variables such as inflation and unemployment.

Currently, these frameworks also gain relevance in the world of cryptocurrencies, where they provide theoretical foundations to interpret market dynamics that, at first glance, seem disconnected from traditional economics.

What components make up an economic model?

An economic model is a theoretical construct that abstractly represents the relationships between different market forces. Its purpose is to explain how various economic elements interact and to allow decision-makers (legislators, companies, analysts) to anticipate outcomes based on hypothetical scenarios.

The four pillars: Variables, parameters, equations, and assumptions

Variables: They are the elements that fluctuate within the model. The most common include:

• Price (monetary value of a good or service)
• Amount (production or consumption volume)
• Income (money flows to individuals or organizations)
• Interest rates (cost of credit)

Parameters: Fixed values that define how variables behave. For example, in an analysis of inflation versus unemployment, the parameters include the natural rate of unemployment (known as NAIRU: the unemployment rate that does not accelerate inflation), which indicates the equilibrium point of the labor market.

Equations: They are the mathematical formulas that connect variables and parameters. The Phillips curve exemplifies this: π = πe − β (u−un), where π represents current inflation, πe expected inflation, β inflation sensitivity, u actual unemployment, and un natural unemployment.

Assumptions: Logical constraints that limit the scope of the analysis. The most common are:

• Rational behavior (economic actors maximize benefits)
• Perfect competition (many participants, none dominant)
• Ceteris paribus (other factors remain constant during the analysis)

Construction Methodology: Step by Step

The process of developing an economic model follows a clear sequence:

1. Identify key variables and their connections

Let's start with a practical example: an apple market. The main variables would be:

• Price (P)
• Quantity demanded (Qd) - how many apples buyers wish to purchase
• Offered amount (Qs) - how many apples the sellers are willing to place

2. Estimate parameters from real data

Market information is collected to quantify:

• Price elasticity of demand: how sensitive buyers are to price changes
• Price elasticity of supply: how sensitive sellers are to price changes

For our example, let's imagine:

• Demand elasticity = -50 (for every $1 increase, demand decreases by 50 units)
• Elasticity of supply = 100 ( for every $1 increase, the supply rises by 100 units)

3. Formulate equations

With the defined parameters, the relationships are expressed:

• Qd = 200 − 50P
• Qs = −50 + 100P

4. Define operational assumptions

Limits are established: we assume perfect competition ( many sellers and buyers without market power ) and isolation of effects ( we analyze only the price, leaving other factors unchanged ).

5. Solve for equilibrium

We equal supply and demand: 200 − 50P = −50 + 100P 250 = 150P P = $1.67

Substituting: Qd = Qs ≈ 117 units

This is the point where the market stabilizes: at the price of $1.67, producers sell exactly what consumers buy.

Main Categories of Economic Models

Visual models: They use graphs and diagrams to illustrate relationships. Supply and demand curves are the classic example: their intersection shows the market equilibrium.

Empirical models: They start from theoretical equations but are fed with real data to validate hypotheses. They could demonstrate, for example, how much national investment varies when interest rates rise by 1%.

Mathematical models: Algebraic or calculative constructs that accurately represent economic theories with technical precision. They are rigorous but require specialized training to interpret.

Expectations Models: They incorporate what economic agents expect to happen in the future. If the population anticipates higher inflation, they tend to spend more now, increasing present demand.

Simulation Models: They use computer programs to create virtual scenarios. They allow experimentation with different variables without experimenting in reality, useful for understanding possible consequences of policies or crises.

Static vs. dynamic models: The former provides a snapshot of the economy at a specific moment, being simpler but less realistic. The latter integrates the time factor, showing how variables evolve, capturing cycles and long-term trends.

Applicability in cryptocurrencies: Beyond theory

Although economic analysis frameworks do not apply directly in blockchain networks, their theoretical value allows for the interpretation of behaviors in the crypto market:

Price Dynamics: The limited supply of coins ( like the fixed supply of Bitcoin ) interacts with fluctuating demand. A supply-demand model reveals how token scarcity can drive prices up when demand increases.

Impact of Fees: Transaction fees on blockchains function as cost parameters. High fees discourage usage; low fees encourage it. Transaction cost models predict how these structures affect user behavior and network efficiency.

Prospective Scenarios: Simulations allow for modeling possible futures. What would happen to the price of a cryptocurrency if regulation tightens? If mass adoption accelerates? If technological competitors emerge? Although theoretical, these exercises provide frameworks for anticipating developments.

Inherent Limitations

Limited realism of assumptions: Models often assume perfect competition and total rationality of agents. In reality, there are information asymmetries, irrational behaviors, and concentrated market power.

Over-simplification: By reducing complexity to make it manageable, models may overlook relevant factors. For example, a model that treats all consumers identically ignores individual variations that could impact outcomes.

Current Practical Applications

Policy Analysis: Governments simulate the effects of tax changes, public spending, or interest rates before implementing them, improving the quality of legislative decisions.

Forecast: Companies and administrations use models to predict future economic growth, unemployment, and inflation, allowing for strategic planning.

Business Strategy: Companies model anticipated demand for their products, adjusting production levels according to economic projections.

Classical economic models: Mandatory references

Supply and Demand Model: Fundamental structure that determines prices. Two curves ( of producers' supply, consumers' demand ) intersect at market equilibrium.

IS-LM Model: Connects interest rates with real output, analyzing simultaneous equilibrium in goods and money markets.

Phillips Curve: Describes the trade-off between inflation and unemployment: when inflation rises, unemployment falls, and vice versa.

Solow Growth Model: Explains long-term economic expansion through labor, capital accumulation, and technological progress, showing how these forces generate sustained growth.

Synthesis: Why Economic Models Matter

Economic frameworks translate complexity into clarity. They break down intricate interactions into understandable components, revealing how different forces generate specific outcomes.

For lawmakers, they are decision-making tools. For companies, they are planning maps. For cryptocurrency analysts, they are theoretical lenses that allow interpreting market dynamics that would otherwise seem chaotic.

A well-constructed economic model does not predict the future with accuracy, but it does provide frameworks for understanding scenarios, assessing risks, and grounding decisions with greater rigor than pure intuition.