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Research Skills and Academic Writing & Accounting Course

website·Indexed 31 days ago

https://www.pengjiaxin.com

Educational resources covering two primary domains: a comprehensive workflow for research skills and academic writing—including research questions, evidence synthesis, and literature reviews—and a detailed accounting course focusing on evidence-to-decisions, double-entry bookkeeping, revenue recognition, and financial reporting using the Northstar Cycles case study.

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What's inside Peng Jiaxin

  1. Overview of Standard Auction Formats in IPV Models

    In the Independent Private Values (IPV) model, $N$ bidders have valuations $\theta_i$ that are independently and identically distributed (i.i.d.) according to a distribution function $F(\theta)$ and density function $f(\theta)$.

    There are four classic auction formats:

    1. English (Ascending): Price increases publicly; the winner pays slightly more than the second-highest valuation.
    2. Dutch (Descending): Price decreases publicly; the first bidder to accept the price wins and pays that price.
    3. First-Price Sealed-Bid (FPSB): Bidders submit sealed bids; the highest bidder wins and pays their own bid.
    4. Second-Price Sealed-Bid (Vickrey): Bidders submit sealed bids; the highest bidder wins and pays the second-highest bid.

  2. Introduction to Economics Course Map

    The course is structured into 12 chapters, moving from individual choice to global trade. Each chapter focuses on a central question and produces a specific analytical output.
    | Chapter | Central Question | Main Output |
| :--- | :--- | :--- |
| 1. Choice and opportunity cost | What is given up, and what changes at the margin? | decision table |
| 2. Demand, supply, and welfare | How do plans become prices, quantities, surplus, or shortage? | equilibrium analysis |
| 3. Elasticity, revenue, and tax incidence | How strongly do buyers and sellers adjust? | elasticity and incidence calculation |
| 4. Firms, market power, and market failure | When does private choice fail to produce a socially preferred outcome? | policy diagnosis |
| 5. GDP, income, wealth, and welfare | What do national accounts measure—and omit? | accounts table |
| 6. Inflation and labour markets | Are prices, purchasing power, and employment improving? | indicator brief |
| 7. Productivity and growth | Why does output per person rise over decades? | growth decomposition |
| 8. Money, credit, and banking | How do bank balance sheets create credit and fragility? | balance-sheet stress test |
| 9. Business cycles and monetary policy | Is the shock to spending or productive capacity? | shock diagnosis |
| 10. Fiscal policy and public debt | When does the budget stabilise, crowd out, or build capacity? | fiscal memo |
| 11. Trade and exchange rates | How do trade and finance transmit international shocks? | open-economy chain |
| 12. Integrated cases | Which model—or combination—answers the question? | final policy brief |

  3. Overview of numerical methods for asset pricing

    Common numerical methods used to implement asset pricing models when analytical solutions are unavailable:

    • Monte Carlo: Approximates expectations, distributions, or prices via random paths. It converts "integration problems" into "averaging problems." Error decreases at a rate of $1/\sqrt{N}$.
    • Numerical Optimization: Finds optimal parameters or portfolio weights given a target function. Results are sensitive to constraints and initial value selection.
    • Maximum Likelihood Estimation (MLE): Selects parameters that make the observed data most likely under a assumed probability distribution. Efficient but sensitive to distribution assumptions.
    • Generalized Method of Moments (GMM): Estimates parameters using theoretical moment conditions (e.g., Euler equations) without requiring a full distribution. Sensitive to instrumental variables and weighting matrices.
    • Bootstrap: Estimates uncertainty of statistics via resampling. For time-series data, Block Bootstrap must be used to preserve local temporal dependencies (autocorrelation, volatility clustering).

  4. Overview of Instrumental Variable (IV) Methods

    Instrumental Variable (IV) methods are used as a research design to construct treatment variation using an exogenous 'encouragement' (the instrument). This is particularly useful when there is imperfect compliance (e.g., people are offered a training voucher but not all use it), allowing researchers to identify the causal effect of the treatment on an outcome.

  5. Overview of Descriptive Analytics principles

    Descriptive analytics is used to reconstruct what happened to a defined population. Beyond simple data summarization, its primary purpose is to test whether aggregate metrics hide critical issues such as skew, timing, selection bias, or operational failures.

  6. Overview of Planning and Control in Management Accounting

    The Planning and Control module focuses on using cost information for forward-looking planning and comparing those plans against actual results. Unlike retrospective archiving, management accounting helps managers set goals in advance and understand deviations after execution. Key components include:

    • CVP (Cost-Volume-Profit) Analysis: A simplified model analyzing the relationship between sales volume, price, cost, and profit.
    • Budgeting: Provides a baseline for action and serves as both a planning and control tool.
    • Variance Analysis and Performance Evaluation: Used to determine if issues stem from price, efficiency, quantity, scope of responsibility, or external conditions.

  7. Overview of Mechanism Design Theory

    Mechanism Design Theory, often called "Economic Engineering," is the process of reverse-engineering market rules to achieve a specific goal (such as efficiency, revenue maximization, or fairness). Unlike traditional economics, which analyzes the outcomes of existing market systems, mechanism design defines the desired outcome first and then creates a set of rules (the mechanism) that guides rational participants—acting in their own self-interest—to reach that goal.

  8. Overview of Behavioral and Experimental Microeconomics

    Behavioral economics challenges the 'perfect rationality' assumption of traditional economics by integrating psychological insights to explain systematic biases in human decision-making. Key concepts include:

    • Bounded Rationality: The use of heuristics and the presence of cognitive biases.
    • Prospect Theory: Focuses on loss aversion and framing effects.
    • Social Preferences: The influence of fairness and reciprocity on choices.
    • Intertemporal Choice: Concepts such as hyperbolic discounting and present bias.

    Experimental economics provides the methodology to test these theories (e.g., via Ultimatum Games or asset bubble experiments), often demonstrating where standard economic models fail. These insights are applied in policy through 'nudges'—altering the choice architecture to help individuals make better decisions and improve social welfare.

  9. Overview of Quantitative Trading System Components

    A typical quantitative research and trading platform consists of five primary functional modules:

    1. Data Platform: Handles data collection, cleaning, storage, and serving.
    2. Research Platform: Provides backtesting frameworks, factor libraries, and statistical tools.
    3. Trading & Execution System: Manages strategy signal reception, order generation, order placement, and trade execution reporting.
    4. Risk & Monitoring System: Real-time monitoring of risk metrics, strategy performance, and system health.
    5. Scheduling & Orchestration: Manages batch processing tasks (e.g., overnight backtesting, report generation) and real-time tasks (e.g., market data processing, strategy calculation).

  11. Overview of R Matrix Laboratory coding standards

    The R Matrix Laboratory focuses on translating classical time-series mathematical derivations into base R code. The goal is to implement mathematical objects and perform numerical identity checks rather than empirical dataset modeling. All code implementations must adhere to the following standards:

    • Use base R and the stats package.
    • Ensure every code cell is self-contained and runnable (compatible with webR).
    • Be explicit regarding dimensions and parameter conventions.
    • Include a numerical identity or error check following the implementation to verify the result.

  12. Overview of Numerical Methods for Asset Pricing

    Implementing asset pricing models requires moving from analytical derivations to numerical methods to simulate cash flows, estimate parameters, and validate results. Key methods include:

    • Monte Carlo: Used for understanding stochastic returns and path dependency.
    • Numerical Optimization: Used for portfolio construction and parameter calibration.
    • Bootstrap: Used for evaluating uncertainty, especially under time dependency.
    • MLE (Maximum Likelihood Estimation) & GMM (Generalized Method of Moments): Used for parameter estimation and risk premium estimation.
    • Model Testing: Used to distinguish between pricing errors, prediction errors, and economic value.