Neoclassical and New Classical Macroeconomics

Continue with the blog Keynesianism and Monetarism. Here is the summary of school of economic theory.

Classical Economics

Starting with The Wealth of Nations, 1776, by Adam Smith. The central idea is that the market can be self-correcting. The central assumption implied is that all individuals choose to maximise their utility.

Neoclassical Economics

Neoclassical economics is formalised by Alfred Marshall (Marshallian demand, and Cambridge quantitative theory of money). The school is based on the mathematical formulation of the general equilibrium by Léon Walras (Walras’ Law).

Neoclassical economics states that the production, consumption and valuation (pricing) of goods and services are driven by the supply and demand model. Value is determined by maximising utility s.t. constraints.

Assumptions: 1. people have rational preferences (complete and transitive, see R100 at the Cambridge uni); 2. individuals maximise their utility and firms maximise profits; 3. people act independently on the basis of full and relevant information.

Neoclassical schools dominated until the Great Depression during the 1930s. However, John Maynard Keynes led with the publishment of The General Theory of Employment, Interest and Money. Keynesian dominated until 1973-1975 recession triggered by the 1973 oil crisis (stagflation crisis resulted from oil price increase) that Keynesian policy failed to reduce unemployment and also lead to hyperinflation. Phillips curve also failed because high unemployment and inflation came together. Then, new classical took the dominant.

New Neoclassical Economics

The new classical school works on real business cycle (Real Business Cycle model) theory that used fully specified general equilibrium models and used changes in technology to explain fluctuations in economic output.

Modigliani-Miller (M&M) Theorem

M&M theorem (Modigliani and Miller, 1958) is used to value a firm. It states that a firm’s value is based on its ability to earn revenue plus its risk of underlying assets. The way a firm finances its operations should not affect its value.

At its most basic level, the theorem argues that, with certain assumptions in place, it is irrelevant whether a company finances its growth by borrowing, by issuing stock shares, or by reinvesting its profits.

Assumptions are 1. the markets are completely efficient; 2. there are no costs of bankruptcy or agency dynamics and no taxes.

However, there are of course taxes and costs in the reality, and the assumptions do not hold. Therefore, the M&M theorem implies that firms are more valuable if financed by debts than financed by equities. The reason is the tax shield effects of debts.

Mathematic Example

Consider two companies, same risks, same expected cash flow before interest, \( Y\).

  1. Co1, has debt with market value of \(D_1\). Total market value \(V_1=E_1+D_1\).
  2. Co2, has no debt. \(V_2=E_2\), market value of equity.

We can invest,

  • Investment A: We own a fraction \(a\) (e.g. 6%) of shares in Co1z. They worth \(aE_1\) (e.g. $6,000). Expected cash flow from investment A, \(y_A\), is:

$$(y_A=\underbrace{a}_{SharesOwned} \times \underbrace{(Y-R_D D_1)}_{Co1’s EarningAfterInterest}$$

Co1 needs to pay an interest rate of its debt, \(R_D D_1\). Purchasing Co1 means only purchasing the equity of Co1, which is EV-Debts.

  • Investment B: We sell the shares of Co1. We receive amount \(aE_1\) ($6,000). Then, we use this to buy shares in Co2, which is ungeared.

To Produce the same gearing and risk as investment A, we borrow a further amount worth \(aD_1\) (‘home-made gearing’ or ‘artificially gearing’), at interest rate \(R_D\), and buy more shares in Co2.

E.G. if gearing of Co1 is \( \frac{D_1}{D_1+E_1}=0.25\), then to get same gear for our investment B, we borrow \(6,000\times \frac{0.25}{0.75}=2,000\).

Expected cash flow from investment B, \( y_B\), is:

$$ y_B=\frac{aE_1+aD_1}{E_2}Y-R_D\times aD_1 = a\frac{V_1}{V_2}Y-R_D\times aD_1$$

In equilibrium, \( y_A=y_B\). Otherwise, arbitrage opportunity emerges. Therefore, we get \(V_1=V_2\).

In conclusion, gearing does not affect value (Equity plus Debt).

Implication

Since expected net cash flow (\(Y\)) and company value are the same for each company, the cost of equity for ungeared Co2 and WACC for geared Co1 must be equal. So WACC must be constant with respect to gearing.

Let the cost of equity for a company with no debt be \(R_{ungeared}\).

$$ R_{ungeared}=R_D\frac{D}{V}+R_E\frac{E}{V}=WACC$$

$$ R_{ungeared}=R_D\frac{D}{D+E}+R_E\frac{E}{D+E}=WACC$$

WACC is constant w.r.t. \(\frac{D}{V}\).

$$ R_{ungeared}(D+E)=R_DD+R_EE$$

$$ R_EE=R_{ungeared}(D+E)-R_DD $$

$$ R_E=R_{ungeared}+(R_{ungeared}-R_D)\frac{D}{E} $$

So we get a linear relation between cost of equity and D/E, assuming a constant cost of debt, assuming a constant cost of debt (and assuming \(R_{ungeared}>R_D\)). Implicly, changes in gearing structure (or how to finance the business) do not affect the WACC for a company.

The relation is shown in this figure

Violation of the constant debt assumption of course would make WACC unconstant.

MM Theory & Beta

In the CAPM, expected returns on assets differ because their betas differ.

So we can write,

$$ \beta_{ungeared}=\beta_{debt}\frac{D}{D+E}+\beta_{geared}\frac{E}{D+E} $$

$$\beta_{geared}=\beta_{ungeared}+(\beta_{ungeared}-\beta_{debt})\frac{D}{E}$$

, where \(\beta_{ungeared}\) denotes asset beta, and \( \beta_{geared}\) denotes actual beta of shares of a geared company (estimated from the market data).

Calculations are similar to those as above.

If assume \(\beta_{debt}=0\), then

$$ \beta_{ungeared}=\beta_{geared} \frac{E}{V}$$

$$ \beta_{geared}=\beta_{ungeared}(1+\frac{D}{E}) $$

A Modigliani-Miller Theorem for Open-Market Operations

Monetary policy determines the composition of the government’s portfolio. Fiscal policy (the size of the deficit on the current account) determines the path of net government indebtedness. Wallace showed that alternative paths of the government’s portfolio consistent with a single path of fiscal policy can be irrelevant. The irrelevance means that both the equilibrium consumption allocation and the path of the price level are independent of the path of the government’s portfolio.

Typos are there. See the original paper issued by Fed in 1979.

Reference

Modigliani, F. and Miller, M.H., 1958. The cost of capital, corporation finance and the theory of investment. The American economic review48(3), pp.261-297.

Wallace, N., 1981. A Modigliani-Miller theorem for open-market operations. The American Economic Review71(3), pp.267-274.

分析市场的方式 Market Analysis in China

  1. 宏观贯穿短、中、长期 Macroeconomic condition needs to be consider in short, medium, and long run.
  2. 短期:微观+市场(供求)+宏观
  3. 中期:周期+政策+宏观
  4. 长期:生产要素(labour+capital)+技术进步+宏观

保险研究:增加老年人对于secure money的使用,减少money held in the pocket,增加Velocity of Money,提高货币政策的效率。

Potential Ideas and Researches: 保险资金对对货币政策有效性的影响。建模+实证(see the example in the previous post: A Cash-in-Advance Model)。

Interaction Effects between Consumption And Leisure

In the utility function, consumption and leisure time might have interaction effects.

$$ U(C(L),L)$$

Currently, people’s overtime working hours increased. People hence spend more time on working and making money, and thus there is less time on leisure for people. Though wages are earned, people’s preference for less valuable goods is decreasing because of less time left on leisure. Meanwhile, consumption also takes time. Therefore, less leisure results in less consumption even though nominal income increase (did not consider the real terms now).

Ideas: Model(consumption is a function a leisure) + Empirical Analysis (include the interaction term)

Kalman Filter

Definition

In statistics and control theory, Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. Kálmán, who was one of the primary developers of its theory.

Wikipedia

During my study in Cambridge, Professor Oliver Linton introduced the Kalman Filter in Time Series analysis, but I did not get it at that time. So, here is a revisit.

My Thinking of Kalman Filter

Kalman Filter is an algorithm that estimates optimal results from uncertain observation (e.g. Time Series Data. We know only the sample, but never know the true distribution of data or never know the true value when there are no errors).

Consider the case, I need to know my weight, but the bodyweight scale cannot give me the true value. How can I know my true weight?

Assume the bodyweight scale gives me error of 2, and my own estimate gives me error of 1. Or in another word, a weight scale is 1/3 accurate, and my own estimation is 2/3 accurate. Then, the optimal weight should be,

$$ Optimal Result = \frac{1}{3}\times Measurement + \frac{2}{3}\times Estimate $$

, where \( Measurement\) means the measurement value, and \(Estimate\) means the estimated value. We conduct the following transformation.

$$ Optimal Result = \frac{1}{3}\times Measurement +Esimate- \frac{1}{3}\times Estimate $$

Optimal Result = Esimate+\frac{1}{3}\times Measurement – \frac{1}{3}\times Estimate

Optimal Result = Esimate+\frac{1}{3}\times (Measurement – Estimate)

Therefore, we can get

Optimal Result = Esimate+\frac{p}{p+r}\times (Measurement – Estimate)

, where \(p\) is the estimation error and \(r\) is the measurement error.

For example, if the estimation error is zero, then the fraction is equal to zero. Thus, the optimal result is just the estimate.

Applying Time Series Data

$$ Optimal Result_n=\frac{1}{n}\times (meas_1+meas_2+meas_3+…+meas_{n}) $$

Optimal Result_n=\frac{1}{n}\times (meas_1+meas_2+meas_3+…+meas_{n-1})\\ +\frac{1}{n}\times meas_n

Optimal Result_n=\frac{n-1}{n}\times \frac{1}{n-1}\times (meas_1+…+meas_{n-1})\\ +\frac{1}{n}\times meas_n

Iterating the first term because\( \frac{1}{n-1}\times (meas_1+…+meas_{n-1}) = Optimal Result_{n-1} \),

Optimal Result_n=\frac{n-1}{n}\times Optimal Result_{n-1}\\ +\frac{1}{n}\times meas_n

Optimal Result_n=Optimal Result_{n-1}\\ -\frac{1}{n}\times Optimal Result_{n-1} +\frac{1}{n}\times meas_n

OResult_n=OResult_{n-1}+\frac{1}{n}\times (meas_n-OResult_{n-1})

Kalman Filter Equation

$$ \hat{x}_{n,n}=\hat{x}_{n,n-1}+K_n(z_n-\hat{x}_{n,n-1}) $$

$$ K_n=\frac{p_{n,n-1}}{p_{n.n-1}+r_n} $$

, where \(p_{n,n-1}\) is Uncertainty in Estimate, \(r_n\) is Uncertainty in Measurement, \(\hat{x}_{n,n}\) is the Optimal Estimate at \(n\), and \(z_n\) is the Measurement Value at \(n\).

The Optimal Estimate is updated by the estimate uncertainty through a Covariance Update Equation,

$$ p_{n,n}=(1-K_n)p_{n,n-1} $$

In a more intuitive way (1),

$$ OEstimate_n=OEstimate_{n-1}+K_n (meas_n-OEstimate_{n-1})$$

$$ K_n=\frac{OEstimateError_{n-1}}{OEstimateError_{n-1}+MeausreError_n}$$

$$OEstimateError_{n-1}=(1-K_{n-1})\times OEstimateError_{n-2}$$

Example

numMeasMeasErrorKOEstimateOEstimateError
0755
18130.62578.751.875
28330.38461580.384621.153846
37930.277778800.833333
47830.21739179.565220.652174
58130.17857179.821430.535714
67930.15151579.696970.454545
78030.13157979.736840.394737
87830.11627979.534880.348837
98130.10416779.68750.3125
107930.0943479.622640.283019
118030.08620779.655170.258621
127830.07936579.523810.238095
138130.07352979.632350.220588
147930.06849379.589040.205479
158230.06410379.743590.192308

A Senior Study

Estimation Equation:

$$ \hat{x}_k^-=A\hat{x}_{k-1}+Bu_k $$

$$ P_k^-=AP_{k-1}A^T+Q$$

Update Equation (same as the one I just introduced in (1)):

$$K_k=\frac{P_k^- C^T}{CP_k^-C^T+R}$$

$$ \hat{x}_k^-=A\hat{x}_{k-1}+K_k(y_k-C\hat{x}_k^-) $$

$$ P_k=(1-K_kC)P_k^-$$

Intuitively, I need \( \hat{x}_{k-1}\) (, which is the weight last week) to calculate the optimal estimate weight this week \(\hat{x}_k\). Firstly, I estimate the weights this week \(\hat{x}_k^-\) and measure the weight this week \(y_k\). Then, combine them to get the optimal estimate weights this week.

Reading

The application of the Kalman Filter could be found in the following reading. Also, I will continue in my further study.

https://towardsdatascience.com/state-space-model-and-kalman-filter-for-time-series-prediction-basic-structural-dynamic-linear-2421d7b49fa6

Reference

https://www.kalmanfilter.net/kalman1d.html

https://www.bilibili.com/video/BV1aS4y197bT?share_source=copy_web

近期经济情况

宏观经济情况 2021年11月14日

1. 目前国内情况仍然产能过剩,所以依旧是买方市场。国际大宗商品涨价带来的原材料上游成本上涨不能被中下游企业通过提价(终端产品)转移到消费者中,因为涨价就意味着industrial orgnisation原理中丧失市场。所以下游企业只能自己消化上游PPI带来的利润下降。

解决:保供稳价

2. US情况:供给端乏力,cost-pull inflation情况。供给方面消费意愿极低(推断),所以大额G使Aggregate Demand维持,Gov Spending仍为正但减少,可能会使AD减少。最终总供给+总需求双重下降导致经济衰退。中国情况:预期财政政策货币政策目标仍然是拉动经济刺激消费,扩大内需,同时保证出口。出口方面,他国通胀情况使外币升值,增加本国出口额。同时参考中金宏观“紧信用、松货币、宽财政”:紧信用为降低风险,货币+财政(降低T以刺激C+I)支持刺激消费。

Liquidity Trap

Recall the Euler condition in the previous blog post A Cash-in-Advance Model.

$$ u'(y_t)=\beta(1+i_t)\frac{p_t}{p_{t+1}}u'(y_{t+1}) $$

Assumption

For simplification, we assume no government spending, \(g_t\), government debt, \(d_t\), and taxes, \(T_t\). Also, we assume money is stable such that \(m_t=m_{t+1}=m\) (so there is not seignorage). We here consider \(y_t\) is exogenous.

Recall

Suppose that \( y_t=u_{t+1}=…=y\), then

$$\quad 1=\beta(1+i_{t+1})\frac{p_t}{p_{t+1}}$$

Now if guess both \(x_{t+1}=x_{t+2}=0\), then the velocity of money \(v_t=1\).

\( \quad p_t=p_{t+1}=\frac{m}{y}, \quad \) and \(\quad i_{t+1}=\frac{1}{\beta}-1\geq0\)

P.S. if violate the guess \(x_{t+1}=x_{t+2}=0\), then the euler equation shows \(1+\beta (1+i_{t+1})\frac{p_t}{p_{t+1}}\) would be \( p_{t+1}=\beta p_{t}\). So, \( p_{t+1}<p_t\). By QTM \(m \cdot v_t= p_t \cdot y\) (\(m, y\) are constant), \( v_{t+1}<v_T\) must be true to make next-period price level be low than the current price level. Lower velocity means \( x_{t+2}>x_{t+1}\) (people would hoard more money on hand in the next period). The loop begins, and price level would decline in the following periods.

If future outputs decrease,

u'(y_t)=\beta(1+i_t)\frac{p_t}{p_{t+1}}u'(y_{t+1})

If replace \(p_{t+1}=\frac{m_{t+1} v_{t+1}}{y_{t+1}}\),

u'(y_t)=\beta(1+i_t)\frac{p_t \times y_{t+1}}{m_{t+1}v_{t+1}}u'(y_{t+1})

u'(y_t)=\beta(1+i_t)\frac{p_t \times y_{t+1}}{m_{t+1}-x_{t+2}}u'(y_{t+1})

Here, by complementary slackness, \( x_{t+2}\times i_{t+1}=0\).

If replace \(p_{t+1}=\frac{m_{t+1} v_{t+1}}{y_{t+1}}\), =\frac{m_{t+1}}{y_{t+1}}\) by assume not in liquidity trap in the first so \(v_{t+1}=1\). Then we get,

u'(y_t)=\beta(1+i_t)\frac{p_t \times y_{t+1}}{m_{t+1}}u'(y_{t+1})

We, in the following, assume \(x u'(x)\) is decreasing in x.

If the economy experiences a fall in period \( t+1\) output from \(y_{t+1}\) to \(y’_{t+1}\). What happens to the nominal interest rate?

We write it in this way for simplification.

u'(y)=\beta(1+i_t)\frac{p_t }{m}y’u'(y’)

As \(y_{t+1}\) decrease, \(y’u'(y’)\) increase as our assumption. The LHS keeps stable, so the interest rate has to decrease to keep the equality holding. Therefore, \(i_{t+1}\) we’ll eventually hit zero.

As \( i_{t+1}=0\), the economy enters into the liquidity trap, and people start to hoard money ,\(x_{t+1}>0\). Recall the QTM equation, \(p_t=\frac{ m_t-x_{t+1} }{y_t}=\frac{mv_t}{y} \), \(p_t\) would decrease. So, the price level at time \(t\) finally decreases as well.

From the figure, we can find that once the effective federal fund rate (The effective federal funds rate (EFFR) is calculated as a volume-weighted median of overnight federal funds transactions) hits zero, excess reserves increases. Injecting more money would only cause excess money reserves in the liquidity trap.

If future outputs decrease and price is sticky,

An extension. If the price is “sticky” in the short run. In other words, \( \bar{p}_t=\frac{m}{y}\), price cannot fall below a certain threshold. Then, a decrease in \(y_{t+1}\) would end up with decrease in current output \(y_t\). As shown in the following equation,

$$ u'(\hat{y})=\beta \frac{\bar{p}_t}{m}y’u'(y’) $$

Future output decrease, then RHS increases, and so LHS has to increase as well. \(\frac{\partial u'(y)}{\partial y}=u”(y)\) is negative. For example, in the isoelasticity form \( u(c)=\frac{c^{1-\sigma}}{1-\sigma} \), and \(0\leq \sigma \leq1\).

In summary, recession in \(t+1\) would bring down \(y_{t+1}\). Then, firstly, decrease \(i_{t+1}\) to 0; secondly, reduce \(p_t\) to \(\bar{y}\) if price is stikcy; and thirdly, drive \(y_t\) decrease in the end. (All those are based on the guess of \(x_{t+1}=x_{t+2}=0\))

In a liquidity trap with sticky prices, outputs become “demand-driven”. The reason is that the Euler Equation is derived from the private sector, and thus \(u'(y_t)=u'(c_t)\) if not replaced with the markets clearing condition in equilibrium. The equation would then show that the increase in the LHS is driven by a decrease in consumption. A disequilibrium starts. Finally, a recession begins if nothing happened to productive capacity.

Intuition

  • Private sectors initially earn income, say 100, and buy goods for100 as well (Normal situation).
  • When they receive a “news” that income will decrease in the future from \(y_{t+1}\) to \(y’\), then they all wish to save.
  • However, in the aggregate, nobody can save, because noboday want to borrow or invest.
  • So the interest rate, as the benefits of saving, decrease to eventally zero, and private sectors start to hoard cash.
  • Thus, instead of spending 100, they spend80 and save $20. The demand drives down current outputs.

Role of price stickiness

  • Initally, current and future outputs (endownments) are all $100. \(y_{t}=y_{t+1}=100\).
  • A news tells us future output decrease to 80. In the current period, we save20 and spend $80. Same as the above process.
  • So, current spending is 80 and future spending is100.

If the price is sticky, consume $80 today and price decreases 20% at the same time. Ending up with the same amount of current consumption, \(y_t\). No recession.

If the price is sticky, then agents spend $20 fewer goods in the current. Worse off. And recession.

A Good-Bad Quality Model

12 Nov 2021

1. Consider models including effective labours.

2. For producers (firms). How consumers preference of goods quality could be conveyed to producers. 消费者对质量好与坏的产品的需求倾向如何向生产者传递。

Consider a simplified model.

Firms:

$$ Y_{Good}=A\cdot L_{Good}^{\alpha} $$

$$ Y_{Bad}=A\cdot L_{Bad}^{\alpha} $$

\( s.t. \quad L_{Bad}\leq L_{Good} \)

That means good quality products take more factor inputs to produce.

$$ \pi_{Good}=P_{Good}\cdot Y_{Good}-W\cdot L_{Good} $$

\pi_{Bad}=P_{Bad}\cdot Y_{Bad}-W\cdot L_{Bad}

Consumers:

$$ u(C_{Good},C_{Bad})-v(L) $$

\( s.t. \quad P_{Good}C_{Good}+P_{Bad}C_{Bad}<=WL \) Budget Constraint (Non-negative constraint is ignored just now)

CE

Consumers:

$$\mathcal{L}= v(L) +\lambda (WL-P_{Good}C_{Good}-P_{Bad}C_{Bad}) $$

F.O.C.

$$ u_{C_{Good}}’-\lambda \cdot P_{Good}=0 $$

u_{C_{Bad}}’-\lambda \cdot P_{Bad}=0

$$ -v'(L)+\lambda W=0$$

$$So \frac{u_{C_{Good}}’}{P_{Good}}=\frac{u_{C_{Bad}}’}{P_{Bad}}, and \frac{u_{C_{Good}}’}{u_{C_{Bad}}’}=\frac{P_{Good}}{P_{Bad}}$$

Firms:

F.O.C.

$$ \alpha P_{Good} A L^{\alpha-1}_{Good}-W=0$$

\alpha P_{Bad} A L^{\alpha-1}_{Bad}-W=0

$$ So \frac{P_{Good}}{P_{Bad}}=( \frac{L_{Good}}{L_{Bad}} )^{1-\alpha} $$

Combining consumers and firms euler condition, we get

\frac{u_{C_{Good}}’}{u_{C_{Bad}}’} =( \frac{L_{Good}}{L_{Bad}} )^{1-\alpha}

Problems exist, reconsider it. Intra-temporal model + and intertemporal model in RBC framework.

3. 对于生产者公司模型的研究

Separate firms production \(Y^S=Y^{i}+Y^{-i}\), firms maxi long term total profits

$$ \max_{Y^{i}_t, Cost^{i}_t, Y^{-i}_t, Cost^{-i}_t } \sum_{t=0}^{\infty} \pi_t=A(s_t, m_t)P^{i}_t (Y^{i}_t-Cost^{i}_t)+P^{-i}_t(P^{-i}_t-Cost^{-i}_t) $$

s.t. \(Cost^{i}>Cost^{-i} \forall t \)

, where \(A(\cdot,\cdot)\) is a ratio function of \(s_t\) the market share and \(m_t\) the cross market occupation ratio (跨市场经营指数). Meanwhile, \(m_t\) and \(s_t\) are all functions of \(Y^{i}_t, Cost^{i}_t, Y^{-i}_t, Cost^{-i}_t \).

P.S. consider what is the competitive equilibrium in the long run. At the steady-state condition. VIE application and impacts to cross-market operations such as Tencent Alibaba and Facebook.

4. 消费者传递链条, 生产者->平台->消费者。 考虑情况:上游利润低是因为平台榨干上游利润,最终消费者剩余多,但是生产者剩余极低。导致Aggregate Supply 减少。

Revisit 24 Nov 2021

Generalise the model. I denote \(c_1, l_1, L_1, y_1\) as the consumption, labour supply and demand, and outputs of relative high-quality goods. Also, denote \(c_2, l_2, L_2, y_2\) as those of relative low-quality goods. Note that the high-low quality stated in this working blog only refers to relative quality.

Consumers maximise their utility function subject to the budget constraint. For a representative consumer, the utility function is,

$$ \max_{c_1, c_2, l_1, l_2} u(c_1,c_2,1-l_1,1-l_2) $$

$$ s.t. \quad (l_1\cdot w_1)^i (l_2 \cdot w_2)^{1-i}\geq P_1 c_1 +P_2 c_2 $$

$$ i\in \{0,1\} $$

The wealth of consumers is in Bernoulli form because we assume each consumer can only provide a unique kind of labour in productions.

Consumers provide labours \(l_1\) or \(l_2\), and consume goods \(c_1\) or \(c_2\).

Firms maximise profits. I simplify the model by considering only labour inputs as the factors input. The model could be further expanded by including capital term and letting the technology term be depending on other factor inputs. E.G. \(F( L, K )\).

$$ \max_{L_1, L_2} \pi = \max_{L_1, L_2} P_1 F(L_1)+P_2 F(L_2) – w_1 L_1 -w_2L_2 $$

Solve the model.

Consumer:

$$ \frac{{\partial} \mathcal{L}}{\partial l_1}: u’_3=i\cdot \lambda (w_1 l_1)^{i-1} (w_2 l_2)^{1-i} $$

$$ \frac{{\partial} \mathcal{L}}{\partial l_2}: u’_4=(1-i)\cdot \lambda (w_1 l_1)^{i} (w_2 l_2)^{-i} $$

$$ \frac{\partial \mathcal{L}}{\partial c_1}: u’_1=\lambda P_1 $$

$$ \frac{\partial \mathcal{L}}{\partial c_2}: u’_2=\lambda P_2 $$

And I can get,

$$ \frac{u’_4}{u’_3}=\frac{1-i}{i}\frac{w_1 l_1}{w_2 l_2} $$

$$ \frac{P_1}{P_2}=\frac{u’_1(c_1)}{u’_2(c_2)} $$

Firms:

$$ \frac{\partial \pi}{\partial l_1}: P_1 F’_{l_1}=w_1 $$

$$ \frac{\partial \pi}{\partial l_1}: P_2 F’_{l_2}=w_2 $$

And get,

$$ \frac{w_1}{w_2}=\frac{P_1 F’_{l_1}}{P_2 F’_{l_2}} $$

Friedman Rule

Let’s continue with the previous blog post The Neutrality of Money.

In the previous model, consumers maximise their utility subject to contraints.

$$ \max_{c_t, b_{t+1}, x_{t+1}} \sum_{t=0}^{\infty}\ \beta^{t} [u(c_t)-v(l_t)] $$

$$ p_{t-1}y_{t-1}+b_t(1+i_t)+x_{t}-T_t=x_{t+1}+p_t c_t+b_{t+1} $$

0 \leq x_{t+1}

$$ 0 \leq l_t \leq 1 $$

We have solved it and get the Euler condition,

v'(y)=\beta u'(y)\frac{1}{\pi}

Here, we would consider the Planner’s Problem that makes social optimal.

Planner’s Problem

In the planner’s problem, we would release the budget constraints and cash-in-advance constraints, because the planner only needs to achieve social optimal. The planner’s problem is as the following.

\max_{c_t, b_{t+1}, x_{t+1}} \sum_{t=0}^{\infty}\ \beta^{t} [u(c_t)-v(l_t)]

$$ s.t. \quad c_t=l_t $$

F.O.C.

$$ u'(c_t)=v'(l_t) $$

Here let’s compare the planner’s Euler equation with the private sector one.

To make them equal, the only thing we need to adjust is to let \( \beta\times\frac{1}{1+\pi}=1\). The implication is that we need \( \pi =\beta -1\). As in the steady state, the discount factor \( \beta = \frac{1}{1+r}\), so the optimal inflation rate should be \( \pi^*=\frac{-r}{1+r}\).

The implication is that the optimal inflation rate is negative and close to the negative real interest rate.

Cash Credit Good Model

Stokey and Lucas (1987) included the cash-credit good into the cash in advance model.

\max_{ \{ c_t,b_{t+1} \}_{t=0}^{\infty} } \sum_{t=0}^{\infty}\ \beta^{t} [u(c_t^1)+u(c_t^2)]

$$ s.t. \quad b_{t+1}+p_t c_t^1+p_{t-1} c_{t-1}^2 =(1+i_t)b_t+p_{t-1}y_{t-1}$$

In equilibrium, markets clear and resources constraints,

$$ y_{t-1}=c_{t-1}^1+c_{t-1}^2 $$

y_{t}=c_{t}^1+c_{t}^2

F.O.C.

$$ u'(c_t^1)=\lambda_t p_t $$

$$ u'(c_t^2)=\beta\lambda_{t+1}p_t $$

$$\lambda_t=\beta \lambda_{t+1}(1+i_{t+1})$$

Combining them we can get

$$ \frac{u'(c_t^1)}{ u'(c_t^2) }=1+i_{t+1}$$

The ratio of marginal utility is equal to one plus the nominal interest rate.

The implication is that people want to consume \(c_t^2\) instead of \(c_t^1\), pay money at the time at \(t\), and hold some bonds and earn the nominal interest rate.

However, the planner problem is that

$$ \frac{u'(c_t^1)}{ u'(c_t^2) }=1 $$

Thus, the optimal rule is to set \(i_{t+1}=0\).

The Euler equation in the steady state (\( c_t^i=c_{t+1}^i=…=c^i \)) is that,

$$ \beta \frac{1+i_{t+1}}{1+\pi_t}=1 $$

By plugging in \(i_{t+1}=0\), \(\pi^*=\beta -1 \), the Friedman rule also holds.