EPS 2020APR29

> eps_year_xts["2019::"]
             [,1]
2019-01-01 134.39
2019-04-01 135.27
2019-07-01 132.90
2019-10-01 139.47
2020-01-01 138.15
2020-04-01 139.29
2020-07-01 144.62
2020-10-01 149.92
2021-01-01 154.52
2021-04-01 159.07
2021-07-01 163.75
2021-10-01 166.82

~$ tac eps.txt 
3/31/2019 2834.40 $37.99 $35.02 18.52 21.09 $153.05 $134.39
6/30/2019 2941.76 $40.14 $34.93 19.04 21.75 $154.54 $135.27
9/30/2019  2976.74 $39.81 $33.99 19.46 22.40 $152.97 $132.90
12/31/2019 3230.78 $39.18 $35.53 20.56 23.16 $157.12 $139.47
3/31/2020 (27.6%) 2584.59 $31.90 $24.94 18.52 21.62 $151.03 $129.39
6/30/2020 $29.40 $23.16 19.94 23.79 $140.29 $117.62
9/30/2020 $35.66 $30.33 20.55 24.55 $136.14 $113.96
12/31/2020 $39.12 $34.62 20.56 24.75 $136.08 $113.05
3/31/2021 $38.75 $33.45 19.57 23.02 $142.93 $121.56
6/30/2021 $41.34 $35.47 18.07 20.90 $154.87 $133.87
9/30/2021 $44.53 $39.12 17.09 19.61 $163.74 $142.67
12/31/2021 $46.25 $41.13 16.37 18.76 $170.87 $149.17

   
~$ tac eps.txt | awk '{gsub("\\$","",$NF);print "eps_year_xts[\"2019::\"]["NR"] <- "$NF}'
eps_year_xts["2019::"][1] <- 134.39
eps_year_xts["2019::"][2] <- 135.27
eps_year_xts["2019::"][3] <- 132.90
eps_year_xts["2019::"][4] <- 139.47
eps_year_xts["2019::"][5] <- 129.39
eps_year_xts["2019::"][6] <- 117.62
eps_year_xts["2019::"][7] <- 113.96
eps_year_xts["2019::"][8] <- 113.05
eps_year_xts["2019::"][9] <- 121.56
eps_year_xts["2019::"][10] <- 133.87
eps_year_xts["2019::"][11] <- 142.67
eps_year_xts["2019::"][12] <- 149.17

SIR model for R

## Load deSolve package
library(deSolve)

## Create an SIR function
sir <- function(time, state, parameters) {

  with(as.list(c(state, parameters)), {

    dS <- -beta * S * I
    dI <-  beta * S * I - gamma * I
    dR <-                 gamma * I

    return(list(c(dS, dI, dR)))
  })
}

### Set parameters
## Proportion in each compartment: Susceptible 0.999999, Infected 0.000001, Recovered 0
init       <- c(S = 1-1e-6, I = 1e-6, R = 0.0)
## beta: infection parameter; gamma: recovery parameter
parameters <- c(beta = 2.5, gamma = 0.14286)
## Time frame
times      <- seq(0, 70, by = 1)

## Solve using ode (General Solver for Ordinary Differential Equations)
out <- ode(y = init, times = times, func = sir, parms = parameters)
## change to data frame
out <- as.data.frame(out)
## Delete time variable
out$time <- NULL
## Show data

## Plot
matplot(x = times, y = out, type = "l",
        xlab = "Time", ylab = "Susceptible and Recovered", main = "SIR Model",
        lwd = 1, lty = 1, bty = "l", col = 2:4)

## Add legend
legend(40, 0.7, c("Susceptible", "Infected", "Recovered"), pch = 1, col = 2:4, bty = "n")

CLIs for March 2020 : News Release

08/04/2020 - The CLIs for March 2020 recorded the largest drop on record in most major economies in line with the considerable economic shock caused by the COVID-19 pandemic and its immediate impact on production, consumption and confidence in the wake of lockdown measures.

Over the next few months, in particular, care will be needed in interpreting the CLI.

Firstly, with considerable uncertainty around the duration of lockdown measures, the ability of leading indicators to predict future movements in the business cycle has been severely curtailed: current estimates of the CLI are able to provide meaningful signals on current movements in activity, and should therefore be viewed as coincident rather than leading.

Secondly, as always, the magnitude of the CLI decline should not be regarded as a measure of the degree of contraction in economic activity, rather it should be viewed as an indication of the strength of the signal that economies have entered a phase of contraction. For comparison, the signal is stronger now than it was at the time of the Financial Crisis.

Thirdly, the CLIs are not yet able to anticipate the end of the slowdown, especially as it is not yet clear how long, nor indeed severe, lock-down measures are likely to be. However, as the situation settles, even with a more prolonged lockdown, the CLI will begin to recover its ability to predict as firms and consumers begin to adapt to new (even if only short-term) realities, especially as governments begin to formulate and provide signals around longer term strategies, beyond the initial immediate measures they have had to impose.

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