analistica/slides/sections/8.md

Trapani test

Infinite moments

For a Landau PDF: \begin{align*} E_L[x] &\longrightarrow + \infty \ V_L[x] \text{undefined} \end{align*}

. . .

For a Moyal PDF: \begin{align*} E_M[x] &= \mu + \sigma [ \gamma + \ln(2) ] \ V_M[x] &= \frac{\pi^2 \sigma^2}{2} \end{align*}

Infinite moments

• Check whether a moment is finite or infinite \begin{align*} \text{infinite} &\thus Landau \ \text{finite} &\thus Moyal \end{align*}

. . .

Trapani test

Trapani test

::: incremental

• Random variable \left\{ x_i \right\} sampled from a distribution f
• Sample moments according to f moments
• H_0: \mu_k \longrightarrow + \infty
• Statistic with 1 dof chi-squared distribution

:::

Trapani test

• Start with \left\{ x_i \right\}^N and compute \mu_k as:

  
    \mu_k = \frac{1}{N} \sum_{i = 1}^N |x_i|^k
  

. . .

• Generate r points \left\{ \xi_j\right\}^r according to G(0, 1) and define \left\{ a_j \right\}^r as:

  
    a_j = \sqrt{e^{\mu_k}} \cdot \xi_j
    \thus G'\left( 0, \sqrt{e^{\mu_k}} \right)
  

. . .

The greater \mu^k, the 'larger' G'

• if \mu_k \longrightarrow + \infty \thus a_j distributed uniformly

Trapani test

• Define the sequence: \left\{ \zeta_j (u) \right\}^r as:

  
    \zeta_j (u) = \theta( u -  a_j) \with \theta - \text{Heaviside}
  

. . .

\begin{center} \begin{tikzpicture} \definecolor{cyclamen}{RGB}{146,24,43} % line \draw [line width=3, ->, cyclamen] (0,0) -- (10,0); \node [right] at (10,0) {$u$}; % tic \draw [thick] (5,-0.3) -- (5,0.3); \node [above] at (5,0.3) {$u_0$}; % aj tics \draw [thick, cyclamen] (1,-0.2) -- (1,0.2); \node [below right, cyclamen] at (1,-0.2) {$a_{j+2}$}; \draw [thick, cyclamen] (2,-0.2) -- (2,0.2); \node [below right, cyclamen] at (2,-0.2) {$a_j$}; \draw [thick, cyclamen] (5.2,-0.2) -- (5.2,0.2); \node [below right, cyclamen] at (5.2,-0.2) {$a_{j+2}$}; \draw [thick, cyclamen] (6,-0.2) -- (6,0.2); \node [below right, cyclamen] at (6,-0.2) {$a_{j+3}$}; \draw [thick, cyclamen] (8.5,-0.2) -- (8.5,0.2); \node [below right, cyclamen] at (8.5,-0.2) {$a_{j+4}$}; % notes \node [below] at (1,-1) {0}; \node [below] at (2,-1) {0}; \node [below] at (5.2,-1) {1}; \node [below] at (6,-1) {1}; \node [below] at (8.5,-1) {1}; \draw [thick, ->] (1,-0.5) -- (1,-1); \draw [thick, ->] (2,-0.5) -- (2,-1); \draw [thick, ->] (5.2,-0.5) -- (5.2,-1); \draw [thick, ->] (6,-0.5) -- (6,-1); \draw [thick, ->] (8.5,-0.5) -- (8.5,-1); \end{tikzpicture} \end{center}

. . .

If a_j uniformly distributed and N \rightarrow + \infty:

• \zeta_j (u) Bernoulli PDF with P(\zeta_j (u) = 1) = \frac{1}{2}

Trapani test

• Define the function \vartheta (u) as:

  \vartheta (u) = \frac{2}{\sqrt{r}}
                  \left[ \sum_{j} \zeta_j (u) - \frac{r}{2} \right]

. . .

If a_j uniformly distributed and N \rightarrow + \infty, for the CLT:

  \sum_j \zeta_j (u) \hence
    G \left( \frac{r}{2}, \frac{r}{4} \right)
  \thus \vartheta (u) \hence
    G \left( 0, 1 \right)

. . .

• Test statistic:

  \Theta = \int_{\underbar{u}}^{\bar{u}} du \, \vartheta^2 (u) \psi(u)

Trapani test

According to L. Trapani (10.1016/j.jeconom.2015.08.006):

• r = o(N) \hence r = N^{0.75}
• \underbar{u} = 1 \quad \wedge \quad \bar{u} = 1
• \psi(u) = \chi_{[\underbar{u}, \bar{u}]}

. . .

\mu_k must be scale invariant for k > 1:

  \tilde{\mu_k} = \frac{\mu_k}{ \left( \mu_{\phi} \right)^{k/\phi} }
  \with \phi \in (0, k)

Trapani test

If \mu_k \ne + \infty \hence \left\{ a_j \right\} are not uniformly distributed \vspace{20pt} Rewriting:

  \vartheta (u) = \frac{2}{\sqrt{r}}
                  \left[ \sum_{j} \zeta_j (u) - \frac{r}{2} \right]
                = \frac{2}{\sqrt{r}}
                  \sum_{j} \left[ \zeta_j (u) - \frac{1}{2} \right]

\vspace{20pt}

Residues become very large \hence $p$-values decreases.

Samples results

Samples results

. . .

Landau sample:

:::: {.columns} ::: {.column width=33%}

$$
\mu_1
\begin{cases}
  \Theta = 0.255 \\
  p = 0.614
\end{cases}

:::

::: {.column width=33% .c}

$$
\mu_2
\begin{cases}
  \Theta = 0.432 \\
  p = 0.511
\end{cases}

:::

::: {.column width=33% .c}

$$
\hence \text{Infinite!}

::: ::::

. . .

\vspace{20pt}

Moyal sample:

:::: {.columns} ::: {.column width=33%}

$$
\mu_1
\begin{cases}
  \Theta^2 = 106 \\
  p = 0.000
\end{cases}

:::

::: {.column width=33%}

$$
\mu_2
\begin{cases}
  \Theta^2 = 162 \\
  p = 0.000
\end{cases}

:::

::: {.column width=33% .c}

$$
\hence \text{Finite!}

::: ::::