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The primary objective of this paper is to measure persistence in overall inflation measured by WPI and CPI-IW and their components for the Indian economy over the period 1996:4 to 2017:02. The study first tests for persistence and then quantifies it using various univariate and multivariate approach. The univariate approach estimates persistence using sum of autoregressive coefficient, largest root of the autoregressive process, spectral measures such as Campbell and Mankiw measure of persistence and spectral density function at frequency zero. Estimates of persistence are also derived using variance ratio tests and rolling regressions technique. The study also uses long memory approach (ARFIMA) to measure persistence. In a multivariate approach, the study examines structural form of persistence by employing a time varying vector autoregressive model (TVP-VAR) with stochastic volatility, incorporating inflation, interest rate, demand and supply side variables as its constituents. Results of the study suggest that persistence is high in the economy at both aggregate and disaggregate level. Wholesale price inflation shows intermediate memory, however Consumer prices exhibit long-memory in a univariate analysis. However when structural persistence is studied then WPI inflation is affected by exchange rate shocks and supply shocks such as fuel but it is found to be invariant with respect to interest rate shocks. On the other hand, CPI-IW inflation exhibits time variation with respect to food shocks and exchange rate shocks but is found to be mostly invariant with respect to interest rate shocks.

This paper brings out the salient features of different measures of inflation in India. Before adopting the flexible inflation targeting framework, RBI focused on using Wholesale Price Index (WPI) as the measure of headline inflation. Consumer Price Index for Industrial Workers (CPI-IW) was the widely used measure of CPI inflation. International practice however, suggests the use of CPI as the measure of headline inflation. There are also limitations of using WPI as a measure of inflation. Hence, Central Statistics Office (CSO), launched a new measure of CPI, known as CPI-Combined which incorporates all Indian rural and urban households. Stylized facts used in the paper show that inflation dynamics in India is characterised by a divergence between CPI-IW and WPI indices. Against this backdrop, the paper examines Granger Causality between the two indices in a Vector Auto Regressive framework. Results of the study indicate that there is bidirectional causality between the two indices which does not get affected even during the period of global financial crisis, when the divergence between the two indices was observed for the longest duration.

Effects of external static magnetic field (applied in$\hat y$-direction) on resonant excitation of surface plasma waves (SPW) have been investigated over the metal free space interface. The high power laser$({\rm \omega} _0,\;\vec k_{0z})$is incident over the metal surface and exerts a ponderomotive force on the metal electrons in the skin layer. The ponderomotive force disturbs the quasi-neutrality of plasma which results into the excitation of space charge field at the frequency 2ω0. The electron density perturbation at frequency 2ω0driven by self-consistent space charge potential couples with the oscillatory velocity due to the seed SPW$({\rm \omega}, \;\vec k_z)$and produces nonlinear current to drive another counter propagating SPW$({\rm \omega} _1,\;\vec k_{1z})$at the phase matching conditions of frequency ω = ω1− 2ω0and wavenumber$\vec k_z = \vec k_{1z} - 2\vec k{}_{0z}$(by feedback mechanism). The parametric process becomes resonant at 2ω0≈ ωpand the maximum growth rate is achieved for an incidence angle of laser θ = 40°. The growth rate of the process reduces to half on increasing the magnetic field from 0.49 to 2.45 MG. The present study may be significant to the laser absorption experiments where surface rippling can strongly affect the laser energy absorption.

The acceleration of an electron beam by surface plasma waves (SPW), in the presence of external magnetic field parallel to surface and perpendicular to direction of propagation of SPW has been studied. This wave propagating along the$\hat z$-axis is excited using Kretschmann geometry, having maximum amplitude at the metal–vacuum interface. Equations of motion have been solved for electron energy and trajectory. The electron gains and retains energy in the form of cyclotron oscillations due to the combined effect of the static magnetic field and SPW field. The energy gained by the beam increases with the strength of magnetic field and laser intensity. In the present scheme, electron beams can achieve ~15 KeV energy for the SPW amplitudeA1= 1.6 × 1011V/m, plasma frequency ωp= 1.3 × 1016rad/s and cyclotron frequency ωc/ωp= 0.003.

An obliquely incident high-power laser (ω0,k0z) on the metallic surface can resonantly excite a surface plasma wave (SPW) (ω1,k1z) and a quasi-electrostatic plasma wave (ω,kz) inside the skin layer at the phase-matching conditions of frequency ω1= ω − ω0and wave numberk1z=kz−k0z. The oscillating electrons in the skin layer couples with the seed SPW and exert non-linear ponderomotive force on electrons at the frequency of quasi-static mode. Density perturbations due to quasi-static mode and ponderomotive force associate with the motion of electrons (due to incident laser) and give rise to a non-linear current by feedback mechanism. At ω/kz~vF(wherevFis the Fermi velocity of metal) this non-linear current is responsible for the growth of SPW. The maximum growth of the present process (≅1.5 × 1012s−1) is achieved at incident angle θ = 42° for laser frequency ω0= 2 × 1015rad/s. Growth of SPW enhances from 1.62 × 1011to ≅1.5 × 1012s−1as the magnetic field changes from 12 to 24 MG. The excited SPW can be utilized for surface heating and diagnostics purpose.

A high-frequency surface plasma wave (SPW) excited over metallic surface irradiated by a laser beam, can undergo stimulated Compton scattering if phase velocity of daughter plasma wave is equal to the Fermi velocity for metal. The pump SPW${\rm (}{{\rm \omega} _0},{\vec k_{0{\rm z}}})$parametrically excites a quasi-electrostatic plasma wave${\rm (\omega}, {\vec k_{\rm z}})$and a backscattered sideband SPW${\rm (}{{\rm \omega} _1},{\vec k_{1{\rm z}}})$at resonance ω0= ω − ω1and${\vec k_{0{\rm z}}} = {\vec k_{\rm z}} - {\vec k_{1{\rm z}}}$. The growth rate of Compton process increases with the frequency of incident laser and turns out to be 5.425 × 1010rad/s at laser frequency ω0= 0.7595 × 1015rad/s for incident laser amplitudeA0L= 11 × 1011V/m, laser spot size b = 1.38 × 10−5m, and free electron density of metaln0= 5.85 × 1028/m3. The excitation of highly damped quasi-electrostatic plasma wave in this parametric process provide a better nonlinear option for surface heating as compared with direct laser heating. The process can also be used for diagnostics purposes.

Recommender Systems (RS) help users to find items and make choices which may suit their taste and needs. A user's past behavior, taste and general buying trends may effectively be used by a RS to suggest items to the user each time he/she enters an e-commerce website. However if the user is new and has no awareness of the available choices (cold start user), it becomes somewhat difficult for the system to offer recommendations. This limitation, known as the cold start problem, has been one of the mostly explored challenges in the Recommender Systems research. In the current work, an attempt is made to handle cold start problem by generating recommendations based on the social interactions between the users on Facebook, a popular social networking website. The choices made by friends or acquaintances, tend to have an impact on the user's opinions and choices. We incorporate this concept to make recommendations to the user. We propose “Interaction Based Social Proximity (IBSP)”, a social interaction factor to overcome the Cold Start problem. A prototype of the system has been developed for the books domain using java. The Facebook graph API is used to extract information from the social graph of the user.